Top AI Training Data Providers: Buyer’s Guide to the Four Categories

Search “AI training data providers” and the same names stack into one ranked column: Scale AI, Appen, Sama, Bright Data, a rotating cast of synthetic data startups.

A single ranking implies a like-for-like comparison. The market data does not support one.

These vendors sit in four structurally different businesses, and the numbers show the gap between them is widening, not closing.

The figures make the categories concrete. Meta took a non-voting minority stake in Scale AI in June 2025, reported at 49% and a valuation of roughly $29 billion, and Scale holds FedRAMP High authorization, because high-stakes labeling commands a premium SLA tier. Bright Data now lists more than 1,000 pre-built datasets and 21 billion-plus records, because raw-feed infrastructure is a volume business.

NVIDIA reportedly acquired Gretel in March 2025 and folded it into the NeMo stack, because synthetic generation is now a platform play. TELUS Digital, a Leader in Everest Group’s 2024 Data Annotation PEAK Matrix on 500-plus language coverage, was taken fully private by its parent TELUS in October 2025.

Four vendors, four cost structures, four buying motions. A flat top-10 averages them into one column and hides the exact distinction a buyer needs.

The single most expensive error in a 2026 training-data evaluation is comparing across categories that should never share a shortlist. A dataset marketplace, a labeling service, a custom sourcing partner, and a synthetic generator each clear a different bottleneck. Treating them as interchangeable is how an evaluation cycle ends in a signed contract that does not match the gap in the training program.

So the data leads, and the providers come first. Below are 19 named vendors, grouped by the four categories that carry the decision, each with a comparison table, a direct read on who it fits, and where the limits sit.

Forage AI sits at the top of the custom sourcing and extraction category because that is where we operate: an end-to-end managed data pipeline that automates discovery, extraction, structuring, quality checks and delivery, built to your own fields and systems. The seven-axis evaluation framework, the IP-cleanliness checklist and a plain guide to how buying actually works follow the list, once the names on the table are the right ones.

The 19 providers compared

Map your gap to a category, then to a vendor. The full roster is below; the sections after it carry the detail on each one.

Provider	Category	Best for	Pricing model	Public rating
Forage AI	Custom data sourcing	AI teams bottlenecked on getting to the data, not on labeling it. An end-to-end managed data pipeline built to your fields and systems, with no data reselling.	Scoped managed program (per-pipeline), not unit-priced per record	No verified public reviews
Bright Data	Custom data sourcing	Buyers who want high-volume raw web feeds and tooling to manage acquisition themselves.	Self-serve and subscription; catalogue datasets listed from $2.50 per 1,000 records	G2 4.7/5 (n=352)
Oxylabs	Custom data sourcing	Teams with strong internal data engineering who want managed infrastructure rather than a fully managed outcome.	Subscription and enterprise contracts with SLAs	G2 4.5/5 (n=424)
Innodata	Custom data sourcing	Buyers who want a public-company SLA and a vendor spanning data collection plus downstream labeling.	Enterprise services contracts	No usable customer-side rating. Trustpilot 2.9/5 (n=2) is below any usable threshold
Scale AI	Labeling and annotation	High-stakes labeling at premium SLAs: autonomous vehicles, defense, frontier LLM alignment.	Premium enterprise contracts	No usable customer rating. The G2 seller page shows 5.0 from n=1
Appen	Labeling and annotation	Language-coverage-heavy projects spanning many dialects.	Project-based and enterprise contracts	G2 4.2/5 (n=35) and Capterra 4.1/5 (n=40), customer-side
Surge AI	Labeling and annotation	Organizations training or aligning their own LLMs at scale.	Premium contracts benchmarked to frontier labs	No public rating on any platform
iMerit (EXL)	Labeling and annotation	Projects where annotator qualification is the constraint: healthcare, AV, geospatial.	Credentialed-annotator pricing	G2 4.7/5 (n=13). Small sample, directional only
TELUS Digital	Labeling and annotation	Global, multimodal labeling at enterprise scale.	Enterprise contracts	No customer-review footprint located
Sama	Labeling and annotation	Organizations that want a documented, auditable annotation chain, especially in computer vision.	Reflects the ethical-sourcing premium	G2 4.6/5 (n=11). Too small to support a comparative claim
Cogito Tech	Labeling and annotation	Mid-tier multimodal labeling with mature compliance documentation.	Mid-tier relative to premium vendors	A G2 profile shows about 4.7, but the sample resolved as 12 in one view and 14 in another
Datarade	Dataset marketplace	Early-stage scoping when you need to see what is out there before committing.	Varies by underlying provider	G2 4.5/5 (n=21). Small sample
Shaip	Dataset marketplace	Medical AI teams who need both licensed clinical datasets and matching annotations.	Catalog licensing plus annotation engagements	Not systematically checked for this guide
Defined.ai	Dataset marketplace	Voice-AI and conversational model teams.	Catalog licensing plus custom collection	Not systematically checked for this guide
Gretel (NVIDIA)	Synthetic data	Teams already standardized on NVIDIA’s stack who need privacy-preserving synthetic data.	Platform-based, within the NVIDIA NeMo ecosystem	G2 4.4/5 (n=13). Small sample
MOSTLY AI	Synthetic data	Regulated industries needing synthetic versions of production records.	Enterprise pricing	G2 4.5/5 (n=17), listed as MOSTLY AI Synthetic Data Platform
Tonic.ai	Synthetic data	Teams where speed and developer experience are the priority.	Platform subscription	G2 4.2/5 (n=38)
K2view	Synthetic data	Enterprises where synthetic data needs to feed live operational systems, not just offline training.	Enterprise pricing	G2 4.6/5 (n=55)
Hazy (SAS Data Maker)	Synthetic data	Regulated tabular workloads: banking, insurance, customer records.	Enterprise sales motion (SAS)	Not systematically checked for this guide

What do custom data sourcing providers actually do?

