"Our docs are out of date" is one of those problems every engineering team says out loud at some point, and it usually gets fixed the same way: someone spends a weekend rewriting a wiki page that goes stale again in a month. DeepDocs and Swimm both promise to break that cycle with AI — but they're built for opposite ends of the same problem. One is a lightweight tool that patches your existing docs the moment your code changes. The other is a heavyweight service that reads an entire undocumented legacy codebase and rebuilds understanding of it from scratch. Comparing them isn't really "which is better" — it's "which problem do you actually have."
DeepDocs: keeps your existing docs honest, automatically
DeepDocs does one job: every time your code changes in a pull request, it checks whether your documentation still matches reality, and fixes it automatically — without storing or indexing your source code long-term (it processes it, then lets go). It preserves the existing structure and style of your docs rather than rewriting them in its own voice, and it explicitly positions itself as complementary to a docs platform like Mintlify or Docusaurus, not a replacement for one. Pricing is straightforward: free for individuals and small projects, roughly $25-30 per seat/month on the Pro tier for teams. The catches are minor but real — the marketing site is behind Cloudflare, which makes it harder to audit in detail, there's no publicly verified founding date, and how well it works depends on how accurately it can read your specific diffs, which you'd want to test on your own repo before trusting fully.
Swimm: turns an unreadable legacy codebase into something a new hire can follow
Swimm is solving a much bigger and slower problem: a company's old, undocumented codebase that nobody currently at the company fully understands anymore. It runs real static analysis on the code — not just an AI summary nobody verified — through a structured four-step process with human validation at each stage, and it's been proven on codebases of 100 million-plus lines. It also supports on-premise or air-gapped deployment with a client-controlled LLM, which matters for regulated or security-conscious companies. None of this is cheap or fast: there's no public pricing or self-serve trial, billing is per engagement (Assessment, Specification, Modernization, Enablement) scaled to how much code is involved, and it requires an actual sales process to get a number. It's built for heavy modernization work, not day-to-day doc upkeep — using it for a small team's routine documentation would be like hiring a construction firm to hang a picture frame.
| DeepDocs | Swimm | |
|---|---|---|
| What it actually does | Auto-patches existing docs on every PR | Reads and rebuilds understanding of a legacy codebase |
| Team size it fits | Individuals and small/mid teams | Large orgs with old, undocumented systems |
| Pricing | Free tier, ~$25-30/seat/mo Pro | Custom, per-engagement (no public rate) |
| Onboarding effort | Plug into your PR flow | Structured multi-step engagement with a vendor team |
| Deployment | Hosted (ephemeral code processing) | On-prem/air-gapped option available |
Pick DeepDocs if your docs are basically fine but keep drifting a little out of sync with the code, and you want that fixed automatically for the cost of a coffee subscription per seat. Pick Swimm if the real problem is that nobody currently understands a massive, aging codebase well enough to write accurate docs in the first place, and you have budget for a proper engagement to fix that at the root. Most teams will never need both — but it's worth knowing which category your actual problem falls into before you buy either one.