Data Labeling Companies: A Buyer's Guide to 26 Vendors (2026)

Last updated August 2026. Vendor facts, certifications, pricing and review figures were checked on 6 August 2026. This category changes fast, and we re-check every figure quarterly.

Scale AI’s largest customer moved to end the relationship within days of Meta taking a 49% stake. Appen’s group operating revenue fell 14.2% the year after Google terminated its contract. Mercor went from a $2 billion valuation to $10 billion in eight months. Three data points, one market that reorganised itself inside eighteen months, and almost none of the guides ranking for data labeling companies reflect any of it.

This is written for the person choosing and defending an annotation vendor: the ML lead, the head of data, the AI product owner who has to put three names in front of a procurement committee and explain the shortlist. It is not a guide to annotation work. If you are looking for a job labeling data, the rest of this page will not help you.

A disclosure, because this SERP needs one. At least 12 of roughly 15 guides ranking for this search were written by an annotation company that placed itself in its own ranking, usually at #1, and only one of them, Kili Technology, tells you. This guide is published by Forage AI, and Forage AI appears in it at entry #5. Labeling and annotation sit within our capabilities as part of managed engagements, but we do not sell standalone data labeling and are not taking clients for it. The entry says so in the same tables every other vendor gets. Disclosure is the editorial standard here, and we apply it to ourselves first and then to all 26 entries.

The numbering below is not a ranking. Nobody paid for placement, and no entry was written from a vendor’s marketing copy alone.

What you walk out with: 26 vendors with sourced core facts and public review figures printed with their sample sizes, the three questions that shorten a shortlist faster than any feature comparison, per-modality cost benchmarks, and a pilot protocol plus an SLA clause skeleton you can lift straight into a procurement pack.

What data labeling companies do, and where they stop

A data labeling company adds structure to data you already have. Labels, bounding boxes, transcriptions, entity tags, preference rankings, rubric scores. The industry uses data labeling and data annotation interchangeably and so does this article. The purchase includes more than the labels themselves: guideline authorship, annotator training, the tooling, a QA layer, delivery format and export.

Grand View Research values the data labeling solution and services market at USD 18.63 billion in 2024, projecting USD 57.63 billion by 2030 at a 20.3% CAGR, with the outsourced segment accounting for 84.6% of 2024 revenue and North America holding 33.9%.

Name the market when you quote that number, because Grand View publishes three overlapping market definitions at different sizes, and the annotation tools market is roughly $2.1 billion in 2026 against $18.63 billion for solutions and services. Those are different scopes, not competing estimates.

Labeling versus the wider AI training data market

Labeling is one of four categories in the AI training data market, alongside data collection, dataset licensing and synthetic generation. Which one you need turns on whether you have the data, whether it is labelled, and whether somebody has already built the dataset you are assembling. If your corpus exists and has no labels, this market is the answer. If the corpus does not exist, it is not. A data labeling company and an AI training data provider are not the same purchase.

One more boundary worth stating plainly, because roundups blur it constantly: several companies routinely listed as labeling vendors are not selling labeling. Hive AI is an API and model company. Snorkel AI sells programmatic supervision, which is a different way of not hiring labelers.

What gets annotated: modality coverage

Modality is the fastest first filter on a long vendor list. Computer vision covers bounding box, polygon, semantic segmentation and instance segmentation, keypoint and landmark work, 3D cuboids and LiDAR point clouds, and video object tracking. Text and NLP covers NER, sentiment and intent classification, and increasingly RLHF, SFT, preference and rubric data and RL environments. Audio covers transcription and diarization. Documents cover OCR and key-value extraction. Multimodal sits across all of it.

Two method entities show up in almost every modern engagement and change the economics: model-assisted pre-labeling, where a model proposes and humans correct, and active learning, where the system chooses what to send to a human next. Neither removes the human. Both move where the human’s time goes.

How we evaluated these companies (and what we sell)

The harder question is not what these companies do but whose account of them you should trust.

Start with the conflict, because every other guide on this page has one and does not name it.

Forage AI published this article, and Forage AI is entry #5 in it. Our core business is managed data extraction and data acquisition: getting web and document data, structuring it, running quality assurance on it, and delivering it. Labeling and annotation sit within our capabilities and are delivered inside managed engagements, typically as human-in-the-loop validation and expert feedback on our own extraction models. We do not sell standalone data labeling as a productized service and we are not currently taking clients for it. If what you need is annotation of a corpus you already hold, one of the other 25 entries fits your problem better than we do. Our entry gets the same two tables, the same evidentiary standard, and the same watch-out row as everybody else, and its review table states our actual review posture rather than a number we invented.

That is the standard applied to all 26 entries: where a vendor publishes nothing, this guide prints the absence rather than leaving the cell blank.

Nine criteria, published up front. Delivery model. Workforce model. Whether workforce geography is disclosed at all. Ownership and independence. Modality and domain fit. Whether a quality method is published. Security and compliance, read as framework plus Type plus scope. Pricing posture. Public review evidence, printed with its sample size.

Entries were selected from SERP presence across 14 competing roundups, plus the expert-data tier those roundups largely omit, plus our own sweep of 28 vendor websites on 6 August 2026. That sweep is wider than this roster on purpose: 28 vendors were audited field by field, 26 earned a full profile below, and eleven further vendors were assessed and named rather than profiled, either because they are a different purchase or because the evidence did not support a full entry: Snorkel AI, Invisible Technologies, Hive AI, Shaip, Centific, Innodata, Keymakr, Prolific, Defined.ai, TransPerfect DataForce and Mindy Support.

A certification claim on a marketing page is a marketing claim

Across 28 vendor security pages read on the same day, breadth of certification claims ran inverse to strength of evidence.

Vendor	Certifications claimed	Verifying artefact published?	Scope stated?	What a buyer can actually check
Cogito Tech	Six, including “HIPAA Certified” and ISO 27001:2013	No artefact for any of them	No	Nothing. HIPAA has no certifying body, and the ISO claim cites a superseded revision
Encord	Three (SOC 2, HIPAA, GDPR)	Yes, live trust centre; HIPAA and GDPR via Vanta automation	Type not stated on the security page	The SOC 2 examination is evidenced; the Type is not, so ask
Micro1	None published	N/A	N/A	Nothing, and they market a Government partnerships track
Surge AI	None published; the security URL 404s	N/A	N/A	Nothing first-party. Aggregator claims of SOC 2, ISO 27001 and FedRAMP are not Surge’s own
Handshake AI	SOC 2 Type II, PCI DSS v4.0.1, GDPR, TX-RAMP and more, on a real trust portal	Yes	Yes, and that is the problem	The scope covers the recruiting products, not the AI data unit

The fairness cuts both ways. Encord, V7, SuperAnnotate, CloudFactory and Kili Technology all publish real security pages or live trust centres, and SuperAnnotate makes its SOC 2 Type II and ISO 27001:2022 reports available on request.

The RFP-ready form of the question has three parts, not one. Which framework? Which Type? What systems are in scope, and can I have the report? SOC 2 Type I describes controls at a point in time; SOC 2 Type II tests them over a period; SOC 1 Type 2, which Innodata publishes, is a financial-controls report and is not SOC 2 at all. Five vendors publish “SOC 2” with no Type stated: TELUS Digital, CloudFactory, Encord, Label Studio and Roboflow. Only three of 28 vendors claim ISO 27701 and only four claim PCI DSS, so a procurement checklist that treats either as standard will disqualify most of the market before you have evaluated anything.

A name collision that will corrupt your evaluation. Every G2, Capterra, TrustRadius and Gartner Peer Insights figure published against “Handshake” belongs to the college-recruiting product, not to Handshake AI’s data business. Gartner’s vendor page lists Handshake as having exactly one product in one market. There is no review-platform coverage of the AI data unit anywhere, and this guide prints none of the circulating figures.

The review evidence in this category is thinner than it looks

Customer-side public review volume across the biggest names is close to zero. Scale AI shows n=1 on G2. Surge AI shows n=0. TELUS Digital shows n=0. Innodata shows n=0. G2 is the only platform with meaningful coverage at all, and Gartner Peer Insights and TrustRadius were not systematically swept in this pass, so those rows say “not checked” rather than “no reviews.” Claiming an absence you never verified is its own form of fabrication.

One methodological disclosure, since this article holds vendors to a disclosure standard. G2, Gartner Peer Insights, TrustRadius and Glassdoor all block automated reads. Except where a row says otherwise, the G2 and Glassdoor figures below were confirmed from the platforms’ own indexed pages rather than from a rendered live page, and Trustpilot figures were re-read on the live page. Where a figure could not be resolved either way, the row says so instead of printing a number.

Trustpilot in this category is a worker channel, not a customer channel. Appen carries 4.1 to 4.2 from buyers on B2B review sites and 1.4 from workers on Trustpilot (n=366). Same brand, two populations, opposite verdicts. Every rating in this article is labelled with which side of the marketplace is talking.

One evaluation note on provenance. Labeling is downstream of acquisition, and where the corpus came from is a separate diligence question from who labels it. The distinction between where the underlying training data came from and who annotated it will come up in legal review whether or not you raise it first.

Analyst context: Everest Group’s Data Annotation and Labeling PEAK Matrix assessment, published in 2024, evaluated 19 providers. Individually confirmed placements from that 2024 assessment include Appen, TaskUs and TELUS International as Leaders and iMerit as a Major Contributor.

The 26 data labeling companies at a glance

#	Company	Category	Delivery model	Best for
1	Scale AI	Enterprise / frontier-lab	Hybrid	Large frontier-scale programmes, ownership conflict permitting
2	Appen	Enterprise / frontier-lab	Hybrid	Multilingual breadth with a checkable public owner
3	iMerit	Enterprise / frontier-lab	Managed + platform	Regulated work needing employed annotators
4	Sama	Enterprise / frontier-lab	Managed + platform	Impact-sourced delivery with published office addresses
5	Forage AI	Publisher of this guide	Managed data extraction	Teams that do not have the corpus yet
6	Surge AI	Expert-data marketplace	Platform + managed expert	Credential-gated RLHF and evaluation work
7	Mercor	Expert-data marketplace	Marketplace	Hourly domain-expert capacity at speed
8	Turing	Expert-data marketplace	Managed + platform	Coding, reasoning and STEM training data
9	Handshake AI	Expert-data marketplace	Fellowship network	US-only academic expert data
10	Micro1	Expert-data marketplace	Platform + expert network	RL environments and agent evaluation
11	TELUS Digital	BPO and crowd-scale	Hybrid at scale	500+ language coverage at volume
12	TaskUs	BPO and crowd-scale	Managed BPO	Certification-heavy, audit-heavy programmes
13	LXT (incl. clickworker)	BPO and crowd-scale	Managed + open crowd	Named secure facilities and on-site deployment
14	Toloka	BPO and crowd-scale	Platform + marketplace	Crowd breadth moving toward vetted experts
15	CloudFactory	Managed mid-market	Managed + platform	A dedicated employed team on a mid-market budget
16	Cogito Tech	Managed mid-market	Managed service	Named India delivery centres, if you verify the certs yourself
17	Label Your Data	Managed mid-market	Hybrid	Published per-object rates and no minimums
18	Labelbox	Annotation platform	Platform + expert crowd	Platform-first teams wanting an expert network attached
19	SuperAnnotate	Annotation platform	Platform + expert network	Teams that want the deepest review evidence in the category
20	Encord	Annotation platform	Platform-first	Physical AI, robotics and AV data workflows
21	V7 (V7 Darwin)	Annotation platform	Platform-first	Medical and document workflows with a full security page
22	Kili Technology	Annotation platform	Platform-first hybrid	European enterprises wanting on-prem or air-gapped
23	Label Studio (HumanSignal)	Open source / self-serve	OSS + SaaS	Apache 2.0 self-hosting with a paid cloud tier
24	CVAT.ai	Open source / self-serve	OSS + managed cloud	Fully published pricing and MIT-licensed core
25	Roboflow	Open source / self-serve	Platform	Computer vision teams wanting self-serve labeling
26	AWS SageMaker Ground Truth	Cloud-native	Cloud-native	Bringing your own workforce onto rented tooling. Existing AWS customers only since 30 July 2026

Are you buying a platform, an outcome, or a workforce?

Those nine criteria assume you already know which purchase you are making, and that is the assumption this market breaks first.

Most roundups bucket vendors into “service-first,” “hybrid” and “platform-focused” and stop there. The consequence is never stated, and the consequence is the entire decision. These are not three flavours of the same purchase. They are three different risk transfers, and the market sizes them separately.

	Platform	Managed service	Your workforce on their tooling	Licence a finished dataset
What you buy	Software, workflow and QA measurement tooling	An outcome: labelled data to a spec	Software plus workflow, staffed by your own people	Data that is already labelled
What risk you retain	Labour, throughput, annotator quality, ramp	Almost none operationally; you retain spec risk	Labour and quality; you shed tooling build	Fit risk: it was built for someone else’s taxonomy
What you give up	Speed to first batch when you have no annotators	Process visibility, and often the ability to audit who did what	Nothing structural, but you must already employ annotators	Customisation, and any claim to a proprietary asset
Pricing shape	Seat or usage fees, largely fixed	Per unit or per dedicated FTE, scales linearly	Seat or usage, plus your own payroll	One-time or subscription licence
When it is the right call	Stable taxonomy, existing annotator capacity, long-running programme	You need volume fast and have no annotation function	Domain experts already on payroll, confidentiality is tight	Somebody has already built what you need

Three of those columns are purchases inside this category; the fourth is an exit from it. The third path is the one nobody names. AWS SageMaker Ground Truth’s private-workforce option is the clearest illustration: you rent the labeling workflow and staff it with your own employees, which keeps the data and the people inside your perimeter. It is an illustration rather than a recommendation as of August 2026, because AWS closed Ground Truth to new customers on 30 July 2026; the shape of the purchase is what matters, and several platform vendors sell it. The fourth path leaves the category entirely, and buying a finished dataset outright is frequently cheaper than labeling a corpus somebody has already labelled.

Platform and managed service are a spectrum, not a binary. Scale AI, Appen, Encord and SuperAnnotate all sell both. So the useful question is not which box the vendor ticks. It is which risk you retain after signing.

Five routing conditions decide it, in the order they bite. Label volume, because below roughly 50,000 units a managed vendor’s minimums dominate the economics. Taxonomy stability, because a fixed per-unit contract on a moving ontology prices the wrong thing. Existing capacity, because if you already employ the domain experts, buying a workforce is buying something you have. Confidentiality posture, which pushes the answer toward employed teams or your own people on rented tooling. And programme duration: a platform’s seat cost is roughly fixed while a managed service’s per-unit cost scales linearly, so the curves cross somewhere, and where they cross depends on your volume trajectory rather than on anybody’s feature matrix.

Pricing transparency follows the purchase type almost perfectly. The platform and cloud-native tier is where all six published rate cards live. The managed and expert tiers are quote-only with essentially no exceptions.

Workforce models and data labeling outsourcing

Two of those three purchases come with a workforce attached, which makes the workforce model the next filter rather than a footnote.

Outsourcing is the default position in this market, not the risky one. Grand View Research put 84.6% of the 2024 data labeling solution and services market in the outsourced segment, which leaves in-house at roughly 15%. The question is not whether to outsource. It is which of five workforce models you are outsourcing to.

The five workforce models, and what each one costs you

Workforce model	Unit price	Quality variance	Ramp time	Edge-case handling	Confidentiality control	ESG exposure
Employed in-house teams (vendor’s own staff)	Highest of the managed options	Lowest	Slowest to scale up	Strong, because the cohort persists	Strongest; named individuals, fixed facilities	Lowest, and usually documented
BPO delivery centres	Mid to high	Low to moderate	Moderate	Strong on repeatable tasks	Strong; geo-fencing is available	Moderate; site-level disclosure is common
Managed remote teams	Mid	Moderate	Moderate	Moderate	Moderate; depends on device and access policy	Moderate, and rarely audited
Open crowd marketplaces	Lowest	Highest	Fastest	Weakest; consistency degrades on ambiguity	Weakest; population is fluid and often undisclosed	Highest, and the hardest to evidence
Credential-gated expert networks	Highest overall	Low on domain judgement, variable on process	Fast for small volumes	Strongest on expert judgement	Moderate; identity is verified, geography often is not	Moderate; pay is high, tenure is short

Two mechanisms in this market are worth naming because they are mechanisms rather than adjectives. CloudFactory organises workers into five-person teams, called toli, with weekly face-to-face accuracy reviews. iMerit publishes a flat commitment that its people work full-time for iMerit at its secure facilities and are not remote or outsourced workers. Both are mechanisms you can ask another vendor to match, which is more than can be said for “rigorous multi-stage QA.”

In-house or outsourced: the worked comparison

Take 20,000 images needing bounding boxes. In-house, that is five annotators plus a QA reviewer, a tool to license or build, guideline authorship, and two to four weeks of ramp before throughput is predictable. Outsourced at a published rate of $0.02 per object, the same volume at two objects per image lands near $800 of direct labeling cost plus setup, plus whatever rework the first pass generates. The direct comparison flatters outsourcing every time. It is the same build-versus-buy calculus that shows up in every data infrastructure decision, and it resolves the same way: in-house wins on control and on retained institutional knowledge, and it loses on cost and speed until the volume is small enough that vendor minimums dominate.

Where the data legally cannot leave your jurisdiction, workforce geography stops being a preference and becomes a first-round disqualifier, and the handover mechanics then matter more than the rate card. If you are handling personal data, the service-provider versus contractor obligations under CCPA determine what your contract has to say, and they are not the same obligations.