This is the category most buyers do not register as a distinct discipline. Custom sourcing partners acquire training data at the source: websites, documents, public records, niche corpora. They extract it into structured form, run quality assurance, and deliver it as model-ready datasets.

The work spans crawling infrastructure, document parsing, schema design, and continuous refresh. It is the right category when the AI program is constrained by the data it can access, not by the data it can label.

Forage AI

Best for	AI teams bottlenecked on getting to the data, not on labeling it
Key use cases	Web-scale and document-sourced training data, multimodal corpora, continuous-refresh pipelines
Pricing model	Scoped managed program (per-pipeline), not unit-priced per record
Licensing and data handling	No data reselling; client-specific handling rules agreed up front; on-prem or private-cloud deployment available; you own the data outright
Data types & coverage	Text, images, structured records, documents, multimodal, across language
Standout strength	Managed delivery into your warehouse on schedule; a 3x QA team and a 200% QA approach (automated checks plus human verification) on every delivery
Watch-out	Built for production-grade scope, not small-volume or short engagements
Public rating	No verified public reviews

Review attribute	Detail
What users like	Strong on hard-to-get data, the sources other vendors decline. Buyers describe the engagement as a partnership rather than a tool purchase, and the accuracy of what lands is the thing they name first. Everything is custom and done for you: the spec is yours, the pipeline, the monitoring and the fixing are ours.
What users flag	No self-serve entry point and no published price, so you cannot try it in an afternoon or budget it from a web page. Scoping work takes a conversation, which is slower to start than buying API credits. It is the wrong shape if you want to own and run the scrapers yourself, or if you need a dataset today rather than a feed built to your fields.

A note on these rows. Several vendors on this page have no usable public review corpus: either no profile on the review sites at all, or a profile with too few entries to read as a pattern, and G2 now keeps its review text behind a login. Rather than leave the rows empty, what they like and flag has been researched away from the review sites, from product and API documentation, independent and vendor benchmarks, press reporting, regulatory filings and practitioner write-ups, with public star ratings and review counts used where those are still visible. Where a claim comes from a competitor it is named as such. Nothing here is invented sentiment: where the only evidence is documented product behaviour, the row says what the behaviour is. Researched 26 October 2026.

Forage AI is where we operate, and we sit at the top of this category because the business was built around it. Forage AI is an end-to-end managed data pipeline: it automates discovery, extraction, structuring, quality checks and delivery, built to your own fields and systems. For AI teams, that means training datasets built to the model’s spec, across text, images, structured records, documents and multimodal sources, in any language. We sell no pre-built or off-the-shelf datasets; every dataset is built to order.

The acquisition layer is our Web Data Extraction service, the document side is our Intelligent Document Processing (IDP) pipeline, and both feed the same QA process: automated checks plus human verification.

Biggest strengths. Forage AI delivers the data, not just the pipeline: it lands in your warehouse on schedule, at agreed quality, without your team touching a scraper, IDP template, or agent prompt. 12+ years of operational history, 500M+ websites and 10M+ documents processed. Every Forage AI delivery passes a 3x QA team before it lands in your system, and the first dataset typically arrives 1-2 weeks after sign-off.

Contractual controls: no data reselling, client-specific handling rules for each modality agreed up front, and on-prem or private-cloud deployment available. You own the data outright.

Potential cons. Not built for small-volume or short-engagement work; the minimum scope is sized around production-grade pipelines. Higher upfront discovery cost than a per-record subscription, because the engagement is scoped to your specific fields, refresh cadence, and delivery format. If the gap is purely human labeling of an already-collected corpus, a labeling specialist is the better single-vendor fit, and we work alongside them rather than replacing them.

Bright Data

Best for	Buyers who want high-volume raw web feeds and tooling to manage acquisition themselves
Key use cases	Pre-built web datasets, self-serve web acquisition, large-scale proxy-based collection
Pricing model	Self-serve and subscription; catalogue datasets listed from $2.50 per 1,000 records
Licensing / compliance	Public compliance program; procurement sometimes reviews residential proxy supply chain
Data types & coverage	21B+ records across 1,000+ pre-built datasets (as listed, October 2026)
Standout strength	One of the largest proxy networks in the market
Watch-out	Self-serve model means your team still owns the pipeline
Public rating	G2 4.7/5 (n=352)

Review attribute	Detail
What users like	Customer support (48 mentions), ease of use (40), collection reliability (37)
What users flag	Cost for smaller teams (8 mentions on expense, 8 on pricing), documentation and debugging (8)

Bright Data now lists more than 1,000 pre-built datasets and 21 billion-plus records, priced from $2.50 per 1,000 records ($250 per 100,000) as of October 2026, plus a self-serve acquisition platform. They sit at the infrastructure end of the custom sourcing category: you bring the engineering, they bring the proxies, scrapers, and dataset feeds.

Biggest strengths. One of the largest proxy networks in the market, and a useful reference point if you are comparing the top proxies for AI data extraction. Hundreds of pre-built web datasets for common targets. A self-serve platform that scales fast for engineering-capable teams.

Potential cons. The delivery model leans self-serve, so the in-house team still owns the pipeline. Pricing scales fast at production volume. Procurement and legal sometimes flag the residential proxy supply chain.

Oxylabs

Best for	Teams with strong internal data engineering who want managed infrastructure
Key use cases	Large-scale web scraping, scraper APIs for high-friction targets, proxy management
Pricing model	Subscription and enterprise contracts with SLAs
Licensing / compliance	Strong enterprise compliance posture and SLA story
Data types & coverage	Web data at scale; 15,000+ clients claimed (October 2026)
Standout strength	Mature managed proxy infrastructure with scraper APIs out of the box
Watch-out	Infrastructure plus tooling, not a fully managed outcome
Public rating	G2 4.5/5 (n=424)

Review attribute	Detail
What users like	Easy setup (116 mentions), ease of use (114), privacy and security (114), integration speed (100)
What users flag	Pricing for small projects (89 mentions), unused requests on a fixed plan (56), mobile proxy reliability

Oxylabs says it is trusted by more than 15,000 clients and operates a web scraping platform and proxy infrastructure for large-scale data extraction, a profile we weigh against the rest of the field in our Oxylabs alternatives guide. The positioning is closest to Bright Data, infrastructure plus tooling, with a heavier emphasis on enterprise compliance and managed services.

