Developer tools decision

Devin

A capable engineer can reproduce a useful single-repo agent (PR generation, test run, CLI/mini UI) in ~30 hours using open models and tooling, but Devin's proprietary model (SWE 1.7) and enterprise features (VPC/SSO/scale orchestration) are material differentiators that are costly or impractical to replicate fully.

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Subscription$20/month ✓ verified
Initial build30 hours
Monthly upkeep8 hours + $100
Evidence2/3 runs agree

Open-source builds that already do this

Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need — the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship.

What a replacement has to do

  • Index a git repo, generate code changes with an LLM given ticket instructions, create a PR via Git provider API, run tests/CI and report results, accept human review/merge.

What it still won’t have

  • Access to Devin's proprietary model SWE 1.7 and any fine-tuned models
  • Enterprise features: VPC deployment, SAML/OIDC SSO, centralized admin controls
  • Built-in integrations and multi-repo orchestration at scale
  • Vendor support, onboarding, and product-level analytics

What remains hard

  • Proprietary modelsSWE 1.7, our latest model, is now available →
  • Proprietary modelsFree use of SWE 1.7 and leading open source models
Read the build prompt

First-year cost

Keep paying

Paying ischeaper in year one.

On cash alone, building overtakes the subscription at 6 seats.

Paid seatsseats

Money you would actually spend

Keep paying

Subscription price × seats × 12

Build it

AI build APIs + hosting

Time you would spend

What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build an autonomous repo-to-PR agent (stack: Node.js or Python backend, Postgres, Redis, Next.js or minimal React UI) that: 1) clones and indexes a single git repo into embeddings (use OpenAI or an open LLM + local vector DB like Milvus/FAISS), 2) accepts a ticket/prompt and calls an LLM to produce a patch/diff, 3) applies the patch in a feature branch and opens a PR via GitHub API, 4) runs tests in a container (Docker) and reports pass/fail, 5) exposes a CLI and a minimal web UI to review session logs and approve/merge. Out of scope: multi-repo fleet orchestration, proprietary model training, enterprise SSO/VPC. Include error handling for failed merges, CI failures, LLM timeouts, and unit tests for core flows.
How we checked5 sources · 2/3 runs agreed · evidence score 60

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 5 cited sources+3
  • Price verified on pricing page+3
  • Hard moats found in the evidence-3
  • Evidence score60

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time — so the same evidence always produces the same number.

How scoring works →

Cited sources · 5

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ Price read off the page! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 2 moats quoted from the page