Best Platforms for Selling Your Codebase in 2026

An idle codebase can still hold value after a company stops shipping. AI labs and data-curation firms buy real production code, internal documentation, and development history because those assets capture how software is designed, tested, changed, and maintained in real engineering environments.

Founders usually have two options when looking for places to sell a codebase. A training-data sale licenses the codebase as a dataset for AI model development. A whole-business sale transfers the company to a buyer who wants to operate the product, serve customers, and own the remaining business assets. The right option depends on whether the main value sits in the codebase, the operating company, or a broader bundle of company data.

This guide compares five options for selling a codebase, ranked by seller fit. Human Union Data (HUD) Vendor ranks first for modern production codebases because HUD grades the codebase before buyers review it. The grading step provides buyers with a clearer signal of quality, supports valuation, and helps sellers justify a higher payout.

What Selling a Codebase as Training Data Means

Selling a codebase as training data means licensing source code, documentation, commit history, and development context to AI labs or data-curation firms that train models on real engineering work. The buyer values the codebase as a dataset and as a record of how software was built. A whole-business buyer values the company as an operating asset. This is the same AI training data demand that drives labs to acquire RL environments and other real-software assets.

A training-data sale usually moves through five steps. For a wound-down SaaS company, the sale might include repositories, product documentation, pull request history, design notes, and setup context.

• Inventory: The seller identifies the repositories, documentation, commit history, and development context included in the asset.
• Ownership confirmation: The seller verifies clean rights to license the code, with no contractor claims or open-source licensing issues that would block the sale.
• Packaging: The codebase is bundled with the docs, design notes, and workflow context a buyer needs to understand the asset.
• Valuation: The asset is priced through negotiation or, on a platform like HUD, through a grading score that measures codebase quality.
• Buyer match and payout: The packaged asset is matched with a buyer, and the seller is paid in accordance with the agreed terms.

Across these stages, four factors shape the value of a codebase.

• Ownership and rights: Clean title to the code, with no licensing or contractor issues that complicate the sale.
• Packaging: Source code, documentation, history, and context that help a buyer understand the codebase without reverse-engineering the original intent.
• Quality signal: Evidence that the codebase is complete, runnable, and production-tested. Grading turns this evidence into a quality score buyers can use during pricing.
• Buyer channel: The route that connects the asset to AI labs and data-curation firms that pay for production code.

Two Markets for Selling a Codebase

Founders usually have two sale options for a codebase. The training-data market is where AI labs and data-curation firms license code to improve models. HUD Vendor, Project Lazarus, and SimpleClosure fit into this category. The whole-business market is where a buyer acquires a running company to operate, which is where Acquire.com fits.

These markets value codebases differently. A training-data buyer pays for the quality, completeness, rights clarity, and domain specificity of the codebase itself, so a clean codebase from a wound-down company can still have value. A whole-business buyer pays for revenue, users, growth, and operating potential, so the same codebase may be worth less when the company no longer has customers or momentum.

Within the training-data market, one practical difference is whether the platform grades the codebase before buyers review it. A grade gives buyers a clearer signal that the codebase is complete, runnable, production-tested, and safe to license. HUD Vendor is built around that grading step, which is why it ranks highest.

5 Best Platforms for Selling Your Codebase in 2026

1. HUD Vendor

Best for: Founders and consultancies with a serious modern codebase that needs QA grading before buyer review.

HUD Vendor is the codebase-specialist option in this list. Among the training-data platforms compared here, HUD is the only one built around QA grading before buyer review. HUD scores the codebase first, then uses that score to support pricing before the asset enters the buyer pipeline.

Grading That Supports the Price

Grading is the reason HUD works well for sellers with strong codebases. Buyers can pay more confidently when a codebase is complete, runnable, well-documented, production-tested, and clean on rights. HUD turns those signals into a score that buyers can evaluate during pricing.

HUD also handles grading and packaging, so the seller does not have to prepare a raw repository for buyers alone. A founder submits the codebase, and HUD turns the submission into a scored package with the code, documentation, and development context buyers need to review.