Biggest strengths. Mature managed proxy infrastructure with a strong compliance posture. Scraper APIs cover common high-friction targets out of the box. A solid enterprise SLA story.

Potential cons. Like Bright Data, this sits closer to tools and infrastructure than to “you tell us what you need and we deliver it.” Strong internal data engineering is still required to turn the infrastructure into a training dataset.

Innodata

Best for	Buyers who want a public-company SLA and cross-category coverage
Key use cases	Data collection, supervised fine-tuning, red-teaming, annotation
Pricing model	Enterprise services contracts
Licensing / compliance	NASDAQ-listed (INOD); long history in regulated data environments
Data types & coverage	Cross-category: collection plus downstream labeling and alignment
Standout strength	Single vendor across multiple categories with a public-company SLA story
Watch-out	Pure-play custom sourcing depth varies by engagement; revenue is concentrated in a few customers
Public rating	No usable customer-side rating.

Review attribute	Detail
What users like	Subject-matter depth rather than crowd scale: over 4,000 domain experts, and coverage of the content types generalist vendors avoid, including legal data, financial reports and contracts. Annotation and collection are bundled, so you do not need a second vendor for sourcing.
What users flag	Independent analysts report finding no reviews for it on any B2B review platform, so nothing public verifies the capability claims. The buyer-side effort is real: clear instruction writing and reviewer criteria before scale, extra rounds to settle complex edge cases, and dataset formatting and validation often landing on you. Public filings also point to heavy revenue concentration in a few large clients, which is a continuity risk.

Innodata is a NASDAQ-listed (INOD) AI services company whose work spans data collection, supervised fine-tuning, red-teaming, and annotation. It reported 2025 revenue of $251.7 million, up 48% organically, and its own SEC filings show how concentrated that growth is: one customer was 58% of 2025 revenue. The breadth makes them a candidate for buyers who want a single vendor across multiple categories rather than stitching specialists together.

Biggest strengths. A public-company SLA story reassures procurement. Cross-category coverage: data collection plus downstream labeling and red-teaming. A long history of operating in regulated data environments.

Potential cons. The company is broad. Confirm the data-collection bench specifically when scoping, because pure-play custom sourcing depth varies by engagement. A larger company’s motion can mean slower onboarding than a specialist vendor’s.

Labeling and annotation services

Labeling vendors take data you already have and add structure to it. Bounding boxes on images, transcripts on audio, preference rankings on model outputs, span labels on text. RLHF, reinforcement learning from human feedback, is a labeling workflow, even though the deliverable is preference data rather than raw labels.

These data labeling companies fit best when the raw data is already collected and the bottleneck is human judgment at scale. They fit worst when the raw data does not exist yet.

Scale AI

Best for	High-stakes labeling at premium SLAs
Key use cases	Autonomous-vehicle labeling, defense, frontier LLM RLHF and evaluation
Pricing model	Premium enterprise contracts
Licensing / compliance	FedRAMP High authorization (September 2024)
Data types & coverage	Image, video, AV sensor data, text, preference data
Standout strength	Top-tier accuracy and SLA on high-stakes labeling
Watch-out	Price and contract motion can be heavy for smaller programs; ask how competitor data is separated after the Meta stake
Public rating	No usable customer rating. The G2 seller page shows 5.0 from n=1

Review attribute	Detail
What users like	Growth and exposure, on the employee side (Glassdoor 3.7, n≈424)
What users flag	Work-life balance, on the employee side. No customer-side corpus exists.

Meta took a non-voting minority stake in Scale AI in June 2025, reported at 49% for about $14.3 billion at a valuation near $29 billion. Founder Alexandr Wang moved to Meta, OpenAI said it was winding down its work with Scale, and Francis deSouza became CEO on 10 August 2026. Scale holds FedRAMP High authorization. Strongest in autonomous-vehicle labeling, defense, and frontier LLM workflows, including RLHF and evaluation. This is the premium tier of the labeling market.

Biggest strengths. FedRAMP High for federal workloads. A deep bench across frontier LLM evaluation, red-teaming, and RLHF.

Potential cons. The price tier tracks the SLA tier. Enterprise contract motion can be heavy for smaller programs. Less of a fit for low-volume, narrow-scope annotation work. After the Meta investment some frontier labs reportedly moved work elsewhere, so ask in writing how one customer’s data is kept from another.

Appen

Best for	Language-coverage-heavy projects spanning many dialects
Key use cases	Multilingual data collection and annotation, turnkey datasets, custom labeling
Pricing model	Project-based and enterprise contracts
Licensing / compliance	Long track record with global enterprise buyers
Data types & coverage	500+ global locales; text, audio, image, multimodal
Standout strength	Multilingual breadth, unmatched in the crowdsourced segment
Watch-out	Crowdsourced quality variance requires buyer-side QA investment
Public rating	G2 4.2/5 (n=35) and Capterra 4.1/5 (n=40), customer-side

Review attribute	Detail
What users like	Scale of language coverage and project management (G2, Capterra)
What users flag	Platform usability and turnaround consistency (G2, Capterra). Trustpilot 1.4/5 (n=366) is contributor-side, not buyers.

Appen runs one of the world’s largest multilingual crowdsourced workforces, publicly stating coverage of more than 500 global locales, and offers both turnkey datasets and custom annotation. The strongest fit when language and locale coverage drive the project. Google ended its contract in March 2024 (about US$82.8 million of 2023 revenue, per Appen’s ASX notice), and growth has since come from China: first-half FY26 revenue was US$119.9 million, up 17%.

