Developer tools decision
Augment Code
A single technical user can build a useful automated PR-reviewer (the common core loop) in ~one week, but reproducing Augment Code's full platform (context engine, shared memory, sandboxes, enterprise controls, and orchestration at scale) is much larger and not practical as a drop-in replacement.
Visit website↗$100/mo
$1,200/yr
Read off the official pricing page.
$50one-off30 h to build
$200/mo8 h/mo upkeep
On cash alone, building overtakes the subscription at 3 seats.
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. All Augment Code alternatives, with the arithmetic →
What a replacement has to do
- Automate first-pass code review on GitHub PRs with an LLM-driven agent that fetches diffs, reasons over context, posts review comments, and optionally opens or updates PRs.
What it still won’t have
- Shared organization memory and cross-run learning
- Managed sandboxes and tenant-level isolated execution
- Built-in enterprise security/compliance (SOC 2, CMEK, ISO) and audit trails
- Service accounts, per-automation tokens, and usage monitoring
- Multi-model routing and a catalogue of specialized sub-agents/Experts
What remains hard
- Execution quality
Cosmos runs your software agents at scale, giving them the context, tools, and feedback loops they need to get better with every workflow.
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 3 seats.
Money you would actually spend
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
Build a minimal self-hosted 'Agentic PR Reviewer' in Node.js: implement a GitHub webhook receiver (Express), fetch PR diffs and relevant files, produce a summarized code-context prompt, call an LLM via OpenAI-compatible API, parse the model's structured review (inline comments + overall verdict), and post comments/reviews back to GitHub. In scope: webhook handling, context retrieval, prompt construction, model integration, posting GitHub review comments, basic deduplication/state (Postgres), Dockerfile, unit tests for webhook and GitHub client, and CI workflow. Out of scope: multi-tenant orchestration, managed sandboxes, SOC2 compliance, multi-model routing, and production-grade sandboxed code execution. Require error handling, retries, rate-limit backoff, logging, and tests covering happy/error paths.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 4 cited sources+3
- Price verified on pricing page+3
- Evidence score63
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 · 4
Every page the run actually retrieved.
- official productAugment Code - Home
- official pricingAugment Code Pricing
- official docsAuggie CLI docs
- open sourcedanger/danger
Integrity checks
What held up, and what did not.