What the Rubric Scores

HUD's rubric scores five factors buyers care about during evaluation. Production history shows whether the code ran in the real world. Completeness encompasses pull requests, design docs, incident notes, and other records that document how the team made engineering decisions.

Runnability checks whether Dockerfiles, CI configuration, seed data, and setup instructions make the project easier to review. Right cleanliness confirms whether the seller has clear ownership of the code. Domain specificity lends additional weight to code from focused, valuable software categories, as specialized code can offer more training value than generic repositories.

How the Sale Works

The sale moves through five stages: inventory, ownership confirmation, packaging, grading, and buyer matching. HUD manages packaging and grading between submission and buyer review, reducing the manual work sellers would typically handle in a direct sale.

For founders, the process is simple. The seller submits the codebase through HUD Vendor, HUD reviews the asset against its grading rubric, and the graded package moves into the buyer pipeline.

A Direct Line to AI Lab Buyers

HUD's main advantage is what happens after grading. HUD already works in the evaluation and training infrastructure market for AI labs, so HUD Vendor can route graded codebases into a buyer channel built for AI training data. That gives sellers a more direct route to buyers who pay for production code, instead of relying on a passive listing.

Pros:

• Codebase-specialist focus, rather than a general asset marketplace.
• Built-in QA grading before buyer review.
• Transparent rubric that shows what drives valuation.
• Packaging support for code, documentation, and development context.
• Buyer channel focused on AI labs and demand for training data.
• 80/20 seller split, with the seller keeping the larger share.

Cons:

• Newer category entrant compared with longer-running broad-scope programs.
• Final payout depends on codebase quality, documentation, production history, and clarity of rights, so weaker submissions may remain closer to the baseline.

Pricing: Starts at a $5,000 baseline per codebase and increases with grading. The seller receives 80% of the final payout, with 20% covering grading and packaging. Sellers can request a codebase review and use the earnings calculator on the HUD Vendor codebase page.

2. Project Lazarus (Turing)

Best for: Sellers with legacy codebases or broader operational data bundles.

Project Lazarus is Turing's program for monetizing startup codebases and operational data for frontier AI training. It fits sellers who have more than a modern repository to sell, especially when the asset includes legacy code, internal documentation, tickets, workflows, or other operational records.

The main advantage is scope. Project Lazarus is designed for both codebases and operational data, so it can work well for sellers with a larger company archive. The limitation is that pricing is not built around codebase-specific QA grading before buyer review, so a strong, modern repository may not receive the same valuation signal as a graded process.

Pros:

• Handles codebases and broader operational data.
• Public pricing ranges make the program easier to benchmark.
• Fits legacy repositories and larger company archives.
• Backed by Turing's broader AI data business.

Cons:

• Less specialized for codebase-only sales.
• No public grading-led pricing model for modern repositories.
• Modern repository payouts appear capped below the highest legacy-code ranges.

Pricing: Current public pricing shows $10K–$100K for legacy code and up to $15K per modern repository. Larger operational data bundles may be priced separately based on scope.

3. SimpleClosure Asset Hub

Best for: Shutdowns where source code and workspace data need to be handled as part of the company wind-down.

SimpleClosure Asset Hub helps founders recover value from startup assets during dissolution. Asset Hub supports Source Code and Workspace Data, with Workspace Data currently in beta. It fits sellers who need one managed workflow for codebases, product docs, tickets, communications, and other operational data.

SimpleClosure is strongest when the sale is part of a broader shutdown process. It reviews the asset, assesses buyer interest, handles PII scrubbing, and coordinates proceeds and distributions. For sellers with only a strong modern codebase, the generalist shutdown workflow may be less precise than a codebase-specific grading process.

Pros:

• Handles source code and workspace data.
• Fits founders already moving through a company shutdown.
• Includes asset review, buyer matching, PII scrubbing, and proceeds support.
• Workspace Data can cover communications, workflows, and internal documents when eligible.

Cons:

• Runs a generalist shutdown workflow, so a strong standalone codebase misses code-specific grading.
• Workspace Data is still in beta.