Biggest strengths. Multilingual breadth is unmatched in the crowdsourced segment. Turnkey datasets sit alongside custom work. A long track record with global enterprise buyers.

Potential cons. The breadth model makes depth in any one vertical harder to validate. Crowdsourced quality variance requires meaningful QA investment on the buyer side. Annotator turnover in some lanes affects consistency.

Surge AI

Best for	Organizations training or aligning their own LLMs at scale
Key use cases	Preference rankings, RLHF, red-teaming, frontier-scale evaluation
Pricing model	Premium contracts benchmarked to frontier labs
Licensing / compliance	Production track record with the most demanding labs
Data types & coverage	Text and preference data for alignment
Standout strength	Frontier-lab-grade alignment data is the core competency
Watch-out	Fit thins outside the LLM alignment lane
Public rating	No public rating on any platform

Review attribute	Detail
What users like	The vendor labs moved to when they left Scale, which is the strongest available signal on output quality. Its own quality stack is specific: gold-standard accuracy, inter-annotator agreement and per-worker trust ratings on live dashboards.
What users flag	Zero public buyer evidence of any kind. Diligence has to come from references.

Surge AI is the largest of the alignment specialists by reported revenue: more than $1 billion in 2024, per Reuters and Bloomberg coverage of its July 2025 fundraising talks. It had never raised outside capital, and no close has been announced. The specialism is alignment data: preference rankings, RLHF, red-teaming, and evaluation at frontier scale. A focused product for organizations training or aligning their own LLMs.

Biggest strengths. Strong QA discipline on preference ranking and red-teaming. A production track record with the labs most demanding on quality.

Potential cons. Outside the LLM alignment lane, the fit thins. Pricing reflects the frontier-lab benchmark. Not a general-purpose annotation vendor for image, video, or AV labeling.

iMerit (EXL)

Best for	Projects where annotator qualification is the constraint
Key use cases	Healthcare, autonomous vehicles, geospatial annotation
Pricing model	Credentialed-annotator pricing
Licensing / compliance	Strong posture on regulated health-data work; auditable annotator-qualification chain
Data types & coverage	Image, medical imaging, geospatial, AV sensor data
Standout strength	Credentialed annotators (clinicians, radiologists, AV specialists)
Watch-out	Longer lead times; less suited to high-volume commodity labeling
Public rating	G2 4.7/5 (n=13). Small sample, directional only

Review attribute	Detail
What users like	Domain expertise and responsiveness of dedicated teams (G2)
What users flag	Onboarding time on complex taxonomies (G2)

iMerit specializes in regulated-domain annotation, healthcare, autonomous vehicles, geospatial, with credentialed domain specialists rather than a generalist crowd. The strongest fit when annotator qualification is the constraint. EXL (Nasdaq: EXLS) completed its acquisition of iMerit on 3 August 2026, so you now contract with a unit of a listed services company.

Biggest strengths. Credentialed annotators in regulated fields (clinicians, radiologists, AV specialists). A strong compliance posture on regulated health-data work. A documented annotator-qualification chain that auditors can verify.

Potential cons. Pricing reflects the credentialed model. Lead times run longer than the generalist crowd. Less suited to commodity bounding-box labeling at high volume. Ownership changed in August 2026, so confirm that pricing, staffing and data-handling terms carried over.

TELUS Digital

Best for	Global, multimodal labeling at enterprise scale
Key use cases	Multilingual annotation across text, image, audio, and video
Pricing model	Enterprise contracts
Licensing / compliance	Mature compliance posture and procurement story
Data types & coverage	500+ languages, all major modalities
Standout strength	Leader in Everest Group’s 2024 Data Annotation PEAK Matrix
Watch-out	Enterprise-shaped contracts and longer onboarding for smaller programs
Public rating	No customer-review footprint located

Review attribute	Detail
What users like	The only vendor here with repeat independent analyst recognition: a Leader in NelsonHall's 2026 NEAT evaluation for AI enablement services and in Everest Group's data-annotation assessment. Reach is genuine, with a contributor community over a million strong and AI-assisted labelling plus QC across text, image, audio and video.
What users flag	No usable buyer review corpus despite the analyst standing, so the day-to-day experience is undocumented. The one substantive caution in third-party material is that outcomes depend heavily on how clearly labelling targets and edge cases are specified at onboarding, which with a bench this large means your instructions are the quality control.

TELUS Digital (formerly TELUS International AI) publicly reports coverage across more than 500 languages and all major modalities, and was placed a Leader in Everest Group’s 2024 Data Annotation PEAK Matrix. Built for global, multimodal labeling at enterprise scale. TELUS took the unit fully private in October 2025, and in Q2 2026 booked a C$2.1 billion impairment on it, citing faster declines in legacy services as customers automate with AI.

Biggest strengths. Global language coverage at enterprise SLA. Strong on multimodal projects spanning text, image, audio, and video. A mature compliance posture and procurement story.

Potential cons. Enterprise contracts tend to be enterprise-shaped, which runs heavy for smaller programs. Onboarding cycles run longer than smaller specialist vendors. Ask how the parent’s restructuring affects the annotation team you would be contracting with.

Sama

Best for	Organizations that want a documented, auditable annotation chain
Key use cases	Computer-vision labeling, AV, geospatial, ethical-AI contexts
Pricing model	Reflects the ethical-sourcing premium
Licensing / compliance	Documented ethical-sourcing chain that holds up to procurement review
Data types & coverage	Image and video, regulated computer vision
Standout strength	Auditable annotation workflow with documented labor practices
Watch-out	Capacity varies by modality; lighter on text-only or audio-only work
Public rating	G2 4.6/5 (n=11).

Review attribute	Detail
What users like	Delivery consistency and account management (G2)
What users flag	Pricing flexibility on smaller volumes (G2)

Sama is best known for computer-vision labeling in regulated and ethical AI contexts. The ethical-sourcing positioning is a real differentiator for buyers whose procurement and legal teams scrutinize labor practices.