This section describes general procurement practice and is not legal advice. Consult qualified counsel for your organisation’s specific compliance requirements.

Geography, delivery-centre transparency, and labor conditions

AFP’s October 2025 wire investigation documented an organised counterparty forming inside the delivery geographies buyers routinely contract into. The Data Labelers Association in Kenya launched in early 2025 with 339 members in its first week, as Computer Weekly reported at the time, and AFP put it at 800 members by October 2025, with demands covering employment contracts, freedom of association, break rights and psychological support. The same AFP reporting recorded almost 250 GlobalLogic data workers let go in September 2025.

That is a procurement input rather than a moral one. For historical baseline, TIME’s 2023 investigation reported Kenyan workers labelling toxic content taking home roughly $1.32 to $2.00 per hour while the client paid the vendor about $12.50 per hour for the same work. That is 2023 data and should not be presented as the current picture. The vendor named in that reporting, Sama, published a response through a TechTarget podcast, holds B Corp certification and operates an impact-sourcing model with published office addresses, and any diligence pack citing the 2023 reporting should carry all of that alongside it.

Antonio Casilli, Professor of Sociology at Institut Polytechnique de Paris, interviewed click workers in 30 countries for Waiting for Robots (University of Chicago Press, 2025), which marks this as a documented field of research rather than a set of anecdotes.

For a buyer the translation is narrow and checkable. Ask where the labour sits, whether annotators are employed or contracted, and whether the vendor will name the facilities. Vendors that can answer all three tend to be the ones already answering them in public.

What changed in the annotation market in 2025-26

For several vendors those three answers changed inside the last eighteen months, which is where most competing guides stop being reliable.

Nearly every guide ranking for this term carries a 2026 date stamp on a roster and a framing that predate June 2025. Three structural shifts happened in that window, and almost nothing ranking reflects them.

Ownership conflicts, and why neutrality became a buying criterion

Meta invested $14.3 billion for a 49% non-voting stake in Scale AI, in a deal that closed on 13 June 2025 and valued Scale at $29 billion. Founder and CEO Alexandr Wang left to lead a new superintelligence research lab at Meta, and chief strategy officer Jason Droege was promoted to CEO.

What happened next is the part that turned ownership into a procurement criterion. Within days, Reuters reported that Google, Scale’s largest customer and planning roughly $200 million of 2025 spend, was preparing to cut ties, and that Microsoft and xAI were looking to pull back. None of the three was reported as a completed termination. On 18 June 2025, OpenAI confirmed it was phasing out its work with Scale, while saying the wind-down had begun roughly a year earlier and was not caused by the Meta deal. That caveat matters and most coverage drops it. Reporting put Google’s 2024 spend with Scale at roughly $150 million, about 17% of Scale’s revenue, though that figure is attributed to sources rather than to any Scale disclosure. On 16 July 2025, about a month after the investment, Scale cut 14% of its workforce, roughly 200 employees, and separately ended contracts with 500 contractors, with the cuts falling largely on the core data-labeling business.

Ownership change, client flight, capacity cut. A complete, public, dated causal chain, all inside five weeks. That makes vendor viability a live procurement risk rather than a theoretical one, and it is why this guide carries an ownership and independence row in all 26 core-facts tables. Not one competing roundup lists who owns your vendor as an evaluation dimension.

The commodity tier contracts, the expert-data tier arrives

Appen is the quantified case for client concentration. Google terminated its contract in an announcement on 23 January 2024, removing US$82.8 million of annual revenue out of FY23 revenue of US$273.0 million. Appen’s FY2024 group operating revenue fell 14.2% to US$234.3 million, with the Global Services division down 38.3% to US$118.1 million. Appen reports in US dollars, so the Australian-dollar versions of these figures circulating elsewhere on this SERP are in the wrong currency.

The other half of that story is mandatory, and most coverage stops before reaching it. Appen’s FY2025 results, released on 25 February 2026, showed revenue of US$230.8 million, up 4.5% on an adjusted basis that excludes the FY24 Google impact and down 1.5% on the statutory group figure, alongside gross margin up 100 basis points to 40.3%, underlying EBITDA before FX rising from US$3.5 million to US$12.2 million, and China revenue up 74.8%. FY2026 guidance is US$270 to $300 million at an underlying EBITDA margin of about 5 to 10%. The company that took the largest single hit in this market is guiding to growth two years later, and a vendor guide that prints only the decline is telling you half of a fact.

While the commodity tier contracted, an expert-data tier arrived and repriced the top of the market. Mercor raised a $350 million Series C at a $10 billion valuation on 27 October 2025, five times its $2 billion Series B eight months earlier, and reports more than 30,000 experts with about $1.5 million per day disbursed to contractors at $85 to $95 per hour (TechCrunch and CNBC, 27 October 2025). Turing raised a $111 million Series E in March 2025 at a $2.2 billion valuation, with ARR reported around $300 million up from $167 million. Private-markets research firm Sacra estimates Handshake AI reached roughly $1.1 billion in annualised gross revenue by April 2026, up about 349% year over year from around $245 million, running on the parent career network’s 18-million-strong student and alumni graph. Surge AI ran bootstrapped with no venture capital from its 2021 launch until July 2025, when it began its first external raise at a valuation Bloomberg reported at at least $25 billion.

In the weeks after the Meta transaction, Sapien AI said 40,000 new annotators joined within 48 hours, against reporting that put Scale’s own gig workforce at roughly 240,000 globally.

Seven things the guides you are reading still get wrong

None of this is a gotcha. It is what changed while the guides you are reading were being republished with a fresh date at the top.

The claim still circulating	The correction (with date)	Why a buyer cares
AWS SageMaker Ground Truth Plus is the managed labeling option inside the cloud you already buy	Ground Truth Plus reached end of support on 30 June 2026 and is no longer available, per AWS’s own service availability notice. Separately, self-service SageMaker Ground Truth moved to maintenance and closed to new customers on 30 July 2026; existing customers keep access	Recommending a product you can no longer buy as described is the most disqualifying error a vendor guide can make. Note the two are different products with different dates
TaskUs is going private	The $16.50 per share, $1.62 billion Blackstone take-private announced 9 May 2025 was voted down by stockholders on 8 October 2025. TaskUs remains public, Nasdaq: TASK	The May announcement was covered far more heavily than the October failure. A listed company’s ownership is checkable, which is the whole point of the ownership column
clickworker is an independent German crowd platform	Acquired by LXT: definitive agreement announced 17 December 2024, technology integration completed 31 July 2025	Roundups listing clickworker and LXT as two separate options are listing one company twice, which materially changes a shortlist
Shaip is independent, headquartered in Louisville, Kentucky	Acquired by Ubiquity Global Services, announced 12 February 2026. Louisville was correct before the deal; Shaip’s listed address is now Ubiquity’s New York headquarters	Ownership and data-residency diligence both change
TELUS International (TIXT) is NYSE-listed	Taken private by TELUS Corporation, closing 31 October 2025 at US$4.50 per share, roughly US$539 million for the shares TELUS did not already own; the shares came off the NYSE and TSX in the following days, with NYSE removal effective 11 November 2025	Changes both the ownership answer and how you verify financial stability
Sama holds SOC 2 among its completed certifications	Sama’s own page lists SOC 2 as certification in progress, not achieved (ISO 42001 also in progress). Achieved: ISO 27001, ISO 9001, TISAX, GDPR, CCPA	A direct hit on the pattern where certifications are asserted and never verified
Centific is a new entrant	Centific is the renamed Pactera EDGE, spun off from Pactera Technology International in 2020 and rebranded in January 2023	Vendor history and viability diligence

One more, because it corrupts the pricing section of almost every competing article: Labelbox has removed its public pricing page. Both the pricing URL and the calculator return 404 as of 6 August 2026. The per-LBU and per-hour figures circulating in essentially every roundup now cite a dead page, and this guide does not reprint them.

Why is Meta writing its own training data?

Because the counter-signal to the expert-data tier came from the market’s largest buyer, and it appears in no competing roundup. Meta formed an Applied AI Engineering unit in March 2026 and moved roughly 6,500 engineers and product managers into it to write coding puzzles and challenge problems for model training. That is expert training-data work performed in-house by salaried engineers. Business Insider reported it first in May 2026; Reuters, working from internal memos, reported on 2 June that the transfers were not optional, and TechCrunch carried the 6,500 figure on 12 June. Meta has not confirmed the headcount, so treat it as consistently reported rather than disclosed. On 13 June 2026, Mark Zuckerberg conceded in an internal memo that Meta had “made mistakes” in the restructuring, and the assignment was subsequently softened to defer to each individual’s choice.

The buyer of the largest labeling stake in the market decided the highest-value annotation could not be bought. The labour market underneath it has stratified into bands that barely overlap, and the cost section below carries those figures.

Take both as vendor positioning, not as evidence. The evidence is the reported sequence: a 49% stake closing 13 June 2025, three named customers reported to be pulling back within days plus a fourth confirming a wind-down it attributes to earlier decisions, and a 14% workforce reduction on 16 July 2025. Kolln’s underlying point survives the discount, though. A public company’s ownership is a disclosed and checkable fact, and most of this market’s ownership is neither.

The 26 data labeling companies, by category

Those are the market conditions the following 26 entries were assessed under.

The numbering is not a ranking. Categories are ordered by buyer-decision logic, and within each category entries are ordered by market prominence rather than preference. Nobody paid for placement.

Every entry carries the same 14-row core-facts table and the same 8-row review table, so the roster reads as one comparison rather than a pile of unrelated specs. Filter on delivery model first and workforce geography second, because geography is a hard disqualifier the moment you carry a data-residency obligation. Modality and domain fit come third; review scores come last.

Absence of published pricing, geography or certifications is printed as a finding rather than left blank. A rating without its sample size is not a claim.

A. Enterprise and frontier-lab providers

The four vendors that take the largest programmes, and the tier where the 2025 disruption landed first.

1. Scale AI

Attribute	Detail
Delivery model	Hybrid: platform plus managed service
Workforce model	Open gig crowd, recruited through wholly-owned Remotasks and Outlier
Workforce geography	Not disclosed on scale.com. Third-party reporting (Washington Post, 2023) places the crowd in Kenya, the Philippines and Venezuela, at roughly 240,000 workers
Ownership / independence	Meta holds a 49% non-voting economic stake, ~$14.3B, closed 13 June 2025, valuing Scale at ~$29B. Founder Alexandr Wang left to lead Meta Superintelligence Labs and remains on Scale’s board. Jason Droege is CEO
Best for	Frontier-scale programmes where volume and tooling maturity outweigh the ownership question
Not for	Any buyer who competes with Meta, or whose confidentiality posture cannot absorb a strategic investor with a 49% economic interest
Modalities	Image, video, 3D/LiDAR, text, audio, document, multimodal
Specialist domains	Defence and government, autonomous vehicles, frontier LLM training and evaluation
Core features	Data Engine, Nucleus, Scale GenAI Platform, Remotasks and Outlier contributor networks
Quality method	Multi-pass review and model-assisted pre-labeling; no published IAA methodology
Security & compliance	SOC 2 Type 2, ISO 27001:2022, DoD IL4 Provisional Authorization, FedRAMP High Authorized (scale.com/legal/security)
Pricing posture	Quote-only. Scale’s own documentation publishes no figures
Typical engagement / minimums	Not disclosed
Watch-out	The ownership conflict above, and a June 2025 Business Insider report that at least 85 Google Docs containing confidential client project material were publicly accessible by link; the files were locked down and an investigation opened after notification

Review attribute	Detail
G2	Seller page shows 5.0 from n=1. Report as no public reviews / fewer than 5 reviews on G2 as of 6 August 2026
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	3.7 (n≈424), accessed 6 August 2026. Employee sentiment, not customer sentiment
Evidence quality	The largest and most-discussed vendor in the category has essentially no customer-review footprint. That absence is itself a finding
What reviewers praise	Growth and exposure, on the employee side (Glassdoor)
What reviewers complain about	Work-life balance, on the employee side (Glassdoor)

What it is. A hybrid platform and managed service running on an open gig crowd, historically the default enterprise choice for high-volume computer vision and, more recently, frontier-lab training and evaluation data.

Best for. Programmes at frontier scale with mature tooling requirements. Not for buyers who compete with Meta, or for anyone whose confidentiality review will ask who else has an economic interest in the vendor handling their data.

What customers say. Almost nothing, publicly. The largest vendor in the category has no usable customer-review footprint, and the only sizeable figure available is employee sentiment, which says nothing about delivery quality.

2. Appen

Attribute	Detail
Delivery model	Hybrid: ADAP platform plus managed services
Workforce model	Open vetted crowd, over 1 million contributors across 170+ countries and 235+ languages, plus secure in-house facilities for restricted work
Workforce geography	Disclosed in part. Named secure facilities in the UK, Cavite (Philippines) and Wuxi (China)
Ownership / independence	Public company, ASX: APX, no controlling shareholder. The checkable-ownership example in this roster
Best for	Multilingual and speech breadth at volume, from a vendor whose ownership you can verify on an exchange
Not for	Buyers who need a single dedicated employed cohort, or who cannot tolerate crowd-level variance on ambiguous tasks
Modalities	Text, audio and speech, image, video, document, multimodal
Specialist domains	Search relevance, speech and language, generative AI evaluation
Core features	ADAP platform, crowd management, secure facility delivery, LLM evaluation services
Quality method	Contributor scoring and multi-pass review; no published IAA threshold
Security & compliance	SOC 2 Type II, ISO 27001:2013 (TÜV Rheinland NA), HIPAA, GDPR, ISO 9001 (UK), Cyber Essentials
Pricing posture	Quote-only. appen.com/pricing returns 404
Typical engagement / minimums	Not disclosed
Watch-out	Client concentration. The Google contract was US$82.8M of FY23 revenue of US$273.0M, and its termination, announced 23 January 2024, removed all of it. FY2025 results (25 February 2026) showed US$230.8M of revenue, up 4.5% on an adjusted basis excluding the FY24 Google impact and down 1.5% statutory, underlying EBITDA before FX up from US$3.5M to US$12.2M, and FY2026 guidance of US$270 to $300M, so the concentration risk is documented and the recovery is too

Review attribute	Detail
G2	4.2/5 (n=35), accessed 6 August 2026. Customer-side
Capterra	4.1/5 (n=40), accessed 6 August 2026. Customer-side
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	1.4/5 (n=366), re-read 6 August 2026. Contributor-side, not customer reviews. Earlier reads in this research pass recorded 1.8 to 2.2; the 1.4 is the figure from the live page and is the one printed
Employee sentiment (Glassdoor)	3.5 global (n≈2,200) and 2.9 US (n≈573), accessed 6 August 2026. Employee sentiment
Evidence quality	The clearest illustration in this article of why populations must be labelled: 4.1 to 4.2 from buyers (n=35 and n=40), 1.4 from workers (n=366), same brand
What reviewers praise	Scale of language coverage and project management, on G2 and Capterra
What reviewers complain about	Platform usability and turnaround consistency, on G2 and Capterra

What it is. A 1996-founded Sydney company running a hybrid of the ADAP platform and managed services, on an open vetted crowd, with named secure facilities for restricted work.

Best for. Multilingual and speech programmes at volume where verifiable ownership matters. Not for teams needing one persistent employed cohort, or highly ambiguous taxonomies where crowd variance is expensive.

What customers say. The buyer-side figures are the strongest in the enterprise tier. Trustpilot’s much lower figure is contributor-side and measures the labour relationship, not the delivery. Everest Group named Appen a Leader in its 2024 Data Annotation and Labeling PEAK Matrix assessment.

3. iMerit

Attribute	Detail
Delivery model	Managed service plus the Ango Hub platform layer
Workforce model	Employed in-house. iMerit states its people work full-time for iMerit at its secure facilities and are not remote or outsourced workers
Workforce geography	The fullest geography disclosure in the enterprise tier. Delivery centres in Silicon Valley, New Orleans, Kolkata, Bengaluru and around eight further Indian cities, plus Thimphu, Bhutan (120+ people). Over 5,000 full-time staff, more than 50% women
Ownership / independence	Private, impact-investor backed (~$24.3M total; British International Investment led December 2019; Khosla Impact, Michael & Susan Dell Foundation, Omidyar Network). iMerit’s own site discloses no amounts or dates
Best for	Regulated and high-consequence work where you need to know who the annotators are and that they are employed
Not for	Teams needing a self-serve platform, or surge capacity in dozens of languages overnight
Modalities	Image, video, 3D/LiDAR, text, document, medical imaging
Specialist domains	Medical and clinical, autonomous vehicles, geospatial, agriculture, commerce
Core features	Ango Hub, dedicated managed teams, domain-expert annotation pods
Quality method	Dedicated QA layer with expert review. Their “output accuracy above 98%” claim is vendor self-reported and is not printed here as a benchmark
Security & compliance	SOC 2 Type 2, ISO 27001, HIPAA, GDPR, ISO 9001:2015, TISAX
Pricing posture	Quote-only
Typical engagement / minimums	Not disclosed
Watch-out	Ownership and funding history are not disclosed first-party, so the financial picture has to be assembled from third-party trackers

Review attribute	Detail
G2	4.7/5 (n=13) on the seller page, accessed 6 August 2026. Small sample; treat as directional
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	3.3 (n≈454), accessed 6 August 2026. Employee sentiment
Evidence quality	n=13 is below the level at which a rating discriminates between vendors. Use it as a signal of presence, not of superiority
What reviewers praise	Domain expertise and responsiveness of dedicated teams (G2)
What reviewers complain about	Onboarding time on complex taxonomies (G2)

What it is. A 2012-founded managed service with the strongest workforce-employment guarantee in this roster, plus Ango Hub as a platform layer for teams that want visibility into the pipeline.

