A team pricing a data extraction project would quickly notice that the quotes from all vendors are not necessarily close. A low-cost vendor, a ready-made dataset, an in-house team trying to build on modern tooling, and a managed partner can sit far apart on the price.
What accounts for the difference comes down to what each quote is really measuring. And depending on the scope of their offerings and capabilities, they often measure different things. The number that makes a like-for-like comparison possible isn't price per record; it's price per usable record, and it's the one you can calculate for yourself.
A look at the complete picture
Almost every quote gets compared on "price per record". That's the number on the pricing page and in the spreadsheet, so it's the number people anchor to. But price per record only measures the cost of an attempt. It says nothing about how much of what arrives is actually usable. And "usable" is really two questions, not one.
The first part is recall: Meaning, did the crawler find the record at all? When a crawler can't locate a company's website, it returns a blank, a futile attempt, a recall issue. The second is accuracy: Of the records it did return, how many are correct? A vendor can be perfectly accurate on the 85% of records it found and still leave you with a 15% hole. These are different failures. Missing data isn't the same as wrong data, but ultimately both land in the same equation:
Here's why splitting the figure matters. A vendor that finds 85% of records and is 85% accurate on the ones it found, and each number sounds fine in isolation. But 0.85 × 0.85 lands at roughly 72% usable data. Two reasonable-sounding numbers multiply into one uncomfortable one. A higher-end vendor running around 96% on each lands at about 92%. That's the whole game, and it only gets sharper once your project complexity and scale increase.
Why small gaps compound into large ones
A single record is rarely a single value. A company profile might carry a name, a verified address, a website, an ownership flag, and a set of executives. Each is often extracted and checked from a different source, and accuracy rate multiplies across those fields. A record is only fully correct if every field is correct.
That's where a difference that looks minor per field, 85% vs 96%, stops looking so minor. Watch what happens as the number of fields grows:
Each stage that runs at 85% instead of 96% further reduces the final result. Multi-field, multi-step data extraction is precisely where working with an expert with domain knowledge, state-of-the-art technology, and years of experience becomes a competitive advantage.
Price per record is only part of the cost
The figures below are illustrative, based on patterns we see across long-tail extraction projects rather than on any one client's invoice.
Run two vendors through that formula side by side, and the "expensive" one often stops looking expensive at all.
| Metric | Low-accuracy vendor | High-accuracy vendor |
|---|---|---|
| Quoted price | $90,000 | $115,000 |
| Records in scope | 50,000 | 50,000 |
| Fields per record | 1 | 1 |
| Recall × accuracy (per field) | 85% × 85% | 96% × 96% |
| Usable rate | ~72% | ~92% |
| Usable records | 36,125 | 46,080 |
| True cost per usable record | $2.50 | $2.50 |
A dead heat; but cost per usable record is only the first factor, not the whole equation. On a single field, the higher price actually ends up costing the same because it gives you more usable records you can act on immediately. But that is rarely the case. Add fields, and each one multiplies its accuracy against the rest: the low-accuracy vendor's usable rate slides from 72% into the 50s and lower as fields stack up, while the high-accuracy vendor holds in the 80s. At three fields, the low-accuracy vendor ends up costing $3.45 per usable record, against $2.71 for the high-accuracy one. The "cheaper" option becomes the more expensive one, and it happens before a single hour of cleanup is counted.
What a wrong match costs once it's already in your dataset
Here's where things get even trickier. A record that's wrong doesn't announce or highlight itself. It sits in your spreadsheet looking exactly like a correct one. Verifying thousands of records in a single hour of cleanup isn't easy. You'll review a slice of it, trust the rest, and ship it. Whatever you didn't catch, your customer will, and every error they find is a withdrawal from the trust they placed in you.
Ask yourself this: How important is dependable, accurate data for your business?
For a company building products and analytics on data, every incorrect record is an opportunity for customers to paste the query into ChatGPT or another LLM. If a $20 subscription surfaces what your feed couldn't, it doesn't take long for the customer to churn.
Do the math on your own numbers
None of the figures above are meant to be taken on faith. The point of the model is that it's yours to run. Plug in the quotes you're weighing, your own accuracy assumptions, what your team costs, and what a lost customer is worth, and see where the true total actually lands.
Every data extraction setup is different, and this calculator is only a high-level guide. But the relative weight of each component is the signal worth reading. If churn from poor data is your biggest line, the case for investing in quality is already made. If internal cleanup and processing costs dominate, offloading that work is your unlock. Whichever number is largest tells you where to act first.
Cost multipliers
Not every record costs the same to get right. The base price assumes a reasonably cooperative target: a business with a website, in a language your tools read, on a website that doesn't actively fight back. The moment those assumptions break, effort per record climbs, and a few factors tend to stack together rather than appear alone.
What the premium actually buys
Managed services like Forage AI actually account for all the above complexities. You're not paying per request, you're paying for clean, accurate data. In fact, when we build a quote at Forage AI, "extraction" is just one part of it. The price covers the full system required to turn a target list into data you can act on without re-checking:
High-quality data is the biggest factor that can make or break a data project, not the sticker price. At Forage AI, we ensure your data undergoes multiple rounds of testing and checks to ensure it is ready for ingestion. While most vendors keep QA as a lean support function to control costs, we make it a competitive advantage and invest in it well beyond the norm because market-leading data is built on market-leading processes.
How we can ensure 95%+ accuracy
Most vendors simply claim 99.99% accuracy without any evidence or transparency into how. Forage AI was founded with the premise that the highest-quality data yields extraordinary results - it's not easy, but it creates winners in every industry. It's why global financial data providers, market intelligence platforms, and healthcare data companies come to us to automate and overhaul their data collection processes so they can win big in the AI era.
