Selling Data to AI Companies: What Sells, What It's Worth, and Where to Sell It (2026)
Sort your data into six buckets, see what AI companies pay for in 2026, and match commodity corpora, eval sets, agent workflows, and production code to the right channel.
TL;DR
- AI companies in 2026 are looking for verifiable data from real decisions rather than raw volume. The era of bulk-corpus data is fading, and the money is moving toward evals, agent workflows, and code-adjacent context.
- OpenAI's own Data Partnerships program asks for datasets that reflect "human intention" and explicitly rejects PII and third-party data, which tells you scraped dumps are worth less than clean, structured, provenance-clear assets ().
- Commodity data (scraped text, generic media, internal docs) sells through brokers and marketplaces like Opendatabay, Defined.ai, and FileYield.
- Eval sets, agent trajectories, and code-adjacent context are DataVendor's lane. Route those there.
What counts as "data" AI companies will pay for
When you decide to sell data to AI companies, the first job is knowing which bucket your data falls into, because each one sells through a different channel and commands a different price. This piece uses six buckets as its lens.
- Static or proprietary datasets: structured collections you own, from tabular records to labeled media.
- Web or scraped corpora: bulk text and media pulled from public sources.
- Slack, email, and internal docs: unstructured communication and knowledge.
- Eval datasets: test sets that measure model behavior against known answers.
- Agent trajectories and workflows: recorded traces of an agent completing real tasks.
- Code and code-adjacent context: production codebases plus the tests, configs, and history around them.
Sort your data into one of these before reading further.
Data type comparison at a glance
The six data types below sort into a clear split. Static corpora and scraped text sell as commodities. Eval sets, agent trajectories, and code-adjacent context command more because a buyer can verify what they're getting.
| Data type | What it is | Who buys it | Value tier | Where to sell | Rights/consent burden |
|---|---|---|---|---|---|
| Static/proprietary datasets | Structured collections you own (tabular, audio, video, images) | Labs via , marketplaces | Low to high, varies | Opendatabay, Defined.ai, FileYield | Medium. You must confirm you hold sharing rights |
| Web/scraped corpora | Text or media pulled from public sites | Marketplaces, some labs | Low | Opendatabay, brokers | High. Copyright and source-license exposure |
| Slack/email/docs | Internal conversations and documents | Rarely bought directly | Low, undisclosed | Limited demand | Very high. PII and third-party content |
| Eval datasets | Labeled test sets that measure model performance | Labs, eval-focused buyers | High, varies | DataVendor | Medium. Provenance must be clean |
| Agent trajectories/workflows | Recorded step-by-step task traces | Labs training agents | High, varies | DataVendor, | Medium to high |
| Code + code-adjacent context | Production codebases, tests, docs | Model labs | High, varies | DataVendor | Medium. Contractor IP and license contamination |
OpenAI states it does not seek datasets with personal or third-party information, which is why Slack, email, and scraped dumps sit at the bottom.
What AI companies actually pay for in 2026
The clearest public signal comes from OpenAI's own data-partnership criteria, and they reward intent-rich data over bulk. OpenAI's program asks for datasets that express "human intention," which it defines as long-form writing or conversations rather than disconnected snippets. Raw scraped text is exactly the disconnected-snippet material that criterion filters out.
OpenAI also draws two hard exclusions that shrink the value of casual dumps. It does not want datasets with sensitive or personal information, and it does not want data that belongs to a third party. A scraped web corpus usually fails both tests at once, since it mixes personal data with content the seller never had rights to.
The premium sits with data that reflects real behavior and isn't already sitting on the open web. OpenAI states it wants "large-scale datasets that reflect human society and that are not already easily accessible online to the public today." That standard pushes value toward material a model can't already learn for free, which favors proprietary records over recycled web crawls.
What your data is actually worth
Value tracks how easily a buyer can verify your data and trust where it came from, more than it tracks raw size.