Best for. Regulated, high-consequence programmes where annotator identity and continuity are contractual concerns. Not for self-serve buyers or overnight multilingual surge.

What customers say. The G2 sample is too small to discriminate between vendors and should be read as a signal of presence. Everest Group listed iMerit as a Major Contributor in its 2024 assessment.

4. Sama

Attribute	Detail
Delivery model	Managed service through in-house impact and BPO delivery centres, plus the Sama Platform
Workforce model	Employed in-house, impact sourcing, B Corp certified
Workforce geography	Best geography disclosure in the enterprise tier. Offices published in San Francisco, Montréal, San José (Curridabat) Costa Rica, Nairobi, Gulu, Kampala and The Hague
Ownership / independence	Private. Last disclosed round $70M led by CDPQ, 4 November 2021. No round found after 2021 and no valuation disclosed
Best for	Buyers with an ESG or impact-sourcing mandate who also need published delivery addresses
Not for	Anyone whose procurement checklist requires a completed SOC 2 today
Modalities	Image, video, 3D/LiDAR, text, multimodal
Specialist domains	Autonomous vehicles, retail and e-commerce, agriculture, content moderation
Core features	Sama Platform, managed delivery pods, impact-sourcing programme with published impact reporting
Quality method	Dedicated QA layer with published impact and quality reporting cadence
Security & compliance	Achieved: ISO 27001, ISO 9001, TISAX, GDPR, CCPA. SOC 2 is listed on Sama’s own page as certification in progress, not achieved. ISO 42001 also in progress
Pricing posture	Quote-only
Typical engagement / minimums	Not disclosed
Watch-out	The SOC 2 status above. Sama is the only vendor in the enterprise tier without a completed SOC 2, and competing roundups routinely list it as certified

Review attribute	Detail
G2	4.6/5 (n=11), accessed 6 August 2026. Small sample
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	3.5 (n≈472), accessed 6 August 2026. Employee sentiment
Evidence quality	n=11 does not support a comparative claim. The published office addresses are stronger evidence than the rating
What reviewers praise	Delivery consistency and account management (G2)
What reviewers complain about	Pricing flexibility on smaller volumes (G2)

What it is. A 2008-founded managed service delivering through owned impact and BPO centres, one of the first AI companies certified as a B Corp, with more than 13,000 people connected to digital work.

Best for. Programmes with an explicit ESG mandate that also need named delivery locations. Not for procurement processes that require a completed SOC 2 report at signature.

What customers say. The G2 sample is too small to rank on. Sama’s client disclosure is the strongest first-party list in the enterprise tier, naming Getty Images, Continental, Verizon, SWIFT, Siemens, Qualcomm, Sony, Microsoft, Walmart, eBay and NASA.

B. The publisher of this guide

One entry in this roster is published by the company that published the roster. It sits in its own block so that it cannot be counted as a peer in any of the other six categories.

5. Forage AI

Disclosure: Forage AI published this article. This entry is held to the same evidentiary standard as the other 25, and the review table below states our real posture rather than a rating.

Attribute	Detail
Delivery model	Managed data extraction and data acquisition, delivered end to end as an outcome. Not a standalone labeling service
Workforce model	Managed teams running human-in-the-loop validation inside managed engagements. Employment status is not published on forage.ai, so by this article’s own transparency test it is unverified from the outside
Workforce geography	Not published on forage.ai as of 6 August 2026. On-premise delivery is available for sensitive environments
Ownership / independence	Private and independent. No investor, valuation or parent disclosed on forage.ai
Best for	Teams whose real problem is upstream: the corpus does not exist yet, or exists only as unstructured web pages and documents
Not for	Anyone who already holds the data and needs it annotated. We do not sell standalone data labeling and are not taking clients for it. One of the other 25 entries fits that problem better
Modalities	Web page content, documents and PDFs, firmographic and professional records. Not an image, video, LiDAR or speech annotation shop
Specialist domains	Web data extraction at scale, intelligent document processing, firmographic data
Core features	Web Data Extraction, Intelligent Document Processing (IDP), Firmographic Data, Managed Data Extraction, data engineering and post-processing
Quality method	Multi-layer validation with human-in-the-loop review and a 3x QA team on every delivery
Security & compliance	SOC 2, with the same gap this article flags in others: as of 6 August 2026 forage.ai states no Type, names no auditor, gives no scope and publishes no trust centre or downloadable report. By the three-part test in section 2 we would fail our own question here, and the honest answer is to ask us for the report rather than take the two words on trust. On-premise deployment available. Clients own all extracted data and Forage does not resell it
Pricing posture	Quote-only. Forage AI does not publish pricing
Typical engagement / minimums	From sign-off to first dataset in 1 to 2 weeks
Watch-out	Labeling and annotation sit within our capabilities but are delivered only inside managed extraction engagements. We do not sell them standalone, and we are not currently taking clients for standalone annotation. If pure annotation is your requirement, this entry is the wrong fit and we would rather say so here than in a sales call

Review attribute	Detail
G2	No public reviews against a data labeling or annotation product as of 6 August 2026
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No public reviews as of 6 August 2026
Employee sentiment (Glassdoor)	Not printed. We are not going to publish a self-referential sentiment figure in our own roundup
Evidence quality	We have no third-party customer review evidence for a labeling or annotation offering, because we do not sell one. Applying our own standard: absence is the finding, and it is printed rather than hidden
What reviewers praise	Not applicable. No public review corpus exists to summarise
What reviewers complain about	Not applicable. No public review corpus exists to summarise

What it is. A managed data extraction partner. Forage AI acquires, structures, quality-checks and delivers web and document data at scale, and takes ownership of the whole pipeline rather than selling tooling.

Best for. Teams whose blocker is that the dataset does not exist yet. Not for teams that already hold the corpus and need it labelled, because that is a service we do not sell and are not taking on. That is the entire reason this entry sits inside the article rather than being implied at the end of it.

What customers say. Nothing on the public review platforms, and this entry prints that absence for the same reason it prints Scale AI’s and Surge AI’s. A vendor with no reviews is not worse than a vendor with fifteen, and a publisher who exempts itself from its own evidence standard has no standard.

C. Expert-data marketplaces: RLHF, evaluations and domain experts

This is the tier most “2026” rosters do not contain at all, and it is where the money moved after June 2025. It is also the tier with the thinnest published certification evidence in the roster, which is an uncomfortable combination.

6. Surge AI

Attribute	Detail
Delivery model	Platform plus managed expert delivery
Workforce model	Credential-gated expert network with published recruiting bars
Workforce geography	Not disclosed. The network is quantified purely by credential (200,000+ PhDs, 500+ disciplines, 80+ languages, 30,000+ publications) and never by country
Ownership / independence	Bootstrapped with roughly $300k of personal savings and no venture capital from its 2021 launch until July 2025, when it began its first raise at a valuation Bloomberg reported at at least $25 billion. Founder Edwin Chen holds a majority. Around 110 full-time employees
Best for	Frontier RLHF, preference data and evaluation work where the credential of the annotator is the product
Not for	Regulated buyers who need a certification artefact, or anyone with a data-residency obligation
Modalities	Text, code, multimodal, preference and rubric data
Specialist domains	Law, medicine, finance, journalism, software engineering, consulting
Core features	Expert recruiting with published bars (legal roles require Supreme Court clerk, appellate advocate or senior counsel; journalism requires a Pulitzer, National Magazine Award or equivalent), managed expert delivery, RLHF and evaluation programmes
Quality method	Credential gating at recruitment. No published QA sampling or IAA methodology
Security & compliance	None published on surgehq.ai. The security URL returns 404. A third-party aggregator asserts SOC 2, GDPR, PCI, HIPAA, ISO 27001, FedRAMP and CSA STAR; none of it is first-party and it is not printed here as fact
Pricing posture	Quote-only. The only published rate is contractor pay, up to $200 to $400 per hour for a journalist role, which is supply-side, not customer price
Typical engagement / minimums	Not disclosed
Watch-out	Zero published certifications, zero disclosed geography, and no named clients on their own site. Press reports Anthropic, OpenAI, Google, Microsoft, Meta and the US Army as customers, which is press, not disclosure

Review attribute	Detail
G2	No public reviews on G2 as of 6 August 2026 (n=0)
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	No usable rating located as of 6 August 2026
Evidence quality	Zero public buyer evidence of any kind. For a company at this reported valuation, that is a striking finding rather than a neutral blank
What reviewers praise	Not applicable. No public review corpus exists
What reviewers complain about	Not applicable. No public review corpus exists

What it is. A 2020-founded expert-data company with the most explicitly credential-gated recruiting model in the roster, selling RLHF, preference and evaluation data to frontier labs.

Best for. Work where the annotator’s professional credential is the deliverable. Not for regulated buyers, because there is no certification artefact to hand your security team.

What customers say. Nothing, anywhere. In a category where review volume mostly tracks customer-marketing motion, silence from a bootstrapped company selling to a handful of labs is expected, but your diligence has to come from references rather than from platforms.

7. Mercor

Attribute	Detail
Delivery model	Marketplace
Workforce model	Vetted expert network, 30,000+ experts, matched to lab projects on hourly contracts
Workforce geography	Not disclosed
Ownership / independence	$350M Series C at a $10B valuation, 27 October 2025, led by Felicis with Benchmark, General Catalyst and Robinhood Ventures. Five times the $2B Series B eight months earlier
Best for	Standing up domain-expert capacity quickly, on hourly terms, with an evidenced security posture
Not for	Buyers who need a fixed per-unit price, or a workforce whose location they can name
Modalities	Text, code, multimodal, preference and rubric data, expert evaluations
Specialist domains	Accounting, finance, computer science research, medicine, law
Core features	Live expert marketplace with posted roles and rates, hourly contracting, lab-project matching
Quality method	Expert vetting at intake. No published QA sampling or IAA methodology
Security & compliance	SOC 2 Type 2, and only that, but genuinely evidenced: a downloadable SOC 2 Type II 2026 report, a 2026 penetration test, a Mandiant report and a BC/DR plan on trust.mercor.com. Narrow claims with real artefacts, the direct foil to Cogito Tech
Pricing posture	Quote-only for buyers. The rates posted publicly (for example “Accounting Expert $80 to $120/hr”, “Computer Science PhD Researchers $70 to $90/hr”) are supply-side
Typical engagement / minimums	Hourly contracts; disburses roughly $1.5M per day to contractors at $85 to $95 per hour average
Watch-out	A security incident in late March 2026. Mercor was affected by a supply-chain attack involving LiteLLM, with unauthorised access between 24 and 30 March 2026; Mercor confirmed it publicly on 31 March. The Lapsus$ group claimed roughly 4TB of data; reporting describes Slack data, internal ticketing, source code, database records and candidate personal information among the material involved. Meta paused its contracts indefinitely, while OpenAI investigated its exposure and did not pause. A class action filed 1 April 2026 alleges more than 40,000 people were affected; Mercor says only “a very limited subset” of its nearly five million experts had sensitive information affected and has published no number. Five suits were on file within the first week and at least seven in federal courts in California and Texas by late April 2026. Mercor notified affected individuals on 25 and 26 June 2026 and offered identity protection. It held SOC 2 Type 2 throughout, which is the point: the certificate did not prevent this (Fortune, 2 April 2026; TechCrunch, 9 April 2026; Mercor statement, 25 June 2026)

Review attribute	Detail
G2	4.9/5 (n=6), accessed 6 August 2026. Below a usable sample size, and the page is CAPTCHA-walled, with the count recorded as 6 in some views and 7 in others. Treat as unverified
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	4.4/5 (n=640) on mercor.io, accessed 6 August 2026. Contributor-side, not customer reviews. The profile is claimed and on a paid subscription, reviews are actively solicited, and 637 of the 640 arrived inside the last twelve months. A secondary domain profile shows 2.5/5 (n=5), unsolicited
Employee sentiment (Glassdoor)	4.2 (n≈162), accessed 6 August 2026. Employee sentiment
Evidence quality	Solicitation asymmetry applies here. Mercor’s 4.4 sits beside Turing’s unsolicited 2.8 (n=194) and LXT’s unsolicited 2.6 (n=6). A material part of that gap is a collection-method difference, not a quality difference
What reviewers praise	Timely payment and clear task flow, from contributors on Trustpilot
What reviewers complain about	Inconsistent project availability, from contributors on Trustpilot

What it is. A 2023-founded San Francisco marketplace matching credentialed domain experts to AI-lab projects on hourly contracts, and the fastest-repricing asset in the category.

Best for. Buyers who need expert hours quickly and value a downloadable audit report over a long certification list. Not for fixed per-unit budgeting, or for anyone whose obligation requires naming the workforce’s jurisdiction.

What customers say. Nothing usable on the buyer side. The visible 4.4 comes from 640 contributors on a paid, actively solicited profile, which is a different population and a different collection method from the unsolicited profiles it gets compared against.

8. Turing

Attribute	Detail
Delivery model	Managed plus platform
Workforce model	Vetted expert network across coding, reasoning and STEM
Workforce geography	Not disclosed
Ownership / independence	Private, VC-backed. Roughly $247M raised at a ~$2.2B valuation; $111M Series E in March 2025 led by Khazanah Nasional. ARR reported around $300M, up from $167M
Best for	Coding, reasoning and agentic training data for model developers
Not for	Buyers with a security questionnaire, because there is nothing published to answer it with
Modalities	Text, code, reasoning traces, RL environments
Specialist domains	Software engineering, STEM, enterprise knowledge work
Core features	Domain Software Engineering, Enterprise Knowledge Work, Frontier STEM Capability, off-the-shelf datasets, RL environments
Quality method	Expert vetting at intake. No published QA methodology
Security & compliance	None found. turing.com/security and /trust both return 404 and trust.turing.com does not resolve. No SOC 2, ISO 27001, HIPAA, GDPR or PCI DSS claim anywhere on turing.com
Pricing posture	Quote-only. Published rates are expert-side
Typical engagement / minimums	Not disclosed
Watch-out	Beyond the missing security pages, turing.com/about, /about-us, /company/about and /company/news all returned 404 on 6 August 2026, so the About link in their own navigation is broken

Review attribute	Detail
G2	4.2/5 (n=18), accessed 6 August 2026. The page is CAPTCHA-walled to automated reads. The figure held consistent across G2’s own product and comparison views on re-check, but it has not been confirmed on a rendered live page
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	TrustRadius 3.95/5 (n=4), accessed 6 August 2026. Below the n=5 threshold, so formally no public reviews. Gartner not systematically checked
Trustpilot	2.8/5 (n=194), accessed 6 August 2026. Contributor-side. Bimodal: roughly 70% five-star and 22% one-star, with the company replying to about 17% of negatives. Unsolicited profile
Employee sentiment (Glassdoor)	3.5 (n≈764), accessed 6 August 2026. Employee sentiment
Evidence quality	Solicitation asymmetry applies. Turing’s 2.8 is unsolicited and Mercor’s 4.4 is paid and solicited, so the two are not directly comparable
What reviewers praise	Pay rates and project interest, from contributors on Trustpilot
What reviewers complain about	Screening process length and payment disputes, from contributors on Trustpilot

What it is. A 2018-founded company that pivoted from remote-developer staffing into frontier-AI human data, now selling coding, reasoning, STEM and agentic training data to labs.

Best for. Model developers who need software and reasoning data at pace. Not for any buyer whose security review requires a published framework, because Turing publishes none.

What customers say. Effectively nothing verifiable. All three visible figures are blocked, below threshold, or contributor-side, so treat them as unusable for vendor comparison.

9. Handshake AI

Attribute	Detail
Delivery model	Fellowship-run expert network
Workforce model	Credential-gated fellows recruited from an 18M+ student and alumni graph; 100k+ fellows, $100M+ paid out
Workforce geography	The sharpest constraint in the roster, and it is published: United States only. “You must be based in the U.S. and have a valid work authorization … STEM OPT is not supported”
Ownership / independence	Private, VC-backed. Last reported valuation $3.5B; acquired Cleanlab in early 2026. Private-markets research firm Sacra estimates roughly $1.1B annualised gross revenue by April 2026
Best for	US-data-residency and US-persons requirements, and academic-credentialed expert data
Not for	Any programme needing non-US coverage or non-English language work
Modalities	Text, code, multimodal, expert evaluations
Specialist domains	Academic disciplines across the graph, plus medicine, law and finance at the top rate band
Core features	Handshake AI Fellowship, verified academic identity graph built on the parent network’s 18M+ students and alumni, credential gating by degree level
Quality method	Credential verification through the academic graph. No published QA sampling methodology for the data business
Security & compliance	Trust portal lists SOC 2 Type II, PCI DSS v4.0.1, GDPR, CCPA, TX-RAMP, UK Cyber Essentials and EU-U.S. DPF. The scope covers the recruiting products, not the AI data unit. A real certificate that does not cover what you would be buying
Pricing posture	Quote-only for buyers. Published rates are fellow-side: Handshake’s own fellowship postings run roughly $40 to $125 per hour, with higher bands for medicine, law and finance that Handshake does not publish as a consolidated rate card
Typical engagement / minimums	Not disclosed
Watch-out	The certification scope gap above. Also, the “trusted by” logos on the trust portal are employers on the career network, not AI-data customers, and should not be read as client references

Review attribute	Detail
G2	No review-platform coverage of Handshake AI’s data business as of 6 August 2026. Any “Handshake” G2 figure belongs to the college-recruiting product and is not printed here
Capterra	Same. No AI-data profile exists; the Capterra listings under this name are the recruiting product and an unrelated wholesale-commerce vendor
TrustRadius / Gartner Peer Insights	Same. Gartner’s vendor page states Handshake has exactly one product in one market, and it is not this one
Trustpilot	A single handshake.ai-specific review (n=1), which is below any threshold. Formally, no public reviews
Employee sentiment (Glassdoor)	The 3.2 (n≈294, accessed 6 August 2026) figure belongs to the parent career-network company, not the AI division, and is not a rating of this business
Evidence quality	The clearest name collision in the category. Every publicly circulating “Handshake” rating measures a different product
What reviewers praise	Not applicable for the AI data business
What reviewers complain about	Not applicable for the AI data business

What it is. The AI data division of Handshake, the Gen-Z career network, launched in 2025 and run as a fellowship that converts a twelve-year academic identity graph into a credentialed expert workforce.