Biggest strengths. A documented ethical-sourcing chain that holds up to procurement review. Strong on computer-vision projects, including AV and geospatial. An auditable annotation workflow.

Potential cons. Capacity varies by modality, so confirm the specific lane before contracting. Less coverage on text-only or audio-only work than the multilingual leaders. Pricing reflects the ethical-sourcing premium.

Cogito Tech

Best for	Mid-tier multimodal labeling with mature compliance documentation
Key use cases	Multimodal annotation across image, video, text, and audio
Pricing model	Mid-tier relative to premium vendors
Licensing / compliance	Strong compliance documentation; reference customers in regulated industries
Data types & coverage	Image, video, text, audio
Standout strength	Quality and compliance without the frontier-lab price tier
Watch-out	Less frontier-LLM alignment specialism; smaller public footprint
Public rating	A G2 profile shows about 4.7, but the sample resolved as 12 in one view and 14 in another

Review attribute	Detail
What users like	The clearest client feedback of any labelling vendor here: 4.7 on G2 across 14 reviews and a substantial Clutch base where over 90% of clients commend timely delivery, annotation quality and project management. Responsiveness and competitive pricing are named repeatedly, round-the-clock coverage is a stated strength, and one vehicle-damage project reported over 90% annotation accuracy.
What users flag	Consistency is the named weakness: clients report occasional drops in labelling quality that point to room for tighter quality control. The reviews come mostly from small and mid-sized clients, so they say little about enterprise-scale programmes. Build an acceptance gate per batch rather than trusting the average.

Cogito Tech operates in the multimodal labeling space, with strong compliance documentation, and displays logos including AWS, Medtronic, and Siemens on its site. A mid-tier option for buyers who want quality and compliance without the frontier-lab price tier.

Biggest strengths. Solid mid-tier pricing relative to the premium vendors. Multimodal coverage across image, video, text, and audio. Reference customers across regulated industries.

Potential cons. Less frontier-LLM alignment specialism than Surge or Scale. Capacity in any one modality varies, so scope the specific lane carefully. A smaller public footprint than the leaders, which makes procurement reference checks take longer.

When does a dataset marketplace make sense?

Marketplaces license pre-built datasets you can buy, download, and use. The corpus is standard. The license terms are pre-negotiated. The speed-to-value is fast. Our guide to the best AI dataset marketplaces compares eight of these platforms side by side.

Marketplaces fit best when the training task has a well-known shape, sentiment, common-object detection, or general-purpose dialogue, and the available data is sufficient. A free tier of open training data for ecommerce models and similar domain corpora is often worth checking before you buy. They fit worse when you need a dataset shaped exactly to your model’s domain, language, or schema.

Datarade

Best for	Early-stage scoping when you need to see what is out there
Key use cases	Browsing and comparing third-party data products across providers
Pricing model	Varies by underlying provider
Licensing / compliance	License terms vary from catalog item to catalog item
Data types & coverage	Wide catalog spanning many provider types and modalities
Standout strength	Standardized browse-and-compare flow across vendors
Watch-out	An aggregator, not a partner for ongoing engagement
Public rating	G2 4.5/5 (n=21). Small sample

Review attribute	Detail
What users like	No pros-and-cons summary published at this review volume.
What users flag	No pros-and-cons summary published at this review volume.

Datarade is a meta-marketplace that aggregates third-party data products, including AI training datasets, from many independent providers, more than 2,700 by its own count. A natural starting point when you are still scoping what is available in the market.

Biggest strengths. A wide catalog spanning many provider types and modalities. Useful for early-stage scoping when you need to map the landscape. A standardized browse-and-compare flow across vendors.

Potential cons. As an aggregator, quality consistency depends on each underlying provider. Not a partner for ongoing engagement; it is a discovery surface.

Shaip

Best for	Medical AI teams needing licensed clinical datasets plus annotations
Key use cases	Healthcare AI data catalog combined with annotation services
Pricing model	Catalog licensing plus annotation engagements
Licensing / compliance	Healthcare-appropriate licensing
Data types & coverage	Medical NLP and imaging; clinical datasets
Standout strength	Combined catalog-plus-annotation model reduces vendor count
Watch-out	Outside healthcare, the catalog narrows substantially
Public rating	Not systematically checked for this guide

Review attribute	Detail
What users like	Not systematically checked for this guide.
What users flag	Not systematically checked for this guide.

Shaip runs a healthcare-oriented AI data catalog alongside annotation services. The dual model fits medical AI teams that want both licensed clinical datasets and matching annotations in a single engagement. Ubiquity agreed to acquire Shaip in February 2026, so check which entity you will be contracting with.

Biggest strengths. A healthcare-specific catalog with appropriate licensing. The combined catalog-plus-annotation model reduces vendor count. Domain depth in medical NLP and imaging.

Potential cons. The domain specialism means less fit for general-purpose training programs.

Defined.ai

Best for	Voice-AI and conversational model teams
Key use cases	Speech, conversational, and multimodal dataset licensing; custom collection
Pricing model	Catalog licensing plus custom collection
Licensing / compliance	Mature licensing terms for audio corpora
Data types & coverage	Speech, conversational, multimodal
Standout strength	Deep specialism in speech and conversational data
Watch-out	Coverage uneven for niche languages and domains outside speech
Public rating	Not systematically checked for this guide

Review attribute	Detail
What users like	Not systematically checked for this guide.
What users flag	Not systematically checked for this guide.

Defined.ai maintains a marketplace of speech, conversational, and multimodal datasets and says it holds more than two million hours of speech across 500-plus languages and dialects. The strongest fit when voice and conversational corpora are the bottleneck.

Biggest strengths. Custom collection sits alongside catalog purchases.

Potential cons. Corpus availability for niche languages or domains is uneven. Less coverage outside speech and conversational AI than general-purpose marketplaces.