Here's a simplified version of how Forage AI upholds the claim.
Layer 1: Automated, rule-based checks
Runs on everything, at scale, for near-zero marginal effort. Does the schema hold? Did a required field go blank? Is the registration number valid for its country, is the domain URL live, and is the HQ city in the country on record? It's built to catch the hundreds of error types we've seen before: the ones with a recognizable digital scent that a rule can flag the moment they appear.
Layer 2: AI confidence scoring
This is where a record earns its score rather than being taken at face value. We make sure the signal itself can be trusted, and that a near-match is really a match. That takes judgment, and a level of deep-dive most vendors won't take the trouble to do. If a website is down, is it gone or just temporarily unreachable? If a company has two social media pages, is one auto-generated and the other actually run by the business? If there's been no update or change in years, does the business still exist? When a company name almost matches, and the address is close by, is that the same company or a coincidence? Every value is checked from multiple angles. We render each website as a real user would, across desktop, mobile, and headless browsers. The same page can serve different content on different devices, and a value that's right on one and wrong on another is exactly the kind of error a single-pass scraper never notices. We pull data from multiple webpages and independent sources, then compare them for confirmation; when sources disagree, a dedicated set of checks investigates why, weighing each piece of evidence before a score is assigned. So a confidence score isn't a number lifted from a single source; it's a verdict from cross-checking every version of the fact we can find, so you can act on the data with confidence.
Layer 3: Human review
This is for records that clear the first two layers as uncertain. This is a resource-intensive step, which is exactly why the first two exist: they keep human judgment for the records that truly need it, so the project stays on budget and on schedule.
Layer 4: Sample tests
We take a random sample of records that passed all automated checks and review them to reconfirm what the system already cleared and to identify any hidden errors it may have missed. This process helps our engineers continually improve our systems by turning that single miss into new tests and logic, so the same issue is automatically caught the next time it occurs. In global firmographic data, this means analysts routing through VPNs across multiple countries, translating legal-entity suffixes from unfamiliar languages, checking historical web archives to confirm the website of a now-defunct company, and cross-referencing map pins with social pages when nothing else can verify a match. That is the work that goes behind our 95%+ accuracy figure - earned record by record on the hardest data we've handled.
Layer 5: The feedback loop
This layer ties the other four together. Every correction made anywhere in the pipeline is logged by error type, source, and root cause, and those logs are what our engineers use to retrain the models and tighten the rules. The result is that error rates decline rapidly over the life of long-running projects rather than remaining flat or worsening.
"Forage AI's pipelines have run reliably for us long enough that we stopped checking on them. They're an extension of our data team, not a vendor we manage." - Client, Healthcare industry
These processes, formed by years of experience, are also why our delivery time doesn't balloon as projects scale from a dozen sources to hundreds across languages and geographies. Extracting and QA-ing 300 websites the way you'd review 12 doesn't work. Instead, our automation takes on more of the work as volume increases. This frees our experts to focus only on the data points that truly need human judgment.
The people behind the operation
None of this works without the people running it and the deep familiarity they have with the data. This familiarity is something we deliberately build at Forage AI. Before a single record is extracted, the team works to understand why the data is being collected and what decision it will feed, an understanding that comes from working closely with each client's team, not at arm's length from it. Knowing the purpose changes what "correct" means for every field. The ability to generate that context and judge which data is accurate is a skill we cultivate in how we run every engagement, and those instincts sharpen with each one.
Our team of experienced researchers and analysts maps the domain: how the industry actually operates, where its information really lives, and how scattered and inconsistent those sources tend to be. Every analyst works not just as a data processor but as a domain specialist, trained to question a value rather than accept it, and to catch the single inconsistent entry sitting among thousands of correct ones: the needle in the haystack that a faster, shallower process slides straight past. Pattern recognition is the muscle we build most: when someone finds a rare error, they don't simply fix it and move on. They trace its cause, document the pattern, and feed it back so the whole team and our automation get better at catching it the next time.
Our company values are woven into every project we take on. Precision, curiosity, and a refusal to deliver data unexamined aren't just slogans on a wall here; they're the habits every task is structured to reinforce, which is why our people are genuinely among the best at this kind of work.
The result is a team that works less like reviewers clearing a queue and more like investigators who take pride in finding what others may miss. That earned, domain-deep instinct is the human quality behind every record we deliver.
Gartner's Top Trends in Data and Analytics for 2026 report names why a wrong match matters more than it used to: enterprises are moving toward "agentic D&A," where AI agents act directly on data with far less human review in the loop. Gartner's own guidance is direct about the risk: trusting automated, ungoverned decisions is a fast track to failure, and it calls for accountability frameworks that keep AI-driven decisions transparent and auditable. An agent acting on a wrong match doesn't just produce a bad report anymore. It takes a bad action. That's a more expensive kind of mistake than it used to be, and it's exactly the gap layered QA is built to close.
Time to reflect
A lower quote from an API vendor or no-code tool isn't dishonest. They're just pricing for something different - the extraction attempt, not the verified data. Once accuracy, remediation, and the risk of doing it twice are counted, the number that looked high on the spreadsheet is usually the lower one. And that's before the stress on your own team - people stuck cleaning data that was supposed to arrive clean. Or worse, you ship it believing it's clean, and then your customers start complaining about wrong records and thin coverage. It isn't your fault, but it becomes your problem to fix - before the revenue walks away. So don't bet on a vendor for their lower price. Bet on data your customers trust, and a pipeline your business can grow on.