Best for. Buyers with US-persons or US-residency requirements who need academic credentials verified rather than asserted. Not for multilingual or non-US work, where the published work-authorisation constraint is disqualifying by design.

What customers say. There is no review-platform coverage of this business anywhere. Note also that handshake.ai is a separate, unrelated company doing audit and assurance for AI agents, which is a third entity in the same name space.

10. Micro1

Attribute	Detail
Delivery model	Platform plus expert network, positioned as a “data lab”
Workforce model	Top-percentile vetted experts, with anti-cheat integrity scoring
Workforce geography	Not disclosed
Ownership / independence	$35M Series A at a $500M valuation, 12 September 2025, led by 01 Advisors. Roughly $41.6M total across four rounds. A December 2025 Forbes piece describes a multibillion-dollar trajectory, but no confirmed priced round above $500M was located
Best for	RL environments, frontier evaluations and expert-demonstrated robotics data
Not for	Government or regulated buyers who need a certification artefact
Modalities	Text, code, multimodal, robotics demonstration data
Specialist domains	Robotics, agent evaluation, academic and clinical expertise
Core features	Realm (RL environments and frontier evaluations), Cortex (contextual evaluation for production agents), Robotics (expert-demonstrated real-world data)
Quality method	Anti-cheat integrity scoring on the expert side. No published QA sampling or IAA methodology
Security & compliance	None. The Legal Center contains only website privacy, cookie, candidate and referral terms, and trust.micro1.ai does not resolve. Notable given they market a Government partnerships track
Pricing posture	Quote-only
Typical engagement / minimums	Not disclosed
Watch-out	No published certifications alongside a Government track, and no named clients; a third-party analyst estimate names Microsoft, which is unverified

Review attribute	Detail
G2	No public reviews as of 6 August 2026
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	Approximately 4/5 (n≈9,333), accessed 6 August 2026. Candidate-side reviews of the AI interviewer product, not customer reviews of a labeling service. Pagination was inconsistent; treat n as approximate
Employee sentiment (Glassdoor)	No usable rating located as of 6 August 2026
Evidence quality	The large visible sample is the single most misleading number in this category, because it measures job candidates rather than buyers. On the buyer side, there is nothing
What reviewers praise	Interview experience, from candidates on Trustpilot
What reviewers complain about	Interview outcome opacity, from candidates on Trustpilot

What it is. An April 2022-founded expert network and platform selling RL environments, agent evaluation and robotics demonstration data, recruiting professors, MDs and PhDs with integrity scoring on top.

Best for. Frontier evaluation and robotics work where expert demonstration is the deliverable. Not for regulated or public-sector buyers, given the absence of any published certification.

What customers say. Nothing on the buyer side. The large visible Trustpilot volume is candidate sentiment about an AI interviewer, and reading it as customer satisfaction would be a category error.

D. High-volume BPO and crowd-scale workforce

Where multilingual breadth and surge capacity actually live, and where the ownership corrections in this article cluster.

11. TELUS Digital (AI Data Solutions)

Attribute	Detail
Delivery model	Hybrid at the largest scale in the roster: BPO delivery centres, a proprietary training platform, and an open crowd
Workforce model	BPO delivery centres plus an open “AI Community” crowd of 1M+ annotators
Workforce geography	Broadly disclosed: 82,000+ team members across 35+ countries including the Philippines, Canada, US, Guatemala, El Salvador, Ireland, Bulgaria, Romania, China, Morocco and South Africa. 500+ annotation languages and dialects; 2B+ labels annually
Ownership / independence	Wholly owned subsidiary of TELUS Corporation (TSX: T / NYSE: TU). The privatization closed on 31 October 2025 at US$4.50 per share, roughly US$539M for the shares TELUS did not own; NYSE removal took effect 11 November 2025 and TIXT no longer trades. The legal entity is still TELUS International (Cda) Inc. and the brand is still TELUS Digital, and there was no rebrand at privatization. Any guide describing TELUS Digital as NYSE-listed is stale
Best for	Programmes needing genuine 500-language coverage and large surge capacity
Not for	Buyers needing a certification scope that covers the whole entity rather than the labeling facilities
Modalities	Text, audio and speech, image, video, document, multimodal
Specialist domains	Search relevance, content moderation, localisation, speech
Core features	AI Data Solutions built by acquisition (Lionbridge AI, completed 31 December 2020 at roughly C$1.2B, and Playment in 2021), proprietary annotation platform, AI Community crowd
Quality method	Platform-managed multi-pass review. No published IAA threshold
Security & compliance	Note the hedging, verbatim: “We are SOC 2 compliant and TISAX certified, and our labeling facilities are ISO 27001 certified.” No Type I or II designation is given, and ISO 27001 is scoped to labeling facilities rather than the entity
Pricing posture	Quote-only
Typical engagement / minimums	Not disclosed
Watch-out	The delisting correction above, the unspecified SOC 2 Type, and the facility-scoped ISO 27001. No named clients on their own site; they lead with Everest Group placement instead

Review attribute	Detail
G2	No public reviews on G2 as of 6 August 2026 (n=0). The seller page carries G2’s own “not enough reviews to provide buying insight” notice
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	Not printed; no consolidated figure isolated for the AI Data Solutions unit
Evidence quality	One of the largest annotation operations on earth carries no customer-review footprint. Absence is the finding
What reviewers praise	Not applicable. No public review corpus exists
What reviewers complain about	Not applicable. No public review corpus exists

What it is. Founded in 2005 as TELUS International and renamed TELUS Digital in 2024, with an AI Data Solutions arm assembled by acquiring Lionbridge AI and Playment, running the largest published label volume in this roster.

Best for. Language breadth and volume that few operations can match. Not for security reviews that will not accept an ISO 27001 scoped only to the labeling facilities.

What customers say. Nothing publicly. Everest Group named TELUS International a Leader in its 2024 Data Annotation and Labeling PEAK Matrix assessment, which is analyst placement rather than customer evidence, and the two are not substitutes.

12. TaskUs

Attribute	Detail
Delivery model	Managed BPO plus a Work@Home model
Workforce model	BPO delivery centres, 64,000+ employees, 30+ languages
Workforce geography	31 named sites across Colombia, Croatia, Egypt, Greece, India, Ireland, Japan, Malaysia, Mexico, the Philippines (11 sites), Serbia, Taiwan and the US
Ownership / independence	Publicly listed, Nasdaq: TASK. The $16.50 per share, $1.62B Blackstone and co-founder take-private announced 9 May 2025 was voted down by stockholders on 8 October 2025, with no termination fee. TaskUs continues to trade
Best for	Certification-heavy, audit-heavy programmes needing site-level accountability
Not for	Small-volume or short-duration engagements
Modalities	Text, image, video, audio, document, multimodal
Specialist domains	Content moderation, trust and safety, customer experience, fintech and crypto operations
Core features	Managed delivery pods, Work@Home model, 31 named sites, audit-ready reporting
Quality method	Dedicated QA layer with site-level reporting. No published IAA threshold
Security & compliance	The fullest stack in the roster: SOC 2 Type II, ISO 27001, ISO 27701, HIPAA, GDPR, PCI DSS, HITRUST. Corroborated in the 10-K for SOC 2 Type 2, ISO 27001, HITRUST and PCI DSS
Pricing posture	Quote-only
Typical engagement / minimums	Enterprise contracts; not published
Watch-out	Named in litigation over the 2024-25 Coinbase data breach, where an amended complaint alleges agents in India took bribes to exfiltrate customer data. Pending litigation, allegations rather than findings. Separately, client concentration is disclosed by SEC obligation: Meta Platforms was the largest client at 19% of FY2023 revenue (22% in FY2022), with top-10 clients at 55% and top-20 at 68% of FY2023 revenue

Review attribute	Detail
G2	4.0/5 (n=11) on the seller page, accessed 6 August 2026. Small sample
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	4.0 (n≈18,094), accessed 6 August 2026. Employee sentiment, and the largest workforce sample in this article
Evidence quality	The two 4.0 figures are coincidental and measure different things. n=11 from buyers, n≈18,094 from employees
What reviewers praise	Delivery discipline and reporting (G2); culture and facilities (Glassdoor)
What reviewers complain about	Cost at lower volumes (G2); shift patterns (Glassdoor)

What it is. A 2008-founded BPO headquartered in New Braunfels, Texas, with delivery originating in Bacoor, Cavite, now running annotation, moderation and trust-and-safety work at 31 named sites.

Best for. Programmes where the certification stack and site-level auditability are procurement requirements. Not for small pilots, where a 64,000-person operation is the wrong shape.

What customers say. The G2 sample is too small to rank on. The far more useful evidence is the SEC-disclosed client concentration, a fact a private vendor never has to give you.

13. LXT (including clickworker)

Attribute	Detail
Delivery model	Managed service plus open crowd hybrid
Workforce model	Managed annotation and collection delivery, plus clickworker’s crowd of over 10 million contributors
Workforce geography	The fullest geography disclosure in the roster. Offices in Canada, the US, UK, Egypt, India, Romania, Turkey, Germany and Australia; 150+ countries and 1,000+ language locales; named secure facilities: two in Toronto, one in Mississauga, one in Montreal, one in Cairo. On-site deployment into the client’s own facility is offered
Ownership / independence	Private, headquartered in Toronto. clickworker was acquired by LXT: definitive agreement announced 17 December 2024, technology integration completed 31 July 2025, so roundups listing them separately are listing one company twice
Best for	Confidentiality-sensitive multilingual work needing named facilities or on-site delivery
Not for	Buyers requiring SOC 2, which LXT does not claim
Modalities	Audio and speech, text, image, video, document, multimodal
Specialist domains	Speech and language data collection, localisation, search relevance
Core features	Managed delivery, supervised annotation inside secure facilities, on-site deployment, clickworker crowd access
Quality method	Supervised delivery inside named facilities. No published IAA threshold
Security & compliance	LXT: ISO 27001 (technology and physical facilities), GDPR, HIPAA, OWASP Top Ten, PCI DSS compliant facilities. No SOC 2, no ISO 27701. clickworker: ISO 27001, GDPR, and a Crowdsourcing Code of Conduct certification, the only labour-fairness credential in this roster
Pricing posture	Quote-only for LXT. clickworker’s pricing URL resolves to a page with no rates on it, which is a fair illustration of the category norm
Typical engagement / minimums	Not disclosed
Watch-out	LXT publishes no named clients; all 20 case studies are anonymised. clickworker’s crowd figure has grown from roughly 500k in 2013 to 10 million now, so always date-stamp it. The Trustpilot location field reads “Anguilla”, which is a platform auto-placeholder and not LXT’s location

Review attribute	Detail
G2	No profile located as of 6 August 2026. Searches for “LXT” return LTX (Lightricks), a different company; do not use that profile
Capterra	No profile located as of 6 August 2026
TrustRadius / Gartner Peer Insights	No profile located; Gartner not systematically checked
Trustpilot	LXT 2.6/5 (n=6), accessed 6 August 2026, unclaimed profile, all six reviewers are contributors rather than buyers. clickworker 2.6/5, re-read 6 August 2026, worker-side; the review count did not resolve consistently and is not printed
Employee sentiment (Glassdoor)	3.4 (n≈134), accessed 6 August 2026. Employee sentiment
Evidence quality	No customer-side evidence exists for either brand. LXT’s 2.6 is unsolicited and n=6, which is below threshold; the solicitation-asymmetry caveat applies when it is placed beside Mercor’s solicited 4.4
What reviewers praise	Timely payment on some clickworker task types, from workers
What reviewers complain about	Unpaid or rejected tasks, account suspensions handled by automated support only, and withdrawal fees, from workers on clickworker’s profile

What it is. A 2010-founded Toronto company selling managed annotation and data collection, now holding clickworker’s ten-million-contributor crowd, with a confidentiality posture that includes supervised annotation inside named facilities and deployment into the client’s own site.

Best for. Multilingual work with a hard confidentiality constraint, where named facilities beat an anonymous crowd. Not for procurement checklists that require SOC 2.

What customers say. Nothing. Neither brand carries a customer-review profile. Both worker-side profiles sit at 2.6, and clickworker’s complaint pattern about unpaid tasks and automated-only support is consistent with that rating rather than in tension with it.

14. Toloka

Attribute	Detail
Delivery model	Platform plus marketplace
Workforce model	Open crowd evolving into a vetted expert network; contributor-facing brand is Mindrift.ai
Workforce geography	200,000+ experts across 100+ countries, 40+ languages, 50+ verified knowledge domains
Ownership / independence	Founded 2014 inside Yandex, now Amsterdam-based (Toloka AI BV) and spun into Nebius Group NV (Nasdaq: NBIS). A May 2025 round led by Bezos Expeditions saw Nebius relinquish controlling voting power while remaining a shareholder; Shopify CTO Mikhail Parakhin is Board Chairman. Amount not stated by Toloka; press reports roughly $72M
Best for	Crowd breadth with a real certification stack, moving toward expert and safety evaluation work
Not for	Healthcare or card-data workloads, since neither HIPAA nor PCI DSS is claimed
Modalities	Text, image, audio, video, multimodal, preference data
Specialist domains	Red-teaming, safety evaluation, search relevance, domain-expert evaluation
Core features	Crowd and expert marketplace, Mindrift.ai contributor brand, red-teaming and evaluation programmes
Quality method	Platform consensus and overlap controls. No published IAA threshold
Security & compliance	SOC 2 Type II, ISO 27001, ISO 27701, GDPR. One of only two vendors in the roster claiming ISO 27701 (the other is TaskUs). No HIPAA, no PCI DSS
Pricing posture	Structurally transparent, numerically opaque. No hidden fees, no minimums and no contracts; the buyer sets the per-task price and the platform adds a platform fee and an LLM-QA cost as an unpublished percentage of it. The price-structure document returned 403 on 6 August 2026
Typical engagement / minimums	No minimums, per their own statement
Watch-out	The unpublished percentage above means you cannot model total cost from the published structure alone. Ask for the platform fee in writing before the pilot

Review attribute	Detail
G2	A profile exists but carries no rating and no sample size as of 6 August 2026; G2’s own “not enough reviews to provide buying insight” notice sits on the page
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	Present but sample size not visible, accessed 6 August 2026. Worker-side. Complaints centre on low effective hourly pay, unpaid training and tests, and rejected submissions
Employee sentiment (Glassdoor)	n≈82, rating not visible as of 6 August 2026. Employee sentiment
Evidence quality	No usable customer-side rating with a visible sample size. The strongest evidence Toloka offers is its named client list, not a score
What reviewers praise	Task availability, from workers
What reviewers complain about	Effective hourly pay and rejected submissions, from workers

What it is. A crowd platform with a decade of history that has repositioned around vetted domain experts, red-teaming and safety evaluation, now operating outside Yandex control under Nebius Group.

Best for. Buyers who want crowd breadth plus a certification stack that includes ISO 27701. Not for regulated healthcare or payment-data work.

What customers say. No usable public rating exists. Toloka’s client disclosure is vendor-published rather than a logo wall: Anthropic, Amazon, Microsoft, poolside, Recraft and Shopify.

E. Managed mid-market and domain specialists

Sub-enterprise budgets that still need a dedicated team, and the tier where published rate cards start to appear.