The licensing market behind these catalogs is consolidating fast. More than 20 publishers, among them Axios, The Atlantic, Condé Nast, Hearst, News Corp, and Vox Media, have signed content-licensing deals with OpenAI, per public reporting. The implication for marketplace buyers: a pre-built corpus with documented, pre-negotiated licensing is becoming the defensible option, while undocumented scraped corpora accumulate legal risk that surfaces later in the model’s life.

Source: public reporting on OpenAI publisher licensing agreements (2024–2025).

Synthetic data generators

Synthetic vendors generate artificial training data that mirrors the statistical properties of real data. The use cases are privacy-safe augmentation, rare-event coverage, and bridging gaps where real data is legally or operationally difficult to collect, a response in part to the looming data-scarcity crisis facing model builders.

Synthetic data fits best when you have a real dataset to anchor on and need either more of it or a version your compliance team can sign off on. It fits worse when used alone, because synthetic-only training programs tend to drift.

Gretel (NVIDIA)

Best for	Teams already standardized on NVIDIA’s stack
Key use cases	Privacy-preserving synthetic structured and text data
Pricing model	Platform-based, within the NVIDIA NeMo ecosystem
Licensing / compliance	Differential privacy and synthetic-record primitives
Data types & coverage	Structured and text synthetic data
Standout strength	Tight integration with NVIDIA’s NeMo and GPU stack
Watch-out	Optimized for NVIDIA-standardized teams; still needs anchor real data
Public rating	G2 4.4/5 (n=13). Small sample

Review attribute	Detail
What users like	No pros-and-cons summary published at this review volume.
What users flag	No pros-and-cons summary published at this review volume.

NVIDIA reportedly acquired Gretel in March 2025 and integrated it into the NeMo ecosystem. The platform generates synthetic structured and text data, with a strong privacy-preservation story.

Biggest strengths. Mature privacy-preservation primitives (differential privacy, synthetic record generation). Backed by NVIDIA’s roadmap and resources.

Potential cons. Optimized for teams already standardized on NVIDIA. Synthetic-only programs still need real data to anchor, and Gretel does not solve that on its own. The post-acquisition product roadmap is still settling.

MOSTLY AI

Best for	Regulated industries needing synthetic versions of production records
Key use cases	Tabular synthetic data for banking and insurance
Pricing model	Enterprise pricing
Licensing / compliance	Mature privacy compliance posture
Data types & coverage	Tabular synthetic data
Standout strength	Deep specialism in tabular synthetic data with production deployments
Watch-out	Less fit for image, text, or audio; still needs anchor real data
Public rating	G2 4.5/5 (n=17), listed as MOSTLY AI Synthetic Data Platform

Review attribute	Detail
What users like	No pros-and-cons summary published at this review volume.
What users flag	No pros-and-cons summary published at this review volume.

MOSTLY AI focuses on enterprise synthetic data for tabular datasets, with a privacy-first generator workflow. A strong fit for regulated industries needing synthetic versions of production records. It open-sourced its Synthetic Data SDK in January 2025, so a team can trial the generator before an enterprise contract.

Biggest strengths. A mature privacy compliance posture. Production-grade enterprise deployments in banking and insurance.

Potential cons. The tabular focus means less fit for synthetic generation of images, text, or audio. An enterprise pricing motion. Still requires anchor real data to train the generator.

Tonic.ai

Best for	Teams where speed and developer experience are the priority
Key use cases	Synthetic data for software testing and AI development augmentation
Pricing model	Platform subscription
Licensing / compliance	Standard data-privacy controls
Data types & coverage	Realistic synthetic data for test and training pipelines
Standout strength	Strong developer experience; fast to integrate into CI/CD and ML pipelines
Watch-out	Less specialized in privacy primitives; anchor real data still required
Public rating	G2 4.2/5 (n=38)

Review attribute	Detail
What users like	No pros-and-cons summary published at this review volume.
What users flag	No pros-and-cons summary published at this review volume.

Tonic.ai generates realistic synthetic data for software testing and AI development. The product emphasis is on developer experience and pipeline speed.

Biggest strengths. Strong developer experience and tooling. Fast to integrate into existing CI/CD and ML pipelines. Useful for both test-data generation and AI training augmentation.

Potential cons. Less specialized in privacy primitives than MOSTLY AI or Gretel. Synthetic-only is still not a complete training program, so anchor real data is required.

K2view

Best for	Enterprises where synthetic data feeds live operational systems
Key use cases	Production-scale synthetic data with live-system integration
Pricing model	Enterprise pricing
Licensing / compliance	Enterprise integrations into existing operational stacks
Data types & coverage	Complex relational data via entity-based architecture
Standout strength	Operational deployment posture, including live-system feeds
Watch-out	Heavier integration motion than pure-play training generators
Public rating	G2 4.6/5 (n=55)

Review attribute	Detail
What users like	Data management across systems (3 mentions), data sharing (3), ease of access (3)
What users flag	Setup complexity and the technical background it demands (3 mentions each on complexity, setup and technical requirement)

K2view competes in the production-scale synthetic data space, with an emphasis on operational deployment and live-system integration. Suited to use cases where synthetic data feeds production rather than training in isolation.

Biggest strengths. An operational deployment posture, including live-system feeds. An entity-based architecture that handles complex relational data.

Potential cons. A heavier integration motion than pure-play training data generators. Less of a fit if synthetic data only needs to feed offline training, not live systems. Enterprise pricing.

Hazy (SAS Data Maker)

Best for	Regulated tabular workloads: banking, insurance, customer records
Key use cases	Synthetic versions of customer or transaction records
Pricing model	Enterprise sales motion (SAS)
Licensing / compliance	Privacy primitives that hold up to regulator scrutiny
Data types & coverage	Tabular enterprise data
Standout strength	Strong tabular synthetic generation for financial and regulated workloads
Watch-out	Tabular focus; less coverage of image, text, or audio
Public rating	Not systematically checked for this guide

Review attribute	Detail
What users like	Not systematically checked for this guide.
What users flag	Not systematically checked for this guide.