Pricing: No fixed payout range for sellers disclosed, and asset value depends on the review and buyer interest.

4. Acquire.com

Best for: Founders selling an operating business with revenue, customers, and a buyer-ready handoff.

Acquire.com sits in the whole-business acquisition market. Buyers on Acquire.com are looking for companies they can operate, not codebases to license as AI training data. This route is well-suited to SaaS companies, apps, agencies, content businesses, ecommerce companies, and other online businesses with real revenue and clean operating records.

Acquire.com can be the right route when the business still has customers, revenue, financial history, and growth potential. It is usually the wrong market for an idle codebase from a wound-down company because operator-buyers value the company's future operating potential, not the codebase as a standalone training dataset.

Pros:

• Large marketplace for selling online businesses.
• Fits operating companies with revenue and customers.
• Provides listing support, buyer vetting, NDAs, legal document builders, and escrow support.
• Useful when the founder wants the product or company to continue under a new owner.

Cons:

• Not designed for selling a codebase as training data.
• Requires business diligence, not only codebase review.
• Sale timelines depend on buyer interest, financial readiness, and negotiation.
• Seller fees apply when the business sells.

Pricing: A 6–8% closing fee based on asking price, plus a $25–$100 monthly listing fee. A realistic average sale timeline is about 90–120 days, though prepared deals can move faster.

5. Direct-to-Lab Deals

Best for: Founders with an existing AI lab or data-team relationship who can manage the sale directly.

A direct-to-lab deal means approaching an AI lab or data buyer without a marketplace. This route gives the seller more control over terms, but it also puts the full burden on the seller. The founder has to prepare the codebase, confirm rights, handle diligence, negotiate the agreement, and manage the transfer.

This route can work when the founder already has a warm relationship with a lab or knows a buyer looking for that specific type of code. For most sellers, direct outreach is slower and less predictable because there is no built-in grading process, buyer-matching workflow, or pricing benchmark.

Pros:

• No platform take rate.
• Full flexibility over terms and deal structure.
• Can work for founders with a warm buyer relationship.

Cons:

• No public pricing benchmarks.
• No built-in grading, packaging, or rights-review workflow.
• Higher failure risk without an existing buyer relationship.
• The seller handles diligence, negotiation, and transfer.

Pricing:Depends on the buyer, codebase quality, rights clarity, data scope, and the seller's negotiation leverage.

Quick Comparison: Best Platforms for Selling Your Codebase (2026)

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How We Ranked These Platforms

Each option serves a different type of seller, so the ranking prioritizes seller fit over the highest possible payout. We compared the five options across six factors.

• Grading and quality signal: A graded codebase gives buyers clearer evidence that the asset is complete, runnable, production-tested, and clean on rights. HUD is the only option in this list built around QA grading before buyer review.
• Buyer channel: Training-data platforms connect codebases with AI labs and data buyers that pay for engineering knowledge. Whole-business marketplaces connect founders with operators who want to acquire and run a company. The buyer channel determines whether the codebase is valued as a dataset or as part of an operating business.
• Pricing transparency: Public pricing is uneven across the category. Project Lazarus publishes payout ranges for legacy and modern code. HUD publishes a baseline and adjusts the final payout through grading. SimpleClosure does not publish a fixed seller payout range, and direct-to-lab deals are negotiated on a case-by-case basis.
• Asset scope: Some sellers have only source code, while others have code, documentation, tickets, workspace data, and internal communications. HUD focuses on codebases. Project Lazarus and SimpleClosure support broader asset bundles. Acquire.com handles the whole company.
• Process support: Packaging, rights checks, PII review, buyer matching, and transfer can take real work. HUD and SimpleClosure manage much of the process for sellers. Direct-to-lab deals leave preparation, diligence, negotiation, and transfer with the seller.
• Time to close: Training-data sales can move faster than full acquisitions because the buyer evaluates a dataset instead of an operating company. Whole-business sales usually require deeper due diligence into revenue, customers, operations, and handoffs. Direct-to-lab deals are the least predictable because there is no standard marketplace workflow.