15. CloudFactory

Attribute	Detail
Delivery model	Managed service with a platform layer
Workforce model	Employed in-house delivery teams, not an open crowd
Workforce geography	Delivery in Nepal, Kenya, the Philippines and Colombia; core team across the UK, US, Nepal and Kenya; 100% remote operation. No HQ address published on their own site; /locations returns 404
Ownership / independence	Private, VC and PE backed. Latest announced round $65M on 20 November 2019, including FTV Capital. Trackers disagree on total raised ($78.1M against up to $153M) and CloudFactory publishes no funding summary
Best for	A dedicated employed team on a mid-market budget, with a QA mechanism you can inspect
Not for	Buyers who need the vendor’s legal HQ and full funding history documented first-party
Modalities	Image, video, text, document, audio
Specialist domains	Insurtech, retail, agriculture, transportation, medical imaging support
Core features	Five-person “toli” teams with weekly face-to-face accuracy reviews, managed delivery pods, platform layer
Quality method	The most citable QA mechanism in the roster: workers organised into teams of five with weekly face-to-face accuracy reviews. A mechanism rather than an adjective
Security & compliance	The strongest disclosure page in the managed tier: ISO 27001:2022, SOC 2 validated against Security, Availability and Confidentiality (Type not stated), HIPAA as a Compliant Business Associate, GDPR “for over seven years”, ISO 9001:2015. Not claimed: ISO 27701, CCPA, PCI DSS
Pricing posture	Quote-only, but unusually they publish a page saying so: a consumption-based model based on annual spend commitments and customised rates, piece-rate and consumption-based, no free trial but a free analysis
Typical engagement / minimums	Annual commitments of spend; specific minimums not published
Watch-out	The SOC 2 Type is not stated, and the missing HQ address and funding disclosure make financial diligence a third-party exercise

Review attribute	Detail
G2	4.5/5 (n=11) on the seller page, accessed 6 August 2026. Small sample
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	n≈547, rating not visible as of 6 August 2026. Employee sentiment
Evidence quality	n=11 is directional only. The published QA mechanism is stronger evidence than the score
What reviewers praise	Team consistency and communication (G2)
What reviewers complain about	Ramp time on new task types (G2)

What it is. A 2010-founded managed service started in Nepal on the premise that talent is distributed more evenly than opportunity, running employed delivery teams rather than a crowd.

Best for. Mid-market buyers who want a persistent employed cohort and a QA mechanism they can audit. Not for anyone whose diligence requires the vendor’s own site to document HQ and funding.

What customers say. The G2 sample is directional only. Named clients include Compound Eye, Driver Technologies, Deepcell, Matterport, Expensify, Ibotta, Sartorius and Activ Surgical, against a claimed 700+ client base.

16. Cogito Tech

Attribute	Detail
Delivery model	Managed service
Workforce model	BPO delivery centres with managed teams of trained specialists by industry, platform-agnostic
Workforce geography	Best delivery-centre disclosure in the roster: three named Noida addresses (A-83 Sector-2; C-01 Sector-59; C-40 Sector-59). Headcount not disclosed
Ownership / independence	Privately held and bootstrapped. No disclosed funding, no institutional investors, no parent, not public. Founded 2011 by Rohan Agrawal, Levittown, New York
Best for	Buyers who value named delivery addresses and are willing to verify certifications themselves
Not for	Regulated buyers who need certification artefacts at signature
Modalities	Image, video, text, document, audio, multimodal
Specialist domains	Healthcare, autonomous vehicles, retail, content moderation, RLHF and red teaming
Core features	Managed expert teams, HITL workflows, content moderation, RLHF, fine-tuning, prompt engineering, red teaming
Quality method	Managed teams with industry-trained specialists. No published QA sampling or IAA methodology
Security & compliance	Claims six: “EU GDPR Certified”, “SOC 2 TYPE II Certified”, “HIPAA Certified”, “ISO 27001:2013 accredited secure facilities worldwide”, “ISO 9001 certified” and CCPA compliance. No verifying artefact is published for any of them. Two factual notes: HIPAA has no certifying body, so “HIPAA Certified” is not a real designation; and the ISO claim cites the superseded 2013 revision where peers cite 27001:2022
Pricing posture	Quote-only. There is no pricing page on the site at all
Typical engagement / minimums	Not disclosed
Watch-out	The certification position above is the “most claims, least evidence” end of the spectrum in this roster. Separately, the logo wall (OpenAI, Medtronic, AWS, NBC Universal, Siemens, Samsara, Stryker) carries no backing case studies and should be treated as claimed rather than verified. Name collision: Cogito Tech is not Cogito Corporation, the contact-centre AI company acquired by Verint in 2024, and not Epic Cogito

Review attribute	Detail
G2	A “COGITO Tech Data labeling” profile exists showing about 4.7, but the sample size resolved as 12 in one view and 14 in another as of 6 August 2026, so no figure is printed here. Check you are on that profile and not one of the several unrelated “Cogito” products
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Gartner listing not resolved on this pass; not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	n≈45, rating not visible as of 6 August 2026. Employee sentiment
Evidence quality	No usable public rating. Better evidence sits in the named testimonial sources than in the logo wall
What reviewers praise	Not established from a public review corpus
What reviewers complain about	Not established from a public review corpus

What it is. A 2011-founded managed annotation provider running BPO delivery from three named Noida centres, covering annotation, moderation, RLHF and red teaming for enterprise buyers.

Best for. Buyers who weight physical delivery-address disclosure heavily and will do their own certification verification. Not for regulated programmes that need the report in hand.

What customers say. No usable public rating exists. The named testimonial sources (Machine AI, VISUA Labs, Tilesview.ai, Axiom Bio, Smart Eye, Verdure Imaging) are better evidence than the tier-1 logo wall, because they are attributable.

17. Label Your Data

Attribute	Detail
Delivery model	Hybrid service plus platform
Workforce model	Managed employed annotator teams, not an open crowd. The site runs a named annotator roster and the FAQ calls annotators “our employees”
Workforce geography	1,000+ teammates across 22 countries and 3 offices; named annotator countries include Poland, Ukraine, Bulgaria, Slovenia, Lithuania, Moldova, Morocco and Kenya. Their own pages disagree: 22 against 25 countries, and roughly 200 to 500 against 1,000+ annotators
Ownership / independence	Founded 2020. Stated subsidiary of SupportYourApp, sourced only to the vendor’s own article; no VC round found. US entity in Wilmington DE, EU entity in Nicosia, Cyprus; no single stated HQ
Best for	Mid-market teams that want published rates and no annual commitment
Not for	Buyers who require a SOC 2 report or a dedicated security page
Modalities	Image, video, text, audio, document
Specialist domains	Retail, agriculture, security, medical imaging support
Core features	Managed employed annotator teams, free pilot, self-serve entry path, published rate card
Quality method	Managed team review. No published IAA threshold
Security & compliance	Claims PCI DSS Level 1, ISO 27001, GDPR and HIPAA, plus CCPA adherence. No SOC 2 claim, and no security page: both /security and /data-security return 404
Pricing posture	Published, one of only six in the roster: bounding box from $0.02 per object; keypoint $0.015 per object; NLP $0.02 per entity; $6 per annotator hour
Typical engagement / minimums	No minimum monthly volumes or annual contracts. Free pilot; self-serve requires a $100 balance top-up after the trial
Watch-out	The internal inconsistencies in their own headcount and country figures, and the parent-company relationship sourced only to their own content

Review attribute	Detail
G2	4.9/5 (n=15), accessed 6 August 2026. A high score on a small sample
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	No usable rating located as of 6 August 2026
Evidence quality	This is exactly the comparison this article warns about. A 4.9 from 15 and a 4.9 from 284 are not the same claim, and only one of them discriminates between vendors
What reviewers praise	Turnaround speed and price transparency (G2)
What reviewers complain about	Capacity limits on large surges (G2)

What it is. A 2020-founded hybrid service running employed annotator teams across 22 countries, and one of the few vendors in this market that publishes an actual rate card.

Best for. Mid-market programmes that need real numbers before a procurement cycle starts and cannot commit annually. Not for buyers whose security review requires SOC 2 or a security page that resolves.

What customers say. A 4.9 from 15 buyers is not the same claim as a 4.9 from 284, and only one of them discriminates. Named clients include ABB, UiPath, Yale, Bizerba, George Washington University, Toptal, Hypatos, Ouster, Zendar, Respeecher, Geberit and NODAR.

F. Annotation platforms and tooling-first hybrids

You supply the workforce; they supply the software. Several of these attach an expert network, which moves them along the spectrum without changing which risk you retain by default.

18. Labelbox

Attribute	Detail
Delivery model	Hybrid: SaaS platform plus a vetted expert crowd branded Alignerr, plus an add-on team of professional labelers
Workforce model	Vetted expert crowd, 2.6M+ contributors claimed
Workforce geography	Breadth disclosed, delivery footprint not. 40+ countries and 75+ languages, named markets including the US, India, UK, Germany, Canada, France, Japan, Brazil, South Korea and Australia; credential mix claimed at 50k+ PhDs, 200k+ master’s and 85k+ licensed professionals
Ownership / independence	Private, VC-backed, $189M total. Latest disclosed round is a $110M Series D on 6 January 2022 led by SoftBank Vision Fund 2, with Snowpoint, Databricks Ventures, B Capital and a16z. No post-2022 round found
Best for	Platform-first teams that want an expert network attached without changing tools
Not for	Anyone budgeting from published rates, because there are none any more
Modalities	Image, video, text, audio, document, multimodal, LLM and RL data
Specialist domains	Frontier model training and evaluation, computer vision, content understanding
Core features	Catalog, Annotate, Model, plus Horizon, Terra and Recursion; Alignerr expert network
Quality method	Consensus and review workflows inside the platform. No published IAA threshold
Security & compliance	Security page states SOC 2 Type II and a HIPAA compliance programme. ISO 27001 appears there only as a standard that informs the programme, not as a held certification, while a company blog post claims ISO 27001:2022; the two have not been reconciled and the trust portal is gated
Pricing posture	Quote-only, following a correction. Labelbox removed its public pricing page: both the pricing URL and the calculator return 404, verified 6 August 2026, and “Pricing” no longer appears in navigation or footer. The per-LBU and per-hour figures circulating in other roundups cite a dead page and are not reproduced here
Typical engagement / minimums	Unit definitions survive without dollar values: 500 free LBU credits per month; 1 LBU per 60 data rows for Catalog storage; 1 LBU per data row for image, text and audio annotation; 20 LBU per data row for live multimodal and LLM work; 1 LBU per 5 data rows for Model
Watch-out	The removed pricing page, and the unreconciled ISO 27001 position between the security page and the blog

Review attribute	Detail
G2	4.5/5 (n=48), accessed 6 August 2026. The n=47 that circulates is an older crawl of the same page
Capterra	Live read required; no figure confirmed as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	No usable rating located as of 6 August 2026
Evidence quality	n=48 is one of the six usable customer samples in this roster, alongside SuperAnnotate, Encord, V7, Kili and Roboflow
What reviewers praise	Catalog search and model-assisted labeling workflows (G2)
What reviewers complain about	Cost predictability and the credit model (G2)

What it is. A 2018-founded San Francisco platform company now branding itself around reinforcement-learning data, with an expert crowd (Alignerr) attached to the tooling.

Best for. Teams that want one platform plus optional labour without switching vendors. Not for buyers who need to model cost before a sales conversation.

What customers say. A genuinely usable sample by this category’s standards. Named clients include Meta, through a first-party case study covering 820 expert-authored problems for a Meta benchmark, plus Google, Ideogram, Speak, ElevenLabs and Suno.

19. SuperAnnotate

Attribute	Detail
Delivery model	Hybrid: platform plus a global network of vetted experts
Workforce model	Vetted expert network
Workforce geography	Not disclosed beyond “global network.” No country list, no delivery-centre map
Ownership / independence	Private, VC-backed, roughly $67.5M total. Latest is a ~$13.5M Series B extension in June 2025 led by Dell Technologies Capital, bringing Series B to $50M; the initial $36M Series B (late 2024) was led by Socium Ventures with NVIDIA, Databricks Ventures and Play Time Ventures
Best for	Teams that want the deepest customer-review evidence in the category alongside a real trust centre
Not for	Buyers with a data-residency obligation, since annotator geography is undisclosed
Modalities	Image, video, text, audio, document, multimodal, LLM data
Specialist domains	Computer vision, LLM fine-tuning and evaluation, healthcare imaging support
Core features	Annotation platform, orchestration and QA tooling, expert network, model evaluation workflows
Quality method	Multi-stage review inside the platform with configurable QA steps. No published IAA threshold
Security & compliance	The strongest certification evidence in the platform tier: SOC 2 Type II, ISO/IEC 27001:2022, GDPR, CCPA, with the SOC 2 Type II and ISO 27001:2022 reports downloadable on request through a clean trust centre. Important caveat: a sentence on their marketing security page listing HIPAA, PCI DSS, ISO 27017, 27018 and 9001 describes their authentication vendor’s certifications, not SuperAnnotate’s, and those are not attributed to SuperAnnotate here
Pricing posture	Quote-only. Three tiers with no dollar figures; the only quantified differentiator is compute hours (1k / 2.5k / 10k)
Typical engagement / minimums	Not disclosed
Watch-out	Client disclosure is thin relative to the review profile: Databricks and ServiceNow are named, against a G2 sample larger than any peer’s

Review attribute	Detail
G2	4.9/5 (n=284), accessed 6 August 2026. The review-volume outlier of the entire category, against Labelbox at n=48 and Encord at n=61
Capterra	5.0/5 (n=2), accessed 6 August 2026. Below threshold; not a rating
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	No usable rating located as of 6 August 2026
Evidence quality	The strongest customer-side sample in the roster. It also demonstrates that review volume is not evenly earned across this market: a 4.9 from 284 and a 4.9 from 15 are not the same claim
What reviewers praise	Annotation throughput tooling and responsive support (G2)
What reviewers complain about	Learning curve on advanced orchestration features (G2)

What it is. A 2018-founded platform and expert network founded by brothers Vahan and Tigran Petrosyan, headquartered in San Francisco with substantial engineering in Yerevan.

Best for. Buyers who weight third-party review evidence and want reports available on request. Not for programmes with a hard data-residency requirement.

What customers say. The only large customer sample in the platform tier. Note the asymmetry: the review footprint is far larger than the named-client footprint, which is unusual and worth asking about in references.

20. Encord

Attribute	Detail
Delivery model	Platform-first with managed services attached
Workforce model	Partner and expert network
Workforce geography	Not disclosed. No annotator location, delivery-centre or country information anywhere on encord.com, despite selling managed annotation services
Ownership / independence	Private, VC-backed, $110M total. Latest is a $60M Series C led by Wellington Management in February 2026; prior $30M Series B in August 2024 led by Next47. Y Combinator-backed, London with a San Francisco office
Best for	Physical AI work: robotics, autonomous vehicles and drone data
Not for	Buyers who need the SOC 2 Type stated before signature
Modalities	Image, video, 3D/LiDAR, DICOM and NIfTI, geospatial, ECG, text, multimodal
Specialist domains	Physical AI, robotics, autonomous vehicles, medical imaging
Core features	Annotation, Index, Active, Data Agents, plus a services arm covering annotation, data collection and physical AI data
Quality method	Active provides quality analytics over labelled data. No published IAA threshold
Security & compliance	The “narrow claims, real evidence” end of the spectrum: AICPA SOC 2 (stated as having successfully completed its SOC 2 examination), plus HIPAA and GDPR self-attested through Vanta automation rather than third-party audit certificates. Type I against Type II is not stated on the security page, so a Type II claim cannot be supported from that page alone. Explicitly not claimed: ISO 27001, ISO 27701, CCPA, PCI DSS. Published specifics: AES-256 at rest, TLS 1.2/1.3 in transit, US and EU deployment regions, VPC and on-prem options
Pricing posture	Quote-only. Three tiers, a long feature matrix, and zero dollar figures; no currency symbol appears on the rendered page. Quantified tiering is data volume only (up to 500k / 100m / 1bn+)
Typical engagement / minimums	Not disclosed. Many items are marked “add on” and unpriced, including on-prem, DICOM and NIfTI, geospatial, ECG, 3D/LiDAR, LLM evaluations and a solutions architect
Watch-out	Selling managed annotation while disclosing no annotator geography is the single biggest gap in an otherwise strong evidence posture. Encord’s own About page also publishes no founding date and no address

Review attribute	Detail
G2	4.8/5 (n=61), accessed 6 August 2026
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	No usable rating located as of 6 August 2026
Evidence quality	n=61 is a usable sample and one of the better ones in this article
What reviewers praise	Video and medical imaging workflows, and support responsiveness (G2)
What reviewers complain about	Pricing opacity and add-on structure (G2)

What it is. A 2020-founded London platform company, Y Combinator-backed, pivoting hard toward physical AI data in 2026 while keeping a managed services arm.

Best for. Robotics, AV and drone programmes that need specialised modality support. Not for buyers who cannot proceed without a stated SOC 2 Type.

What customers say. A usable sample by this category’s standards. UiPath appears as a first-party case study, alongside 18 customer stories of which most are anonymised by industry, against a claim of 300+ top AI teams.

21. V7 (V7 Darwin)

Attribute	Detail
Delivery model	Platform-first with a vetted partner and expert network
Workforce model	Contracted specialists including qualified radiologists and pathologists. V7 does not employ the labelers; it sources, manages and contracts them
Workforce geography	Not disclosed. “Various geographies,” none named
Ownership / independence	Independent, VC-backed. Latest is a $33M Series A on 28 November 2022 co-led by Radical Ventures and Temasek, with Air Street, Amadeus and Partech; roughly $36M total. No Series B as of 6 August 2026
Best for	Medical and document workflows that need clinician-level annotators and a full security page
Not for	Buyers who need the annotators to be employees of the vendor
Modalities	Image, video, medical imaging, document, text
Specialist domains	Medical and clinical, life sciences, insurance and document processing, industrial inspection
Core features	Darwin platform, auto-annotation, workflow orchestration, contracted specialist network
Quality method	Review stages within Darwin workflows. V7’s published cost-reduction percentages are vendor self-reported and are not printed here as benchmarks
Security & compliance	SOC 2 Type II (audited), ISO 27001, HIPAA, GDPR, on a properly published security page. No ISO 27701, CCPA or PCI DSS. The services page adds “HIPAA-certified annotators” and “FDA-compliant annotation workflows”
Pricing posture	Quote-only. Custom package based on platform, users and data volume
Typical engagement / minimums	Not disclosed
Watch-out	The annotators are contracted rather than employed, which matters for continuity and for confidentiality controls. Several client names circulating in third-party databases were not confirmed first-party and are not printed here

Review attribute	Detail
G2	4.8/5 (n=54), accessed 6 August 2026
Capterra	Live read required; no figure confirmed as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	No usable rating located as of 6 August 2026
Evidence quality	n=54 is usable. Combined with a full security page, V7 has one of the better evidence positions in the platform tier
What reviewers praise	Medical imaging workflows and annotation speed (G2)
What reviewers complain about	Pricing structure clarity for smaller teams (G2)

What it is. A London platform company founded in 2018 (with an earlier San Francisco predecessor), selling Darwin plus a contracted network of specialists that includes clinicians.