SAS acquired Hazy’s principal software assets in November 2024 and folded them into SAS Data Maker. The product focuses on tabular enterprise data, with emphasis on regulated industries that need synthetic versions of customer or transaction records. Closer in posture to MOSTLY AI than to the developer-focused tools.

Biggest strengths. An enterprise procurement track record.

Potential cons. A tabular focus, similar to MOSTLY AI, with less coverage of image, text, or audio modalities. The enterprise sales motion runs heavier than developer-tools competitors.

Synthetic vendors return the most when paired with one of the other three categories. Anchor on real data, then augment with synthetic where the math says it helps.

How to evaluate an AI training data provider: the seven-axis framework

With the shortlist in hand, run a real evaluation. These are the seven axes that separate viable shortlists from category-confused ones.

1. Modality. Text, image, audio, video, structured tabular, multimodal, document. Most vendors are honest about which modalities they handle deeply. Make them prove it with samples.

2. Scale. Hundreds of examples or hundreds of millions? Static delivery or continuous refresh? Most vendors scale up cleanly only inside their core category.

3. IP cleanliness. Where does the data come from, who licensed it, and can the vendor show the chain? With the New York Times and OpenAI now cross-moving for summary judgment, and Getty appealing its UK loss to Stability AI, provenance is no longer a nice-to-have.

4. Freshness. A snapshot from 2023 will not train a model that needs to know what happened last quarter. Ask explicitly: how often does this data refresh, and what is the SLA?

5. Customisation depth. Can the schema bend to your model’s needs, or do you bend to theirs? Standardised schemas are fast; bespoke schemas are slower but more useful for differentiated AI products.

6. Compliance posture. SOC 2 Type II, ISO 27001, US health-privacy rules for medical data, GDPR/CCPA for personal data, FedRAMP for federal. Ask for documentation, not a marketing line.

7. Managed vs DIY. Does the vendor run the pipeline for you, or hand you a tool and a login? Both are valid. They carry very different operational costs over a three-year horizon.

A practical decision-criteria table, your axes scored against your three or four finalists, is worth more than any vendor’s pitch deck. Build it before you take a sales call.

The seven axes track a broader shift in where model performance is won. As Andrew Ng put it in framing the data-centric AI movement, “the model and the code are basically a solved problem,” and the remaining gain is in systematically engineering data quality. Two of the seven axes (modality fit and customization depth) operationalize exactly that: they test whether the dataset bends to the model’s needs rather than the reverse.

Source: Andrew Ng, founder of DeepLearning.AI and Landing AI, on the data-centric AI approach (Scale Events / IEEE Spectrum, 2022).

IP cleanliness and provenance: the evaluation axis most listicles skip

This article is for informational purposes only and does not constitute legal advice. Consult a qualified attorney for legal guidance specific to your situation.

Most “AI training data providers” articles in 2026 still treat IP cleanliness as a sentence in the conclusion. The legal record of the past 24 months says it belongs near the top of every RFP.

The New York Times’ copyright case against OpenAI and Microsoft, filed in late 2023, alleges that millions of Times articles were used to train ChatGPT without a license. The Times is seeking statutory damages publicly described as in the billions. Both sides filed cross-motions for summary judgment on 4 September 2026, briefing runs to 20 November, and no trial date is set.

Getty Images’ case against Stability AI produced a UK High Court judgment on 4 November 2025 that rejected the secondary copyright claim over Stable Diffusion’s model weights and found only limited trademark infringement over reproduced watermarks. The training question itself was never decided: Getty dropped it for lack of evidence that training took place in the UK, and it was given permission to appeal in December 2025.

Alongside the litigation, more than 20 publishers, including Axios, The Atlantic, Condé Nast, Hearst, News Corp, and Vox Media, have signed licensing deals with OpenAI. A paid-licensing market is forming alongside the lawsuits.

The takeaway for any 2026 RFP: the provenance chain matters. Ask vendors three questions in writing.

• Where did this data originate, and what is the license under which you acquired the right to use it?
• What contractual protections do I have if a third party claims rights to the underlying source?
• Do you resell the data you collect for me to any other party, and if not, will that no-resell commitment appear in the contract?

A vendor that cannot produce documented answers to those three questions does not belong on the shortlist, no matter how strong the rest of the pitch is. For a deeper look at the public-versus-private sourcing distinction in this context, see our public web data vs. private data in the AI training primer, and for the compliance angle specifically, our guide on solving the AI training data crisis with compliant web scraping.

How do you buy AI training data?

Most teams that set out to buy AI training data expect a price list. There usually is not one. Outside a few self-serve catalogues, training data is priced by negotiation, and the price depends on what you are licensing as much as on how much of it you take.

In practice there are four ways to buy, and each one prices, licenses and fails differently.

Route	What you get	How it is priced	What to check
Catalogue licence (marketplace)	A ready-made dataset under the platform’s standard terms	Per record, per file or per subscription; one large web-data catalogue lists from $2.50 per 1,000 records	Whether the licence names model training at all; defaults differ by platform
Custom collection (managed sourcing or labeling)	Data built to your spec, often on a refresh schedule	Scoped per program or per pipeline, not per record	Who owns the output, the refresh SLA, and the acceptance test
Direct licence from a rights holder	An archive: news, books, images, video or forum content	Flat fee or recurring subscription; per title, per image or per minute	Exclusivity, term, and what you must delete at exit
Open datasets	Public corpora on hubs such as Hugging Face (about 1.1 million public datasets, October 2026)	Free to download	Licence labels on hosting sites are often wrong; read the source licence, not the card

The public price points are few, dated and mostly reported rather than published. They still show the units you will be quoted in. Photobucket’s CEO told Reuters in April 2024 he had discussed 5 cents to $1 per photo and more than $1 per video. Creators selling unpublished footage were reported at $1 to $4 per minute in January 2025. HarperCollins offered authors $5,000 per nonfiction title, split with the publisher, in a 2024 AI deal.