Commodity corpora sit at the bottom. listings show the spread, from £49.99 for a driving video dataset up to £263,000 for a 247-country web data product. Unverified scraped text stays cheap per token because any buyer can scrape similar material themselves.
Structured, purpose-built data commands a premium. Opendatabay lists a healthcare synthetic evaluation kit at £1,999 and an EDR malware telemetry license at £78,228.15. An evaluation set shows a buyer exactly how a model performed on a defined task, and an agent trajectory captures the real steps a workflow took, so both are reproducible and auditable in a way raw corpora aren't.
Eval datasets, agent trajectories, and code-adjacent context sit at the top of the range for the same reason: they map directly to how labs measure and train models, and verifiability is what a lab pays for.
For code and eval-relevant assets specifically, DataVendor publishes the one confirmed anchor point in this tier. It starts modern startup codebases at a $5,000 baseline, then grades up from there based on production history and how legible the code is to a model lab. That baseline is a floor, not a ceiling, and it exists because the buyer can verify what they're getting before they pay.
Treat broker-reported figures with caution. FileYield publishes an average deal value and per-unit pricing on its marketing pages, but those numbers are self-reported and not independently confirmed ().
Where to sell each data type
Match your channel to your data type. Commodity data (static datasets, scraped corpora, internal docs, and general-purpose media) belongs on data-licensing brokers and marketplaces. Eval sets, agent workflows, and code-adjacent context belong with a specialist like DataVendor. Selling the wrong asset in the wrong place either underprices it or fails to find a buyer at all.
Brokers and marketplaces for commodity data
runs an open marketplace for commercially licensed training data, covering text, image, audio, video, code, and agentic trajectories, with public listings ranging from under £50 to over £260,000.
is an enterprise data marketplace and annotation service built around consent and compliance, with certifications like ISO 27001 and GDPR alignment. It's an enterprise-facing channel, not a self-serve listing site.
is a private brokerage that matches data owners directly with AI labs under NDA, with no public listings. Treat its self-reported pricing figures as marketing, not verified market rates.
Skyfire comes up in routing conversations, but no verifiable source describes how it actually works. Confirm independently before treating it as a live option.
DataVendor for code and eval-relevant assets
DataVendor is the right lane when your asset is a production codebase, an eval-relevant asset, or code-adjacent context rather than a bulk data dump. It does not buy your data outright as a marketplace transaction. Instead, it structures and grades the codebase for model-lab buyers, which is why it fits code far better than generic marketplaces do. See what DataVendor lists and how it compares in .
How data deals get structured
Three shapes cover most data deals with AI companies. A license grants the buyer the right to use your data under set terms while you keep ownership, often with usage-based or renewable payment. A one-time sale transfers the asset outright for a single payment. An exclusivity clause bars you from selling the same data to anyone else, and it usually raises the price because the buyer gets sole use.
Most deals move through the same arc. You sign an NDA, hand over a sample, the buyer reviews and accepts, then payment follows on agreed terms. Usage-based compensation, where you get paid per actual use rather than one flat upfront fee, is increasingly floated as fairer to sellers, since .
Beyond that arc, treat any specific figures or clause mechanics as unconfirmed. Public reporting on actual AI data-deal terms is thin, so confirm structure with your own legal and ops review before you commit to anything.
DataVendor is the one lane here with a confirmed process. For production codebases and eval-relevant assets, the flow runs through five steps and starts from a $5K baseline, with grading and terms disclosed up front. The lays out each stage, so you know what acceptance and payout actually look like before you submit.
Legal and consent checklist before you sell anything
Run through five checks before you list anything, because a rights problem you miss becomes the buyer's due-diligence problem and kills the deal.
Ownership. Confirm the company, not a founder or contractor, actually owns the data. Contractors don't get automatic IP assignment the way employees do, so code or datasets built by outside help "technically belong to the individual who created them" without a signed invention-assignment agreement (). Academic-origin work carries the same trap. If a model or pipeline used university resources, the institution likely holds the rights.