Best for. Regulated medical and document workflows where the annotator’s professional qualification matters. Not for buyers whose contract requires employed annotators.

What customers say. A usable sample, and combined with a full security page it gives V7 one of the better evidence positions in the tier. Named clients include Genmab, Miovision, Abyss Solutions, CattleEye, Imidex, InformAI, Intelligent Ultrasound, Raptor Maps, Safari AI, Digital Divide Data, Velmeni, Yembo and Franklin.ai.

22. Kili Technology

Attribute	Detail
Delivery model	Platform-first hybrid; labeling services are an optional paid add-on
Workforce model	Not disclosed
Workforce geography	Not disclosed. Deployment regions (cloud, on-prem, air-gapped) are infrastructure, not labour
Ownership / independence	Private, VC-backed. Seed €5.7M in January 2021 (Serena Capital and Headline), Series A $25M led by Balderton Capital. No valuation disclosed. Paris-registered SAS
Best for	European enterprises needing on-prem or air-gapped deployment with a live trust centre
Not for	Buyers whose evaluation requires knowing where the labeling workforce sits
Modalities	Image, video, text, document, audio, satellite and geospatial
Specialist domains	Financial services and KYC, insurance, defence and geospatial, gaming
Core features	Annotation platform, quality workflows, on-prem and air-gapped deployment, optional dedicated labeling services
Quality method	Platform review and consensus workflows. No published IAA threshold
Security & compliance	SOC 2 Type II, ISO 27001:2022, HIPAA, with annual audits, plus a live trust centre. No ISO 27701 or PCI DSS
Pricing posture	Partially published: free trial at $0 per month (1 seat; 100 assets for text, document and image, 5 for video and satellite); Grow is a custom subscription (up to 20 seats, up to 50,000 assets); Enterprise is a custom contract. Net effect: tier structure published, no dollar rates
Typical engagement / minimums	Grow caps at 20 seats and 50,000 assets; Enterprise is negotiated
Watch-out	Workforce is undisclosed while labeling services are sold as an add-on, which is the same gap Encord has

Review attribute	Detail
G2	4.7/5 (n=53), accessed 6 August 2026. The n=49 that circulates is an older crawl of the same page
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	No usable rating located as of 6 August 2026
Evidence quality	n=53 is usable, and mid-range for the platform tier
What reviewers praise	Deployment flexibility and quality workflows (G2)
What reviewers complain about	Onboarding complexity for smaller teams (G2)

What it is. A 2018-founded Paris platform company selling annotation software with on-prem and air-gapped options, plus dedicated labeling services as a paid add-on.

Best for. European enterprises with deployment constraints and a real security review. Not for anyone who needs the workforce’s location documented.

What customers say. A usable sample, though the exact figure needs a live read. Written case studies are stronger evidence than a logo wall here, covering LCL Bank (KYC document extraction), Covéa, Eidos-Montréal, Jellysmack and Enabled Intelligence. One neutral note for the record: Kili is the single publisher in this SERP whose own roundup discloses that it ranks itself. That is a fact about the SERP rather than an endorsement, and it belongs here because it is the honest counterpart to the disclosure at the top of this article.

G. Open-source, self-serve and cloud-native

The options the vendor-authored roundups have the least reason to mention, and the only tier where pricing is mostly published.

23. Label Studio (HumanSignal)

Attribute	Detail
Delivery model	Open source, plus SaaS, plus managed data services
Workforce model	Self-hosted (you) or vendor-managed; “native speakers in 91 countries” for the managed services arm
Workforce geography	Partially disclosed. The 91-country claim covers language coverage; specific delivery-centre or physical data lab locations are not disclosed. HumanSignal publishes no company address on its own site
Ownership / independence	Private, VC-backed, $30M total from Redpoint Ventures, Unusual Ventures, Bow Capital and Swift Ventures. No round-by-round breakdown, no dates and no valuation published, so whether $30M is current cannot be confirmed first-party
Best for	Teams that want a permissively licensed self-hosted core with an optional paid cloud tier
Not for	Buyers with a formal certification checklist, because the disclosure is the weakest in the platform tier
Modalities	Image, video, text, audio, time series, document, multimodal
Specialist domains	General-purpose; strongest in NLP and audio community usage
Core features	Label Studio Community Edition (self-hosted, free), Label Studio Enterprise (SaaS or on-prem), managed data services covering from-scratch data creation, annotation and evaluation by domain experts
Quality method	Configurable review and agreement tooling in Enterprise, including LLM-as-a-judge. No published IAA threshold
Security & compliance	Weakest formal disclosure in the platform tier. Enterprise is described as SOC 2 and HIPAA compliant cloud or on-prem, with SOC 2 and HIPAA reporting as an Enterprise-only feature and no SOC 2 Type stated. The security page describes controls (TLS enforced, encryption at rest, SAML 2.0, separated data and control planes) but names no certification, with details behind a gated brief. Not claimed anywhere: ISO 27001, ISO 27701, GDPR, CCPA, PCI DSS, and the absence of any GDPR statement is notable given every EU-facing peer carries one
Pricing posture	Published, one of only six: Community Edition free and self-hosted; Starter Cloud $99 per user per month (up to 12 users, free trial); Enterprise custom. Managed data services are quote-only
Typical engagement / minimums	Starter Cloud caps at 12 users. Enterprise is purchasable through AWS Marketplace against committed AWS spend
Watch-out	Licence: Apache 2.0, verified from the repository. Permissive, no copyleft, no source-available commercial restriction, which materially differentiates it from source-available competitors. Also: three mutually inconsistent user counts appear across their own site, so none is cited here

Review attribute	Detail
G2	Figures not visible as of 6 August 2026; live read required
Capterra	4.0/5 (n=2), accessed 6 August 2026. Below threshold; not a rating
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	No usable rating located as of 6 August 2026
Evidence quality	A structural finding worth stating once: open-source tools with developer user bases barely register on commercial B2B review sites. The absence reflects buyer behaviour, not product quality
What reviewers praise	Flexibility of the labeling interface configuration, from the small Capterra sample
What reviewers complain about	Setup effort for self-hosting, from the small Capterra sample

What it is. A 2019-founded company (formerly Heartex) publishing the most widely used open-source labeling interface, with a paid cloud tier and a managed data services arm on top.

Best for. Engineering teams that want to self-host under a permissive licence and buy support later. Not for procurement processes that require a named certification with a stated Type.

What customers say. Almost nothing on the review platforms, which is normal for open-source tooling and should not be read as a quality signal in either direction. Named clients include Bombora, Geberit, Outreach, Trivago and Zendesk.

24. CVAT.ai

Attribute	Detail
Delivery model	Open source, plus managed cloud, plus enterprise self-host, plus first-party annotation services
Workforce model	Self-hosted (you) or first-party Computer Vision and Audio Annotation Services teams
Workforce geography	Not disclosed for the labeling-services workforce. Registered agent in Wilmington DE; EU entity CVAT.ai Europe Ltd in Paphos, Cyprus; cvat.ai publishes no HQ city
Ownership / independence	Independent. No funding round announced on cvat.ai, and no first-party or credible-press priced round located. Project started inside Intel in 2017, open-sourced 2018, spun out as CVAT.ai Corporation in 2022 by ex-Intel engineers Nikita Manovich and Boris Sekachev
Best for	Computer vision teams that want fully published pricing and a permissively licensed core
Not for	Regulated buyers, because no security certification is claimed at all
Modalities	Image, video, 3D point cloud, audio
Specialist domains	General computer vision; audio annotation as a first-party service
Core features	CVAT Community (self-hosted OSS), CVAT Online (managed cloud), CVAT Enterprise (self-hosted commercial), first-party annotation services teams
Quality method	Built-in quality control and honeypot-style validation in the platform. No published IAA threshold
Security & compliance	The sharpest contrast in the open-source tier: GDPR, CCPA and EU AI Act compliance only. No SOC 2, no ISO 27001, no ISO 27701, no HIPAA and no PCI DSS anywhere on the pricing or enterprise pages
Pricing posture	Fully published: Free $0; Solo $33 per month monthly or $23 billed yearly; Team $33 per user per month monthly with a 2-seat minimum ($66) or $23 per user per month yearly (2-seat minimum, $46); Enterprise Basic $12,000 per year for a single-instance deployment with essential support; Enterprise Premium custom
Typical engagement / minimums	2-seat minimum on Team; Team caps at 50 members
Watch-out	Licence: MIT for CVAT Community, with a caveat published in the same README. Code under /serverless is MIT but bundles third-party assets under separate licences including non-commercial restrictions, and FFmpeg components are LGPL or GPL. A free and open-source option with non-commercial components inside it is exactly the detail a legal team finds late

Review attribute	Detail
G2	4.5/5 (n=31), accessed 6 August 2026, read live. A conflicting 4.6 to 4.8 appears elsewhere; the platform page figure is the one used here
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	No public reviews as of 6 August 2026
Trustpilot	No public reviews as of 6 August 2026
Employee sentiment (Glassdoor)	No usable rating located as of 6 August 2026
Evidence quality	One of the few figures in this article read live rather than from a search index. n=31 is usable but modest
What reviewers praise	Annotation speed for video and the value of the free tier (G2)
What reviewers complain about	Self-hosting maintenance burden (G2)

What it is. The Computer Vision Annotation Tool, started inside Intel in 2017 and now an independent company selling managed cloud and enterprise self-host alongside the open-source core.

Best for. Computer vision teams that want to model cost precisely before committing. Not for buyers whose security review requires SOC 2 or ISO 27001.

What customers say. One of the few figures in this article confirmed by live read. Client evidence is unusual: CVAT publishes named individuals with job titles rather than corporate logos, and only two of the four are identifiable companies.

25. Roboflow

Attribute	Detail
Delivery model	Platform (self-serve plus enterprise) with an optional managed labeling add-on
Workforce model	Undisclosed for the labeling add-on
Workforce geography	Not disclosed. Roboflow also publishes no street address or HQ city on its own pages
Ownership / independence	Private, VC-backed. $40M Series B on 19 November 2024 led by GV, with Craft Ventures and Y Combinator; valuation not disclosed. Prior ~$2.1M seed (January 2021) and $20M Series A (September 2021, Craft Ventures)
Best for	Computer vision teams that want to start labeling today at a published rate
Not for	Buyers needing ISO 27001 or GDPR statements in the compliance section
Modalities	Image, video
Specialist domains	Industrial inspection, agriculture, sports analytics, logistics
Core features	Annotate, Train, Deploy, Workflows, Universe; optional managed labeling
Quality method	Platform review workflows. No published IAA threshold
Security & compliance	SOC 2 and HIPAA only. No ISO 27001, ISO 27701, PCI DSS or GDPR appears in the Compliance section, and the badge reads “SOC 2” without specifying Type publicly, though a report is available on request
Pricing posture	Published: Free (15 credits per month, 2 users); Core $79 per month billed annually or $99 monthly (50 credits per month, 3 users); credit add-ons at +$120, +$280 and +$590 per month for +30, +100 and +200 credits; extra seats $29 per user per month up to 10 on Core; data labeling services from $0.10 per bounding box; Enterprise custom
Typical engagement / minimums	Self-serve from free; no annual minimum below Enterprise
Watch-out	The labeling add-on’s workforce is undisclosed, so the vendor’s transparency on pricing is not matched on labour

Review attribute	Detail
G2	4.7/5 (n=108), accessed 6 August 2026. One of only two ratings in the roster built on more than 100 customer reviews, the other being SuperAnnotate’s n=284. A conflicting 4.8 appears elsewhere; the platform page figure is used here
Capterra	Live read required; no figure confirmed as of 6 August 2026
TrustRadius / Gartner Peer Insights	Live read required; not systematically checked
Trustpilot	Live read required; no figure confirmed as of 6 August 2026
Employee sentiment (Glassdoor)	No usable rating located as of 6 August 2026
Evidence quality	A proof point for this article’s review-vacuum finding. Two vendors out of 26, Roboflow and SuperAnnotate, carry a customer rating at a sample size that would be unremarkable in any other software category
What reviewers praise	Speed from dataset to deployed model, and documentation quality (G2)
What reviewers complain about	Credit consumption predictability at higher volumes (G2)

What it is. A 2020-founded computer vision platform (Y Combinator S20) covering annotation through deployment, with labeling available as a priced add-on rather than a sales conversation.

Best for. Computer vision teams that want to move today without procurement. Not for buyers whose compliance section has to include ISO 27001 or an explicit GDPR statement.

What customers say. One of only two customer-review positions in this roster built on more than 100 reviews. Trust-centre named clients include John Deere, Cardinal Health, USG, Intel, Rivian, Medtronic and Column, with full case studies for BNSF Railway, USG and Fletcher Sports.

26. AWS SageMaker Ground Truth

Attribute	Detail
Delivery model	Cloud-native service, not a company
Workforce model	Three options: Amazon Mechanical Turk (open crowd), third-party vendors through AWS Marketplace (managed service), or your own private workforce. The private-workforce path is the concrete BYO example the three-purchases framework needs
Workforce geography	Not disclosed as a service attribute. The MTurk crowd is global, and Marketplace vendor workforces vary by listing
Ownership / independence	AWS Inc., Seattle; parent Amazon.com (Nasdaq: AMZN). The largest and most independently verifiable owner in the roster, and also a hyperscaler that may compete with you elsewhere
Best for	Teams already using Ground Truth on AWS who want to keep the data and the workforce inside their own account
Not for	New buyers, since the service closed to new customers on 30 July 2026; and anyone who needs the certification to cover the labeling workflow rather than the cloud underneath it
Modalities	Image, video, text, 3D point cloud
Specialist domains	General-purpose; strongest where the data already sits in S3
Core features	Labeling job orchestration, three workforce options, automated data labeling, 3D point cloud workflows
Quality method	Consensus and annotation consolidation built into labeling jobs. No published IAA threshold
Security & compliance	A useful nuance: Ground Truth inherits the AWS compliance programme (SOC 2, ISO 27001, ISO 27701, HIPAA eligibility, PCI DSS, GDPR), but none of it is asserted on the Ground Truth page itself. “Your cloud already does this” also means the certification belongs to the cloud, not to the labeling workflow
Pricing posture	Fully published (US East, N. Virginia): reviewed objects per month under 50,000 at $0.08 each; 50,000 to 1,000,000 at $0.04; above 1,000,000 at $0.02. 3D point clouds: $3.00 per single frame; $3.00 for the first frame of a sequence and $1.50 thereafter. MTurk labour, suggested price per labeler: image classification $0.012, text classification $0.012, NER $0.024, bounding box $0.036, semantic segmentation $0.84. Vendor workforce cost per label is set by the vendor and published per listing in AWS Marketplace. Note for verification: those rate tables rendered empty in a live browser on 6 August 2026; the figures were read from AWS’s own backing pricing feed and are authoritative, but a spot-checker will see blank tables
Typical engagement / minimums	Pay as you go; no minimum
Watch-out	Two dated facts from AWS’s own service availability notice, and they are different products. First: Ground Truth Plus, the AWS-managed labeling workforce, reached end of support on 30 June 2026 and is no longer available. Roundups still recommending it as a live managed option, including for its old cost-saving claim, are recommending something you cannot buy. Second: self-service SageMaker Ground Truth, the service profiled here, moved to maintenance and closed to new customers on 30 July 2026. AWS states that customers already using it can continue to do so. If you are not already on it, this entry is a reference point rather than an option

Review attribute	Detail
G2	4.1/5 (n=19), accessed 6 August 2026
Capterra	No public reviews as of 6 August 2026
TrustRadius / Gartner Peer Insights	Not systematically checked
Trustpilot	No customer-side profile located as of 6 August 2026
Employee sentiment (Glassdoor)	Not applicable at the service level
Evidence quality	n=19 is modest but usable, and it is the lowest customer rating among the vendors in this roster carrying a usable sample
What reviewers praise	Integration with the rest of the AWS stack (G2)
What reviewers complain about	Console usability and job configuration complexity (G2)

What it is. An AWS service launched at re:Invent in November 2018, offering labeling job orchestration on top of three different workforce options rather than a workforce of its own.

Best for. Teams already using Ground Truth on AWS who want to staff labeling with their own people on rented tooling. Not for new buyers, because AWS closed the service to them on 30 July 2026, or for anyone who needs a certification scoped to the labeling workflow itself.

What customers say. The lowest usable customer rating in this roster. Named clients include T-Mobile, the National Football League, AstraZeneca and Tyson Foods.

Data labeling companies compared: the master table

Twenty-six entries is more than anyone holds in their head. One scan view, on the six axes that shorten a shortlist. Read columns 2 and 3 together, because delivery model and disclosed workforce geography are the two hard filters. Column 4 is the question nobody else asks, and column 6 sits last because it is the weakest signal on this page.