How to Choose the Right Selling Option

Start with the asset the buyer is paying for: a graded codebase, a broader company archive, or the operating business.

• HUD Vendor: Best fit for a modern, well-documented production codebase where grading can support buyer confidence and valuation. HUD is the strongest option when the codebase itself is the main asset and the seller wants QA review before buyer evaluation.
• Project Lazarus:Better suited to legacy codebases or broader operational data bundles. Turing's program is useful when the asset includes code plus company records that may have AI training value beyond the repository.
• SimpleClosure Asset Hub: Strongest fit for a full shutdown where source code, workspace data, documents, and internal records need to move through one managed dissolution workflow.
• Acquire.com: Relevant when the company is still operating, and the buyer is acquiring revenue, customers, operations, and future upside. Acquire.com fits a whole-business sale, not a codebase-only training-data sale.
• Direct-to-lab deals: Worth considering only when the founder already has a warm relationship with an AI lab or data buyer. Without a marketplace, the seller handles packaging, rights review, diligence, negotiation, and transfer directly.

Why HUD Vendor Leads for Modern Codebases

HUD Vendor leads for modern codebases because it adds QA grading before buyer review. A strong codebase is easier to price when buyers can see evidence that the repository is complete, runnable, production-tested, well-documented, and clean on rights. HUD turns those signals into a grade, then uses that grade to support valuation.

HUD also reduces the work required from the seller. Instead of preparing a raw repository for buyers alone, the seller submits the codebase, and HUD handles the packaging and grading work needed for buyer review. The seller keeps 80% of the final payout, with 20% covering grading and packaging.

For founders with a serious modern codebase, the main benefit is the combination of grading, packaging support, and a buyer channel focused on AI training-data demand. Project Lazarus may fit legacy code or broader operational data bundles. For a modern codebase that should be evaluated on code quality, HUD Vendor is the clearest starting point.

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FAQs

Can you sell a codebase on more than one platform at once?

You can usually request reviews or submit intake forms with more than one company before you accept an offer. The important point is to avoid signing overlapping rights, exclusivity, or transfer terms for the same asset. For example, a seller may compare HUD Vendor, Project Lazarus, and SimpleClosure before choosing an offer. Once a deal moves into contract review, the agreement should clearly define whether the buyer receives a license, an exclusive license, or a full transfer.

Who actually buys these codebases?

The buyers are usually AI labs, data curation companies, strategic technology buyers, and operators seeking real production assets. In a training-data sale, the buyer values source code, documentation, pull requests, tickets, workflows, and other records that show how software was built and maintained.

What is the difference between selling to AI companies and selling the business?

Selling to AI companies usually means licensing the codebase or company data as training material. The buyer wants the engineering knowledge inside the repositories, documentation, and development history. Selling the business means transferring an operating company to a buyer who wants the revenue, customers, product, brand, and future operating potential.

Do you keep ownership of the code after selling?

Ownership depends on the contract. Some training-data deals may be structured as licenses, while a full company sale may transfer the codebase with the rest of the business assets. The seller should review three terms before signing: whether the deal is exclusive, whether the buyer can reuse or resell the asset, and whether the seller can continue using the code. Those terms decide what rights remain with the seller after the sale.

How long does the process take with each option?

Timing depends on asset readiness, buyer review, rights checks, and negotiation. Training-data sales can move faster than full acquisitions because the buyer is reviewing the codebase or data asset, not the full operating history of a company. SimpleClosure says Asset Hub transactions typically happen within 1–2 weeks after meeting an asset specialist, while Acquire.com says sellers can reach buyers and close in as little as 90 days. Direct-to-lab deals are less predictable because there is no standard marketplace workflow, buyer intake, or published review timeline.

What about Project Lazarus's referral program?

Project Lazarus currently offers a $1,000 referral reward when a referred seller closes a qualifying deal. The referral program can be useful for someone who knows founders with valuable code or business data, but sellers should confirm the current terms directly before counting the referral amount as part of the economics.