Customer-contract rights. Data sitting inside a SaaS vendor's platform, or your customers' data inside yours, may not be yours to license. Ask whether your data is segregated, whether other clients can access it, and whether it's already feeding a shared model (). When your rights come from an upstream model vendor, "the rights it can grant to its own customers are necessarily constrained by the terms accepted from the underlying model provider" ().
PII and consent. Public data is not free to reuse. Building profiles from combined public data points, or resharing it, can each require its own legal basis, and personal data in prompts or training inputs triggers regulatory exposure under regimes like California's AB 2013.
Third-party license contamination. Scraped or aggregated content carries copyright risk that survives into the buyer's product.
De-identification. Strip or mask PII before any sample leaves your hands.
Treat this as a starting point, not clearance. Route any dataset through HUD ops and legal review before you list it publicly or submit it to a buyer.
How to choose, by data type and rights position
Match your data type to how clean your rights are, and the right channel becomes obvious. Two clear cases anchor the decision, with a third that stops you before you list anything.
If you hold commodity data with clean rights, static datasets, scraped corpora, or internal docs you fully own, route it to a broker or marketplace. Opendatabay, Defined.ai, and FileYield exist for exactly this. Your job is proving ownership and stripping anything that isn't yours to sell.
If you hold a verifiable eval set, agent workflow, or production codebase, DataVendor is the fit. These assets earn their value from structure and provenance, not volume, and a codebase-specific valuation depends on how legible your code is to a model lab. The codebase valuation walks through what actually moves the number.
The buyer on the other end usually falls into one of three groups. Model labs want training data and code, acquirers want to buy the whole operating business, and shutdown workflows want to buy code as one asset in a larger bundle. DataVendor serves the first group specifically, so it fits best when a model lab is the realistic buyer, not when you're really trying to sell the company.
Messy rights override both paths. When customer contracts limit what you can license, when third-party code sits inside your repos, or when consent for personal data is unclear, delay the sale and fix the rights first. A fast payout on data you didn't fully own is a liability, not a win.
Conclusion
Commodity data, the raw corpora, scraped text, and internal docs, moves through brokers and marketplaces like Opendatabay, Defined.ai, and FileYield. High-value assets, verifiable eval sets, agent workflows, and production codebases, go to DataVendor, where structure and provenance carry the price.
Confirm ownership, clear your rights, and match the asset to the channel before you sell data to AI companies. Get that order right and the payout follows.
In 2026, model labs reward verifiability, not volume. A clean, reproducible eval or a legible codebase beats a larger unverified dump every time, because a lab can trust what it can check.
FAQ
Can I sell my data to OpenAI?
Yes, through the OpenAI Data Partnerships program, which accepts intake from companies and individuals alike (). OpenAI wants large-scale text, image, audio, or video data that reflects human intention, and it explicitly avoids datasets with personal or third-party information. The page discloses no pricing or compensation structure, so treat any expected payout as undisclosed until OpenAI quotes you directly.
How much is my data worth to AI companies?
Value tracks verifiability more than volume. Live marketplace listings on Opendatabay range from under £50 for a small video dataset to £263,000 for a 247-country web data product, which shows how wide the spread runs by type and quality (). Labeled, provenance-clean eval sets and structured code assets command more than raw scraped dumps because a buyer can trust and reproduce them.
How do data licensing deals with AI companies work?
Most run as a license or a one-time sale, sometimes with exclusivity, and typically move from an NDA to a data sample to acceptance to payout. Public reporting on actual AI deal terms is thin, so treat specific mechanics as something your legal and ops reviewers should confirm rather than assume. For codebases and eval-relevant assets, publishes a five-step flow with a $5,000 baseline.
Can I sell evaluation datasets or workflows?
Yes, and these often earn a premium because they are verifiable and reproducible. Opendatabay accepts eval kits and agentic trajectories, while DataVendor runs eval-relevant assets and code-adjacent context through its five-step process, from application and NDA through scoping, milestones, delivery, and payout. Confirm you hold clean rights before listing any workflow trace that touches customer data.