There is no total column and this is not a scoreboard. A vendor with no reviews is not worse than a vendor with fifteen.

Company	Delivery model	Workforce model (geography disclosed?)	Ownership / independence	Pricing posture	Public customer reviews
Scale AI	Hybrid	Open gig crowd (No)	Meta holds 49% non-voting, June 2025	Quote-only	None usable (G2 n=1)
Appen	Hybrid	Open vetted crowd + secure facilities (Partial)	Public, ASX: APX, no controlling shareholder	Quote-only	G2 4.2 (n=35); Capterra 4.1 (n=40)
iMerit	Managed + platform	Employed in-house (Yes)	Private, impact-investor backed	Quote-only	G2 4.7 (n=13), small sample
Sama	Managed + platform	Employed in-house, impact sourcing (Yes)	Private, last round 2021	Quote-only	G2 4.6 (n=11), small sample
Forage AI	Managed data extraction	Not published (No)	Private, independent	Quote-only	None. We do not sell standalone labeling
Surge AI	Platform + managed expert	Credential-gated experts (No)	Bootstrapped to 2025, then first raise	Quote-only	None (G2 n=0)
Mercor	Marketplace	Vetted experts (No)	VC-backed, $10B, October 2025	Quote-only	None usable; Trustpilot 4.4 (n=640) is contributor-side and solicited
Turing	Managed + platform	Vetted experts (No)	VC-backed, ~$2.2B	Quote-only	None usable; Trustpilot 2.8 (n=194) contributor-side
Handshake AI	Fellowship network	Credential-gated, US only (Yes)	VC-backed, $3.5B reported	Quote-only	None. Circulating figures are the recruiting product
Micro1	Platform + expert network	Vetted experts (No)	VC-backed, $500M, September 2025	Quote-only	None; Trustpilot n≈9,333 is candidate-side
TELUS Digital	Hybrid at scale	BPO + open crowd (Yes)	Subsidiary of TELUS Corp; privatization closed 31 Oct 2025, TIXT off the NYSE from 11 Nov 2025	Quote-only	None (G2 n=0)
TaskUs	Managed BPO	BPO delivery centres, 31 sites (Yes)	Public, Nasdaq: TASK; take-private voted down 8 Oct 2025	Quote-only	G2 4.0 (n=11), small sample
LXT (incl. clickworker)	Managed + open crowd	Managed + 10M crowd (Yes)	Private; clickworker acquired by LXT, Dec 2024	Quote-only	None; Trustpilot 2.6 (n=6) contributor-side, and clickworker 2.6 worker-side
Toloka	Platform + marketplace	Crowd to vetted experts (Partial)	Nebius Group; Bezos Expeditions round, May 2025	Structure published, rates not	None usable; G2 profile carries no rating
CloudFactory	Managed + platform	Employed in-house (Yes)	Private, VC/PE, last round 2019	Quote-only, published as such	G2 4.5 (n=11), small sample
Cogito Tech	Managed service	BPO, three named Noida centres (Yes)	Private, bootstrapped	Quote-only, no pricing page	None printed; G2 sample size unresolved
Label Your Data	Hybrid	Employed annotator teams (Yes)	Stated subsidiary of SupportYourApp	Published	G2 4.9 (n=15), small sample
Labelbox	Platform + expert crowd	Vetted expert crowd (Partial)	VC-backed, last round 2022	Quote-only since the pricing page was removed	G2 4.5 (n=48)
SuperAnnotate	Platform + expert network	Vetted experts (No)	VC-backed, Series B extension 2025	Quote-only	G2 4.9 (n=284), the largest sample here
Encord	Platform-first	Partner/expert network (No)	VC-backed, $60M Series C, Feb 2026	Quote-only	G2 4.8 (n=61)
V7 (V7 Darwin)	Platform-first	Contracted specialists (No)	Independent, VC-backed, last round 2022	Quote-only	G2 4.8 (n=54)
Kili Technology	Platform-first hybrid	Not disclosed (No)	VC-backed, Balderton-led Series A	Partially published	G2 4.7 (n=53)
Label Studio (HumanSignal)	OSS + SaaS	Self-hosted or vendor-managed (Partial)	VC-backed, $30M reported	Published ($99/user/mo Starter)	None usable (Capterra n=2)
CVAT.ai	OSS + managed cloud	Self-hosted or first-party teams (No)	Independent, no announced round	Fully published	G2 4.5 (n=31), read live
Roboflow	Platform	Undisclosed for labeling add-on (No)	VC-backed, $40M Series B, Nov 2024	Fully published	G2 4.7 (n=108)
AWS SageMaker Ground Truth	Cloud-native, closed to new customers 30 Jul 2026	MTurk, Marketplace vendors, or your own (No)	AWS / Amazon (Nasdaq: AMZN)	Fully published	G2 4.1 (n=19)

What do data annotation services actually cost?

The SERP keeps the roster and the cost benchmarks in separate articles, which is convenient for publishers and useless for buyers. Here they are together.

Per-unit benchmarks by modality

Task type	Published 2026 benchmark range
Image classification	$0.02 to $0.15 per label
Object detection (bounding box)	$0.05 to $0.90 per object
Semantic segmentation	$0.50 to $2.00+ per mask
Text sentiment or intent classification	$0.01 to $0.10 per item
Named entity recognition	$0.05 to $0.25 per entity
Audio transcription	$0.50 to $3.00 per minute
Video annotation	$1.00 to $10.00 per minute
Medical or clinical items	$1.00 to $5.00+ per item
Hourly managed rates	$6 to $60+ per hour

The verifiable published rates, read from vendor pages on 6 August 2026, are narrower and more useful because you can check them. AWS SageMaker Ground Truth charges $0.08, $0.04 and $0.02 per reviewed object across its three volume tiers, and $3.00 per 3D point-cloud frame. CVAT.ai runs $23 to $33 per user per month with Enterprise Basic at $12,000 per year. Roboflow runs $79 to $99 per month with labeling from $0.10 per bounding box. Label Studio charges $99 per user per month for Starter Cloud. Label Your Data publishes $0.02 per bounding-box object, $0.015 per keypoint, $0.02 per NLP entity and $6 per annotator hour.

The five pricing models

Model	What you pay for	What it hides	When it fits
Per-unit	Each label, box, entity or minute	Rework, QA overhead, setup, minimums	Stable taxonomy, predictable volume
Hourly	Annotator time	Throughput variance between cohorts	Ambiguous or exploratory work
Per-project	A defined deliverable	Change-order pricing when scope moves	Fixed-scope one-offs
Dedicated FTE	A named cohort’s capacity	Idle time you still pay for	Long programmes with continuity needs
Platform seat or usage	Software access and consumption	Your own labour cost, which is the larger number	You already employ annotators

The landed-cost model

The per-unit rate is not the number that decides your bill. Landed cost is:

per-unit rate × rework multiplier + QA overhead + setup and minimums + guideline versioning + re-training the annotator cohort

Worked example. Vendor A quotes $0.01 per record at a 20% error rate. Vendor B quotes $0.04 per record at 1%. On 1,000,000 records, Vendor A’s direct cost is $10,000 and Vendor B’s is $40,000, and Vendor A looks four times cheaper. Then add rework: 200,000 records go back through at least once, which is another $2,000 of labeling and, more expensively, a QA cycle to find them, a guideline revision to prevent recurrence, and re-training for the cohort. Your own reviewers absorb that time at a fully loaded cost that dwarfs the labeling line. Vendor B’s 1% error rate is not a quality nicety. It is the reason the cheaper quote costs more.

Two conditional rules fall out of that. Below roughly 50,000 units, a managed vendor’s minimums usually dominate the economics, so a platform plus internal labour wins. And if the taxonomy is still moving, a fixed per-unit contract prices the wrong thing, because you will pay full rate to label a schema you are about to change. This is the same total-cost-of-ownership framing that applies to any managed data operation: the visible line item is rarely the expensive one.

On the labour side, rates stratify into bands that barely overlap. AFP’s October 2025 reporting documented $0.05 to $0.25 per task at the bottom, with some Remotasks work around $0.01 per task, against $30 to $50 per hour for specialised annotation. Entry-level gig work sits around $15 to $20 per hour where it is hourly at all. At the top, Mercor pays contractors $85 to $95 per hour on average, and Handshake AI’s published fellowship postings reach $125 per hour with higher unpublished bands in medicine, law and finance. Expert RLHF and medical review is often priced per example, in the $50 to $100 range. If a quote for expert-domain work looks like commodity pricing, one of the two numbers is wrong.

How to run the evaluation: pilot, quality bar, and contract

Label error is measurable before you sign, and measuring it is a procurement step rather than a research one.

No ranking roundup gives you a pilot protocol, an acceptance criterion or a contract clause. They all end at “contact them.” Here is what to do between the shortlist and the signature.

A two-week, three-vendor pilot protocol

Run two or three vendors in parallel, on one or two task types, inside two weeks. NDA and DPA signed before any sample data moves, without exception. A batch of roughly 500 units returned in five to ten business days from NDA is what a mature vendor should manage; the ones that cannot are telling you about their queue.

Score the output against your own benchmark set with real ground truth, not the vendor’s gold set, because a vendor scoring itself on its own gold items is measuring its ability to hit its own targets. Use a quality framework for external data sources to define what “correct” means before the batch goes out. Agreeing acceptance criteria after seeing the output is how pilots turn into arguments.

Measuring quality: gold sets, consensus, and IAA thresholds

Write the accuracy floor as a kappa or alpha threshold against a named gold set, not as a percentage. Percentages hide task difficulty and hide chance agreement; agreement statistics do not.

The standard interpretation bands come from Landis and Koch (1977): 0.00 to 0.20 slight, 0.21 to 0.40 fair, 0.41 to 0.60 moderate, 0.61 to 0.80 substantial, and 0.81 to 1.00 almost perfect.

For anything subjective, a result under 0.80 is where you start asking questions rather than where you sign. Specify which statistic you mean, because Cohen’s kappa, Fleiss’ kappa and Krippendorff’s alpha are not interchangeable and a vendor reporting “our IAA is 0.9” without naming the metric has told you nothing.

Guideline authorship and versioning is the largest single driver of label quality and the most common cause of a failed engagement, and it is almost never in the contract. Taxonomies drift, so the acceptance threshold gets re-measured on a cadence rather than set once at kickoff.

The RFP question list

Twelve questions, in the order that disqualifies fastest.

1. Who owns you, and who else do you serve?
2. Where does the labour physically sit, and can you name the facilities?
3. Are annotators employed, contracted, or crowd-sourced?
4. Do you subcontract, and to whom?
5. SOC 2: Type I or Type II, what period does the report cover, and what systems are in scope?
6. Can I have the report?
7. Who authors and versions the guidelines?
8. What is your QA sampling rate, and which strata does it cover?
9. Do you report inter-annotator agreement, on what metric, at what threshold?
10. What happens to the trained annotator cohort if I leave?
11. Who owns the labels, the guidelines and the ontology?
12. What export formats, and can I get the raw annotations plus the audit trail?

The SLA clause skeleton

Eight clauses. Most annotation contracts contain two of them.

1. Accuracy floor expressed as a Landis-Koch kappa threshold against a named gold-standard set, with the statistic specified.
2. Measurement method: sampling rate, strata covered, who measures, and how often it is re-measured as guidelines change.
3. Gold-standard definition: who builds it, who holds it, and how it is refreshed to prevent gold-set overfit.
4. Rework terms: what triggers rework, what the turnaround is, and who pays.
5. Ownership: labels, guidelines and ontology, named explicitly, because “the deliverable” is not the same as “the ontology we developed together.”
6. Subcontracting disclosure: prior written notice, with the right to refuse a named fourth party.
7. Access controls: named-individual annotator logins, no shared accounts, and an audit trail of who labelled what.
8. Exit and portability: export formats, the audit trail, and what happens to the trained cohort at termination.

The failure modes nobody writes down

Annotator churn mid-project, which resets everything the cohort learned about your edge cases. Silent subcontracting to a fourth party you never approved. QA sampling that only inspects the easy strata, so the measured quality is real and irrelevant. Gold-set overfit, where annotators learn the gold items and the measured quality stops predicting real quality. And the pilot-team bait-and-switch, where the A-team runs your pilot and the B-team runs your production volume. Contract for the cohort, not just for the vendor.

Once you have selected and signed, running the programme is a separate discipline, and the operational playbook for running a training-data program covers the part that starts after procurement ends.

When should you not hire a data labeling company?

All of that assumes you are hiring somebody. Four cases where the answer is not a vendor at all.

1. Keep it in-house. When the domain expert is already on your payroll and cannot be replicated externally, when the volume is small enough that a vendor’s minimum exceeds the work, when the taxonomy is still pre-product-market-fit, or when the data legally cannot leave your jurisdiction. Grand View Research puts in-house at roughly 15% of the 2024 market, so this is the minority case rather than the default.

2. Licence a finished dataset. Licensing a finished dataset somebody has already built is a different purchase with different economics and a much shorter time to first usable data, at the cost of any claim to a proprietary asset.

3. Programmatic or weak supervision. Write label functions and heuristics instead of hiring labelers. Snorkel AI is the reference implementation, named here rather than profiled because it is a different purchase, not a different vendor.

4. You do not have the data yet. Every vendor in this article assumes you already possess the corpus to be labelled.

Auto-labeling and synthetic data have not removed the human. They moved the human to audit, calibration and edge cases. Peer-reviewed work on LLM-assisted annotation with active learning, including AutoLabel presented at KSEM 2024 and published in the ACM Digital Library, demonstrates both the automated path and its limits, which is a better anchor than the 10x speedup and 75% savings figures circulating in this market, all of which come from companies selling the automation. Whether you need labelled data at all is worth settling before any of this, and that decision often turns on fine-tuning versus retrieval.

The strongest available counter-signal to “automation solved it” is the Meta case above. The company that paid $14.3 billion for 49% of Scale AI reassigned roughly 6,500 of its own engineers to write training data by hand in March 2026, and conceded the restructuring had gone wrong three months later.

How to choose: routing your shortlist

You came for the list, and the list is above. Here is the repeatable method for turning 26 names into three.

Start with the three questions. Everything in this article converges on the same three, and they shorten a shortlist faster than any feature comparison because each one is answerable in a week and each one produces a yes or a no rather than a score:

1. Who owns you, and who else do you serve? The criterion this market produced in 2025 and 2026. Meta at 49% of Scale AI, TIXT delisted, clickworker inside LXT, Shaip inside Ubiquity. Ownership is the one variable you inherit and cannot negotiate.
2. Where does the labour physically sit, and will you name the facilities? Twelve of the 28 vendors we swept publish zero contributor geographies. The moment you carry a data-residency or US-persons obligation, an unanswered version of this question is a disqualification, not a gap.
3. Which certification framework, which Type, what scope, and can I have the report? Four parts, one question. It separates Cogito Tech’s six unevidenced claims from Mercor’s single evidenced one, and it is what catches a genuine certificate scoped to the wrong product, as Handshake’s is.

Answer those and most of the 26 are gone. The rest of this section is the ordered path for what survives.

The routing path, in order.

1. Which purchase? Platform, managed outcome, your workforce on their tooling, or licence a dataset. This eliminates more of the roster than anything else here.
2. Does workforce geography disqualify anyone? Undisclosed geography is a disqualifier the moment you carry a data-residency or US-persons obligation.
3. Does ownership disqualify anyone? Does the vendor’s owner compete with you, and how would you find out if that changed mid-contract?
4. Modality and domain fit. Does the marketing page say “multimodal” while the case studies say bounding boxes?
5. Certification framework, Type and scope. Evidenced, not asserted.
6. Pricing posture and landed cost. Can you model the number, and does the procurement shape fit your cycle?
7. Review evidence, with sample size. Read last, and read sceptically.

The use-case routing table

Use case	Category to start in	2-3 names to look at	The decisive constraint
Medical and clinical	Enterprise, platform-first with clinicians	iMerit, V7, Encord	Annotator qualification and HIPAA scope
Autonomous vehicles and robotics	Enterprise, platform-first	Scale AI, Encord, Sama	3D and LiDAR tooling maturity
LLM, RLHF and evaluations	Expert-data marketplaces	Surge AI, Mercor, Handshake AI	Credential verification and no certification artefact to lean on
Multilingual speech and language	BPO and crowd-scale	TELUS Digital, Appen, LXT	Language locale count and secure-facility options
Documents and OCR	Platform-first, mid-market managed	Kili Technology, V7, Cogito Tech	Key-value extraction quality and document confidentiality
Content moderation	BPO and crowd-scale	TaskUs, Sama, TELUS Digital	Wellbeing programmes and site-level accountability
Small-volume self-serve	Open source and cloud-native	Roboflow, CVAT.ai, Label Your Data	Published pricing and no minimum commitment

The six disqualifiers

Disqualifying is faster than ranking. Any one of these ends the conversation:

• Workforce geography undisclosed while you carry a data-residency obligation.
• The vendor’s owner competes with you.
• A certification claimed with no artefact and no stated scope.
• A pricing posture incompatible with your procurement cycle.
• No published quality method, and no willingness to describe one in the pilot.
• A product past end of support.

Do not read “#1 in a guide” as market leadership. In at least 12 of roughly 15 ranking guides on this SERP, #1 is the publisher, and the disclosure standard set at the top of this article is the one to hold them to. The same diligence discipline applies upstream, and the enterprise evaluation checklist covers the version of this for data suppliers rather than annotation vendors.