At the top end, News Corp’s deal with OpenAI was reported at about $250 million over five years, and Google’s with Reddit at about $60 million a year. Academic corpora are the exception that publishes prices: the Linguistic Data Consortium charges a $34,000 for-profit membership. Across all of it, the deals are moving from one-time archive sales to recurring subscriptions, which turns training data into an operating cost rather than a one-off purchase.

The other price on the record is what it costs not to license. In July 2026 a federal court gave final approval to Anthropic’s $1.5 billion settlement with book authors, roughly $3,000 per work. That is a settlement, not a market rate, but it is the number legal teams now bring to the buying conversation.

The detail that decides most deals sits in the licence. A licence to use the data, ownership of the data and permission to train a model on it are three separate rights, and standard terms split them differently. One large image library’s standard licence forbids machine-learning use unless separately authorised. One AI-data marketplace’s standard licence permits commercial use of models trained on the data, but not of the data itself, which it supplies “as is”. A data-cloud marketplace’s default consumer terms bar derivative works unless the listing says otherwise. None of these is unusual. They are just different, so read the clause rather than the badge.

If you place a general-purpose model on the EU market, the paperwork also has to survive disclosure. Providers must publish a training-content summary on the European Commission’s public summary template for training content, an obligation that has applied since 2 August 2025.

Before the first sales call, send five questions in writing:

• Which route is this (catalogue, custom, direct licence or open data), and who is the licensor of record?
• Does the licence name model training, fine-tuning and evaluation, and does it cover models we ship commercially?
• Who owns the delivered data and anything derived from it, and what must we delete at the end of the term?
• What is the unit of price (record, hour, title or pipeline), and what triggers a re-price: volume, refresh or exclusivity?
• Can you send a sample with the source and collection date for every record, so we can test it against our own spec?

If the gap is a standard corpus, our guide on where to buy ready-made AI datasets compares the marketplace route platform by platform. If the gap is data built to your own fields and refresh schedule, go back to the custom sourcing category above.

Should you build, buy, or hand it to a managed team?

Most internal conversations frame this as a two-option decision: build the data acquisition team in-house, or buy datasets off the shelf. Both options carry real costs that show up later.

Build gives you control, but extraction pipelines break when sources change, anti-bot measures evolve, and document layouts shift. The maintenance load compounds: every new source adds upkeep, so the engineers who built the first pipelines spend more of each quarter keeping them alive and still fall behind on refresh demand. The scale wall is real.

Buy gives you speed, but the dataset is shaped to someone else’s spec. Your model ends up trained on what was available, not on what was right.

The third option is managed: a partner runs the acquisition, extraction, and QA pipeline; you define the data and consume it. This is the category Forage AI sits in. It is not “we sell you a tool”; it is “we run the pipeline as an extension of your data team, so your team stays focused on the model.” For a deeper build-versus-buy framework adjacent to this decision, see our strategic guide to web data extraction build vs buy, and once you have chosen a partner, the operational playbook for AI training data covering freshness, schema versioning, and vendor handoff.

The build-vs-buy math turns on where the durable advantage sits. Andrew Ng’s data-centric thesis holds that performance gains now come from systematically improving the data rather than the model, which reframes the in-house build question: the maintenance load on extraction pipelines is recurring engineering cost spent on infrastructure, not on the data quality that actually moves the model. A managed pipeline isolates the team’s effort to the part that compounds, freeing engineers to focus on the practical reality of intelligent automation the data is meant to power rather than the plumbing beneath it.

Source: Andrew Ng, founder of DeepLearning.AI and Landing AI, on the data-centric AI approach (Scale Events / IEEE Spectrum, 2022).

FAQ

What is the difference between an AI training data provider and a data labeling company?

A labeling company adds structure (labels, annotations, preferences) to data you already have. An AI training data provider in the broadest sense covers everything from sourcing to labeling, but the strongest ones specialize in one of the four categories described above. Pick the category that matches your gap.

How much does AI training data cost?

There is rarely a public price list. Catalogue data is sold per record (one large web-data catalogue lists from $2.50 per 1,000 records), licensed media has been reported at 5 cents to $1 per photo and $1 to $4 per minute of video, and one publisher’s book deal paid $5,000 per title. Custom sourcing and extraction is typically scoped as a managed program, not unit-priced, because the value sits in the pipeline rather than the line item.

Where can you buy AI training data?

From four places: dataset marketplaces and catalogues, custom collection partners, rights holders who license their archives directly, and open-data hubs. Which one fits depends on whether you need a standard corpus or data built to your own spec, and the buying section above compares the four.

What is the difference between custom data sourcing and synthetic data?

Custom sourcing acquires real data from real sources (web, documents, public records) and structures it. Synthetic data generates artificial data that mirrors a real distribution. They solve different problems; many programs use both.

How do I evaluate IP cleanliness in a vendor?

Ask for the license chain in writing, ask whether the vendor resells data collected on your behalf, and ask what indemnification you get if a third party claims rights. A vendor that hedges on any of the three should not advance.

Can one vendor cover all my modalities?

Sometimes. Multimodal-capable vendors exist in every category, but depth varies. The honest answer is to confirm modality by modality during scoping rather than trust the marketing claim.

Conclusion

The four-category map is the most useful tool you can carry into a 2026 AI training data evaluation. Anchor on the gap, pick the category, run the seven-axis framework against a real shortlist, put IP cleanliness near the top of the RFP rather than the bottom, and settle the buying route and the training clause before you negotiate the price. If your gap is custom, web-scale, multimodal training data delivered as a managed pipeline, we built Forage AI for exactly that conversation.

Related Articles

• Quality Over Quantity in AI Training Data – why curation beats raw volume.
• A Guide to Modern Data Extraction Services in 2026 – the broader data extraction services guide covering the full landscape.
• Data Extraction Automation: The Complete 2026 Guide – the pillar overview on data extraction automation across the stack.
• Automated Data Collection pillar title – the pillar overview on automated data collection.