# DataVendor A marketplace for selling codebases, datasets, tasks, and RL environments to AI labs. - Website: https://datavendor.ai/ - Category: Data seller - Type: Data marketplace - Capabilities: Data licensing, Coding, Evaluation - Reviewed: 2026-09-09 ## Overview **DataVendor is a marketplace for selling codebases, datasets, tasks, and RL environments to AI labs.** Its appeal starts with an imbalance: a business may own something useful for model training without having the relationships, distribution, or experience to sell it. DataVendor brings that supply into a marketplace where buyers can discover and purchase it. The interesting part is the seller's starting point. Some suppliers deliberately create training material. Others already have an asset: a consulting firm's software projects, a startup's private repository, or a collection of specialized documents. For those owners, the challenge is figuring out whether there is demand and reaching the right buyer. Our assessment is that DataVendor's strongest proposition is access to a market. Its value depends on turning potentially useful assets into completed transactions. That makes buyer access, qualification, and seller economics more important than the size of the catalog alone. ## DataVendor and HUD Platform The distinction between the two products is straightforward. [HUD Platform](/companies/hud) provides tools for creating and reviewing training supply, alongside environment-based training and evaluation. DataVendor provides the marketplace where supply is sold. Customers can use both: a specialist can build tasks on Platform and distribute them through DataVendor. Existing asset owners have a different path. A seller bringing a codebase does not need to become an environment developer to understand the marketplace's purpose. That separation makes the product easier to evaluate. A prospective seller can start with the asset and the buyer's requirements, then decide whether creation or evaluation tooling is relevant. ## Who the marketplace fits **Codebase aggregators and consulting firms** are a particularly interesting fit. These businesses may have multiple projects to offer but limited direct access to labs. A marketplace could give them a repeatable distribution channel. Our view is that this makes them stronger recurring customers than sellers with a single asset. **Founders closing a startup** have a different motivation: realizing value from software they have already built. DataVendor offers a possible route to buyers, although a marketplace sale should not be confused with immediate liquidity. Finding demand and completing a transaction still take time. **Specialist task and environment creators** arrive with supply made expressly for AI development. Company background describes a concentrated supplier market with substantial variation in quality. For these vendors, relevant demand and credible qualification matter more than attracting a broad audience. **Owners of unusual domain data** may also fit. The useful question is what the material helps a model learn or measure. A distinctive operating history can be interesting, but uniqueness alone does not establish that a buyer will pay for it. ## What you can sell today DataVendor's [supply documentation](https://datavendor.ai/supply-types) describes repositories, file bundles, tasksets, and runnable environments. A listing can combine asset types, allowing a seller to offer related material together. Depending on the asset, delivery is through a download or managed infrastructure. There is an important availability distinction: operational data is described as a forthcoming category, and the HubSpot and Salesforce connectors are marked as coming soon. Existing file uploads should not be mistaken for a fully launched enterprise-connector product. This breadth gives sellers several entry points. It also makes the profile of the asset important: a repository, a set of scored tasks, and a running environment are different purchases, even when they address the same domain. ## From potential supply to a purchase The public marketplace describes adding inventory, checking quality, and matching supply with buyers. Its documentation also distinguishes a finished listing from a data proposal. A proposal lets a supplier describe what it could deliver before investing in a complete marketplace offer; a listing contains the assets, pricing, and delivery details for a purchase. That distinction is useful for sellers who need to establish interest before doing substantial preparation. It creates a way to discuss supply without treating every promising idea as finished inventory. For an owner evaluating the service, the practical milestone is a buyer accepting the asset. An estimate, a proposal, and a live listing are steps toward that outcome. None is a substitute for a completed sale. ## Pricing and seller economics **DataVendor reports a 20% commission on completed sales.** Under its stated model, the seller sees the lab's purchase price and receives the remaining 80%. Payment depends on a sale occurring. The company also describes most transactions as non-exclusive, creating the possibility of licensing the same asset to multiple buyers. As an illustration, four purchases at $100,000 each would leave the seller with $320,000 after the stated commission. That calculation assumes four completed purchases; it is not a forecast of what a listing will earn. DataVendor reports a $50,000–$300,000 range for typical successful codebase deals. These figures describe assets that sell, not the expected value of an arbitrary repository. Preparation effort, transaction timing, and any other applicable costs also affect the seller's outcome. The useful comparison is with an outright purchase. A fixed payment may transfer some downstream sales risk to the purchaser. A marketplace can preserve more upside for the original seller while requiring them to wait for demand. Sellers should compare the actual offers on proceeds, timing, certainty, and licensing terms. ## Where the proposition needs proof DataVendor estimates that only the top 3–5% of codebases find buyers. The estimate's cohort and measurement period were not specified, so it should not be read as a measured marketplace conversion rate. Its practical implication is still important: qualification matters, and many assets may never generate revenue. We would evaluate DataVendor on completed-sale evidence: time to a first purchase, proceeds after fees, repeat purchases of the same asset, and how much preparation the seller had to do. Those measures would show whether marketplace access translates into meaningful outcomes. For comparisons with [Mercor](/companies/mercor) or [Turing](/companies/turing), request concrete offers. Price visibility, commission, exclusivity, and payment timing are useful comparison points; blanket claims about a competitor's margins are not a reliable substitute. ## Our assessment DataVendor is most compelling for owners of potentially valuable training assets who need a route to AI buyers. The marketplace framing explains both its opportunity and its limits: it can improve discovery and distribution, but revenue depends on demand and completed purchases. For a prospective seller, the sensible starting point is a concrete description of the asset, followed by a discussion of buyer fit, preparation, and commercial terms. [Explore DataVendor](https://datavendor.ai/) to review the marketplace and available seller paths. *Review basis: public product documentation and company-provided background. Commercial terms and market estimates are company-reported and have not been independently verified. This is an editorial assessment, not a hands-on transaction review.* ## Sources & references - [DataVendor marketplace](https://datavendor.ai/) - [What you can sell](https://datavendor.ai/supply-types) - [HUD Platform](https://www.hud.ai/)