A tie-breaker, because scorecards produce ties. If two vendors land within about 5% of each other on a weighted comparison, do not re-weight until one wins. Ask instead which dimension, if it failed in year two, would cost the most. For most enterprise annotation programmes that is annotator cohort continuity, because a cohort that has learned your edge cases is the asset you are actually buying and cannot be re-procured quickly. Where the programme touches regulated data, ownership and workforce geography displace it, because those two fail in ways that are not recoverable with money.

Frequently asked questions

How much does data labeling cost?

Published 2026 benchmarks run from roughly $0.02 per image classification label to $0.50 to $2.00+ for semantic segmentation, $0.50 to $3.00 per audio minute and $1.00 to $5.00+ for medical items, with hourly managed rates between $6 and $60+. Only six of the 28 vendors we checked on 6 August 2026 publish real figures, so most of this market prices by quote. The correction most buyers need: the lowest per-unit rate is frequently the most expensive vendor, because $0.01 per record at a 20% error rate costs more than $0.04 at 1% once rework, QA cycles and cohort re-training are counted.

What is the difference between a data labeling platform and a data labeling service?

They are opposite risk transfers. A platform sells you software and leaves the labour, throughput and quality risk with you; a service sells you an outcome and takes process visibility away in exchange. A third path that almost no roundup names is renting the vendor’s tooling and staffing it with your own workforce, of which AWS SageMaker Ground Truth’s private-workforce option is the clearest illustration, though AWS closed that service to new customers on 30 July 2026. Most vendors sit on a spectrum rather than in a box, so the useful question is which risk you retain after signing, not which label the vendor uses.

Should we outsource data labeling or do it in-house?

Outsourcing is the norm. Grand View Research put 84.6% of the 2024 data labeling solution and services market in the outsourced segment, which leaves in-house at roughly 15%. In-house wins in four specific situations: the domain expert is already on your payroll, the volume is small enough that vendor minimums dominate, the taxonomy is still moving, or the data cannot legally leave your jurisdiction. Outside those, the question is not whether to outsource but which of five workforce models to outsource to, because they behave differently on price, variance, ramp and confidentiality.

How do I evaluate annotation quality before signing a contract?

Run a two-week pilot with two or three vendors on a batch of roughly 500 units, with the NDA and DPA signed first, and score the output against your own benchmark set rather than the vendor’s gold set. Then write the accuracy floor into the contract as an agreement threshold rather than a percentage: on the Landis-Koch scale, published in Biometrics in 1977, 0.61 to 0.80 is substantial agreement and 0.81 to 1.00 is almost perfect. For subjective tasks, anything under 0.80 is a question rather than a signature. Name the statistic too, because Cohen’s kappa, Fleiss’ kappa and Krippendorff’s alpha answer different questions.

What certifications should a data annotation vendor have, and is my data safe?

Ask three questions rather than one: which framework, which Type, and what is in scope. Only three of the 28 vendors we checked claim ISO 27701 and only four claim PCI DSS, so a checklist that assumes either is standard will disqualify most of the market before you have evaluated anything. A certificate can be entirely genuine while covering a different product: Handshake’s SOC 2 Type II covers its recruiting products rather than its AI data unit. Ask for the report, the scope, and the report date.

The Ownership Question

Eighteen months ago, “who owns this vendor?” was a curiosity you might get to in the third meeting. Then Meta bought 49% of Scale AI for $14.3 billion, and Google, Microsoft, xAI and OpenAI were all reported reducing or ending their engagements inside a week. TELUS International’s privatization closed on 31 October 2025 and the shares came off the NYSE days later. TaskUs stayed public because its shareholders voted a take-private down on 8 October 2025. clickworker turned out to be inside LXT. Shaip turned out to be inside Ubiquity Global Services. Centific turned out to be Pactera EDGE with a new name.

None of those are scandals. They are ordinary corporate events, and every one of them changes the answer to a question your RFP probably does not ask: who owns this vendor, and who else do they serve?

That is the criterion this market produced in 2025 and 2026, and it belongs at the top of an evaluation rather than the bottom. It is checkable in an afternoon for a public company and genuinely hard for a private one, which is itself the finding. Certification you can request. Pricing you can negotiate. Ownership you inherit, and it can change without you.

Put it in the RFP as question one. The guides ranking for this search still do not ask it.

Before you label anything, you need the data

Every company in this guide, including our own entry, assumes you already have the corpus. Labeling adds structure to data you possess. It does not produce the data.

If the harder half of your problem is acquiring it, at scale, from the open web and from documents, and keeping it current as sources change, that is what Forage AI does. We run managed web data extraction end to end: discovery, extraction, structuring, quality assurance and delivery, as an outcome rather than as tooling you maintain. Clients own the data, and we do not resell it. For a broader view of how this category has developed, modern data extraction services in 2026 covers the landscape.

That is a different problem from annotation, and if annotation is what you need, the 25 other entries above are where to look.

See what Forage AI builds for AI teams →

Talk to our expert →

Sources

External claims in this article are attributed in prose rather than hyperlinked in the body, so that no annotation vendor is placed in this article’s citation graph. The full source list is below, ordered by first appearance. Vendor own-site pages appear only where a claim rests on them. All URLs accessed 6 August 2026.

1. CNBC, “Scale AI founder Wang announces exit for Meta, part of $14 billion deal”, 12 June 2025. https://www.cnbc.com/2025/06/12/scale-ai-founder-wang-announces-exit-for-meta-part-of-14-billion-deal.html
2. TechCrunch, “Scale AI confirms significant investment from Meta”, 13 June 2025. https://techcrunch.com/2025/06/13/scale-ai-confirms-significant-investment-from-meta-says-ceo-alexandr-wang-is-leaving/
3. Axios, “Meta finalizes Scale AI stake”, 13 June 2025. https://axios.com/2025/06/13/meta-scale-ai-deal
4. CNBC / Reuters, “Google, Scale AI’s largest customer, plans split after Meta deal”, 14 June 2025. https://www.cnbc.com/2025/06/14/google-scale-ais-largest-customer-plans-split-after-meta-deal.html
5. Appen Ltd, FY24 Results and FY25 Outlook (ASX: APX). https://www.listcorp.com/asx/apx/appen-limited/news/fy24-results-and-fy25-outlook-3156567.html
6. TechCrunch, “Mercor quintuples valuation to $10B with $350M Series C”, 27 October 2025. https://techcrunch.com/2025/10/27/mercor-quintuples-valuation-to-10b-with-350m-series-c/
7. Grand View Research, Data Labeling Solution and Services Market 2025–2030. https://www.grandviewresearch.com/industry-analysis/data-labeling-solution-services-market-report
8. Grand View Research, Data Collection and Labeling Market 2025–2030. https://www.grandviewresearch.com/industry-analysis/data-collection-labeling-market
9. TechRepublic, Scale AI Google Docs security lapse. https://www.techrepublic.com/article/news-scale-ai-security-lapse-google-docs-meta-xai/
10. Business Insider (syndicated), Scale AI exposed client documents. https://news.yahoo.com/bi-revealed-scale-ai-exposed-113949933.html
11. Everest Group, Data Annotation and Labeling (DAL) Solutions for AI/ML PEAK Matrix Assessment 2024. https://www.everestgrp.com/report/egr-2024-71-r-6348/
12. Business Wire, “TaskUs Named a Leader in Everest Group’s DAL PEAK Matrix Assessment 2024”, 11 March 2024. https://www.businesswire.com/news/home/20240311658483/en/TaskUs-Named-a-Leader-in-Everest-Groups-Data-Annotation-and-Labeling-DAL-PEAK-Matrix-Assessment-2024
13. iMerit press release, “Recognized as a Major Contributor in Everest Group’s inaugural DAL PEAK Matrix Assessment”. https://imerit.ai/press/imerit-recognized-as-a-major-contributor-in-data-annotation-and-labeling-services-for-ai-ml-in-everest-groups-inaugural-peak-matrix-assessment/
14. AFP (Daxia Rojas), “The gruelling, low-paid human work behind the generative AI curtain”, 17 October 2025 (syndicated). https://www.canadianaffairs.news/2025/10/17/the-gruelling-low-paid-human-work-behind-generative-ai-curtain/
15. Computer Weekly, “Kenyan AI workers form Data Labelers Association”. https://www.computerweekly.com/news/366619321/Kenyan-AI-workers-form-Data-Labelers-Association
16. Business & Human Rights Resource Centre, Kenyan data labellers and Appen. https://www.business-humanrights.org/en/latest-news/australia-kenyan-data-labellers-make-modern-slavery-allegations-against-ai-company-appen/
17. Billy Perrigo, TIME, “Exclusive: OpenAI used Kenyan workers on less than $2 per hour”, 18 January 2023. https://time.com/6247678/openai-chatgpt-kenya-workers/
18. TechTarget, “Podcast: Sama responds to AI data labeling criticism”. https://www.techtarget.com/searchenterpriseai/news/366567959/Podcast-Sama-responds-to-AI-data-labeling-criticism
19. Schwartz Reisman Institute, University of Toronto, “The data-production dispositif” (Dr. Milagros Miceli). https://srinstitute.utoronto.ca/news/the-data-production-dispositif
20. Miceli & Posada, “The Data-Production Dispositif”, arXiv:2205.11963. https://arxiv.org/abs/2205.11963
21. Antonio Casilli, note on the AFP report, 16 October 2025. https://www.casilli.fr/2025/10/16/new-report-by-afp/
22. CNBC, “OpenAI is winding down its work with Scale AI”, 18 June 2025. https://www.cnbc.com/2025/06/18/openai-is-winding-down-its-work-with-scale-ai-founder-is-joining-meta.html
23. TechCrunch, “OpenAI drops Scale AI as a data provider following Meta deal”, 18 June 2025. https://techcrunch.com/2025/06/18/openai-drops-scale-ai-as-a-data-provider-following-meta-deal/
24. CNBC, “Scale AI cuts 14% of workforce after Meta investment”, 16 July 2025. https://www.cnbc.com/2025/07/16/scale-ai-cuts-14percent-of-workforce-after-meta-investment-hiring-of-wang.html
25. AIwire / HPCwire, “Appen suffers major blow as Google terminates multi-million dollar contract”, 29 January 2024. https://www.hpcwire.com/aiwire/2024/01/29/appen-suffers-major-blow-as-google-terminates-multi-million-dollar-contract/
26. PPC Land, “Appen faces $82.8 million contract termination from Google”, 23 January 2024 (the figure is US$82.8M; Appen reports in US dollars). https://ppc.land/appen-faces-82-8-million-contract-termination-from-google/
27. Appen Ltd, FY25 Results and FY26 Outlook (ASX, 25 February 2026). https://www.listcorp.com/asx/apx/appen-limited/news/fy25-results-and-fy26-outlook-3319135.html
28. CNBC, “AI hiring startup Mercor raises at $10 billion valuation”, 27 October 2025. https://www.cnbc.com/2025/10/27/ai-hiring-startup-mercor-funding.html
29. TechCrunch, “Turing raises $111M Series E at a $2.2B valuation”, 6 March 2025. https://techcrunch.com/2025/03/06/turing-a-key-coding-provider-for-openai-and-other-llm-producers-raises-111m-at-a-2-2b-valuation
30. Business Wire, Turing Series E release, 6 March 2025. https://www.businesswire.com/news/home/20250306942806/en/Turing-Gears-Up-to-Power-Next-Wave-of-AGI-with-$111-Million-in-Series-E
31. Sacra (private-markets research), Handshake AI research note. https://sacra.com/research/handshake-indeed-for-data-labelers/
32. Bloomberg, “Scale rival Surge AI in talks for funding at $25 billion value”, 30 July 2025. https://www.bloomberg.com/news/articles/2025-07-30/scale-rival-surge-ai-in-talks-for-funding-at-25-billion-value
33. Inc., “Bootstrapped to $1 billion: Surge AI CEO Edwin Chen on how he did it”. https://www.inc.com/sam-blum/bootstrapped-to-1-billion-surge-ai-ceo-edwin-chen-on-how-he-did-it/91207937
34. Business Insider, “Scale AI’s rivals say they’re going hard after its customers”, 30 June 2025 (syndicated). https://www.aol.com/scale-ais-rivals-theyre-going-092354466.html
35. TechCrunch, “Meta’s months-old AI unit is a ‘soul-crushing gulag’, say the engineers stuck inside it”, 12 June 2026. https://techcrunch.com/2026/06/12/metas-months-old-ai-unit-is-a-soul-crushing-gulag-say-the-engineers-stuck-inside-it/
36. The Next Web, revolt at Meta’s Applied AI unit, 18 June 2026 (carrying Reuters, 2 June 2026, on the internal memos). https://thenextweb.com/news/meta-applied-ai-unit-revolt-data-labeling-draftees
37. Fortune, “Mercor, a $10 billion AI startup, confirms it was the victim of a major cybersecurity breach”, 2 April 2026. https://fortune.com/2026/04/02/mercor-ai-startup-security-incident-10-billion/
38. TechCrunch, “After data breach, $10B-valued startup Mercor is having a month”, 9 April 2026. https://techcrunch.com/2026/04/09/after-data-breach-10b-valued-startup-mercor-is-having-a-month
    38a. Mercor, “Update on Mercor Security Incident”, 25 June 2026. Cited only for Mercor’s own account of the incident, not as an authority for any market claim. https://www.mercor.com/blog/update-on-mercor-security-incident/
    38b. Appen Ltd, FY24 Investor Presentation, 26 February 2025 (US$ reporting basis; Google contract US$82.8M of FY23 revenue US$273.0M). https://www.aspecthuntley.com.au/asxdata/20250226/pdf/02917530.pdf
39. Northcutt, Athalye & Mueller, “Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks”, NeurIPS 2021 Datasets and Benchmarks Track. https://arxiv.org/abs/2103.14749
40. Landis & Koch, “The Measurement of Observer Agreement for Categorical Data”, Biometrics 33(1), 1977, 159–174. https://doi.org/10.2307/2529310
41. “AutoLabel: Automated Textual Data Annotation Based on Active Learning and LLMs”, KSEM 2024, ACM Digital Library. https://dl.acm.org/doi/10.1007/978-981-97-5501-1_30
42. Label Your Data, published rate card and company pages. https://labelyourdata.com/pricing
43. V7 Labs, security, pricing and about pages. https://www.v7labs.com/security
44. G2, Capterra, Trustpilot, TrustRadius and Glassdoor product and seller pages for all 26 entries. Every rating in this article is printed with its sample size (n) and the date accessed. Full per-vendor URL list held in the research file; all pages accessed 6 August 2026.

Primary sources behind the corrections table, each opened on 6 August 2026:

45. Amazon Web Services, “AWS Service Availability Updates”, 30 June 2026. Ground Truth Plus end of support 30 June 2026; SageMaker Ground Truth, Mechanical Turk and A2I to maintenance and closed to new customers 30 July 2026. https://aws.amazon.com/about-aws/whats-new/2026/06/aws-service-availability/
46. TaskUs Inc., results of the special meeting of stockholders, 8 October 2025, and the 9 October 2025 merger-agreement termination (SEC Form 8-K). https://ir.taskus.com/news-releases/news-release-details/taskus-inc-announces-results-special-meeting-stockholders-and
47. LXT, “LXT Acquires clickworker”, 17 December 2024. https://www.prnewswire.com/news-releases/lxt-acquires-clickworker-to-deliver-industry-leading-ai-data-solutions-302333052.html
48. LXT, “LXT Completes Integration of clickworker”, 31 July 2025. https://www.prnewswire.com/news-releases/lxt-completes-integration-of-clickworker-to-deliver-single-platform-for-industry-leading-ai-data-solutions-302517815.html
49. Ubiquity Global Services, “Ubiquity Acquires Shaip”, 12 February 2026. https://www.ubiquity.com/resources/news/ubiquity-acquires-shaip-ai
50. TELUS Corporation, “TELUS Completes Privatization of TELUS Digital”, 31 October 2025. https://www.prnewswire.com/news-releases/telus-completes-privatization-of-telus-digital-302600768.html
51. Sama, Data Security and Trust page (SOC 2 listed as certification in progress). https://www.sama.com/data-security-and-trust
52. Pactera EDGE, “Pactera EDGE Announces Strategic Rebrand as Centific”, 23 January 2023. https://www.prnewswire.com/news-releases/pactera-edge-announces-strategic-rebrand-as-centific-301727758.html
53. Amazon Web Services, SageMaker Ground Truth pricing page and its backing pricing feed (the rendered rate tables were empty on 6 August 2026; the feed is authoritative). https://aws.amazon.com/sagemaker/ai/pricing/ · https://b0.p.awsstatic.com/pricing/2.0/meteredUnitMaps/sagemaker/USD/current/groundtruth.json

Every figure above was re-checked in a technical verification pass on 6 August 2026. Where a platform blocks automated reads, or a figure could not be resolved to a primary source, the article says so at the point of use rather than printing a number.

About the author

Forage AI Research Team. This guide was researched and written by the Forage AI content and research team, which ran a same-day sweep of 28 vendor websites, security pages, pricing pages and public review profiles on 6 August 2026. Forage AI is a managed data extraction partner. Labeling and annotation sit within our capabilities inside managed engagements, and we do not sell standalone data labeling. Forage AI appears in this guide as entry #5 and is disclosed as the publisher in the introduction, in “How we evaluated” and at the entry itself.

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