Automated code documentation

Published pricing: starts at $20/month; higher tiers available.

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2. Swimm

Best for engineering teams that need to understand complicated, unfamiliar code. Creates code-linked explanations and walkthroughs, with synchronization features designed to help documentation remain accurate as the underlying code changes. Particularly worth evaluating for large internal or legacy systems.www.happysupport.ai+2

Pricing: contact sales.

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3. DeepWiki by Cognition

Best for quickly understanding an unfamiliar repository. Automatically creates a browsable wiki, explains repository structure, provides architecture diagrams, and lets developers ask questions about the code. Especially useful for an initial assessment of a public GitHub repository.securityboulevard.com+1

Pricing: free for public repositories; private-repository access has separate requirements.

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4. Mintlify

Best for producing polished developer documentation and API guides. Includes AI-assisted writing and documentation workflows. More appropriate when you want a professional documentation portal, rather than simply generating a complete knowledge base from raw legacy source code.securityboulevard.com+1

Pricing: free starter tier; enterprise options available.

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5. GitBook

Best for teams that need a centralized, searchable documentation site with AI assistance. Supports Git-based documentation workflows and AI-powered search. It is primarily a documentation authoring and publishing platform, so it is less directly suited to generating all documentation from a completely undocumented codebase.tajo.io+1

Pricing: free tier, with paid plans available.

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6. Penify

Best for generating inline code documentation and docstrings through development workflows. Worth considering if your immediate priority is documenting individual functions and classes, rather than building an extensive architectural knowledge base.securityboulevard.com+1

Pricing: a limited free tier and paid options have been reported; verify current plans.

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Which should you choose?

For your undocumented legacy code, I would start with a proof of concept using DocuWriter.ai and compare it with Swimm. Add DeepWiki if you have public GitHub repositories or want a fast initial overview.

What to test before committing

Run a small pilot on one representative legacy module. Evaluate whether each tool can:

• Explain what the module does and why it exists.
• Map dependencies, call flows, and connections to other modules.
• Document classes, functions, APIs, configuration, and business rules.
• Identify undocumented behavior without inventing explanations.
• Detect documentation that needs updating after a code change.
• Meet your security requirements for source-code access, including private repositories and self-hosted deployment if necessary.

One important caution: AI-generated documentation is a first draft, not proof of correctness. For legacy systems, have an engineer validate critical business rules and unusual edge cases before relying on the documentation.

One question to narrow the shortlist

What language or technology is your legacy system primarily built with?

1. Primary technology

Java / .NET / C#

COBOL / mainframe

Python / JavaScript / TypeScript

C / C++

Multiple languages / large monorepo

Other or unknown

2. Your primary goal

Document the entire codebase

Generate inline comments and function documentation

Map architecture, dependencies, and business rules

Help developers ask questions about the code

3. Deployment requirements

SaaS is acceptable

Private cloud or on-premises

Must not send source code to an external service

[Find the best-fit tools]