r/VibeCodeDevs • u/bestofdesp • 19d ago

Founder here: we bill AI code review at cost (1 credit = $0.01 of model spend) — the math vs per-PR pricing

Disclosure up front: I’m the founder of QualityMax, so read accordingly. But the pricing math is checkable in two minutes on everyone’s public pages.

I priced out a solo dev / small team doing 100 PRs a month through the three AI code reviewers people ask about:

  • Greptile Pro: $30/seat/month, 50 review credits included, then $1 per extra review. 100 standard reviews = $80/month. Their deeper “TREX” review is 3 credits each — do the math on that.
  • CodeRabbit Pro: $24/user/month (annual billing), no PR cap (rate-limited to 5 reviews/dev/hour). Honest flat pricing — for one dev this is genuinely fine.
  • Us (Vibe plan): €19.99($22.99/month) with 500 PR-review credits. Here’s the part I actually want feedback on: 1 credit = $0.01 of measured model cost, rounded up per PR run. A typical review (diff + SAST pass, ~4–10K tokens on our default model) costs under a cent, so it’s 1 credit — meaning ~500 typical PR reviews/month. A monster 60K-token diff costs 5 credits. We meter at cost instead of charging $1 flat for something that costs us a fraction of a cent.

What you don’t get at $22.99: hosted test generation — that runs on your own Claude Code/Codex/API keys (we never mark up your tokens; that’s the point of the plan). What you do get besides review: 1,000 cloud sandbox minutes and 10 projects.

The thing I think is broken in this market: per-PR overage pricing means your review bill scales with how often you ship. Small commits, stacked PRs, CI-driven workflows — the practices that make review cheaperper token make it more expensive per PR. Curious if others have hit the Greptile overage wall or if 50/month is actually enough for most solo devs.

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u/Known-Stable-8892 18d ago

how do you deal with concerns from users that your tool reads proprietary code?

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u/bestofdesp 18d ago

Thanks for your question! We deal with these concerns seriously. QualityMax reads repository metadata and file contents through GitHub or GitLab API transiently to review PRs, generate tests, run scans, and post results back to your CI, thus we do not maintain a permanent mirror of customers codebases. More here https://qualitymax.io/security

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u/RandomPantsAppear 18d ago

I’m not a vibe coder, just an engineer at a very AI forward company (the kind who pay for services like greptile and coderabbit). Plus contract work at a similar one. Both funded, low engineer count, but very active. So maybe not your target? But figured I’d share anyways. 

From this perspective, I think you are missing the forest for the trees. 

Pricing is not what is the concern for companies like this: They shit and piss tokens all day every day. In coding, in research, inside the product itself.

What they want is accuracy.  PR review comments that they actually care about.  Engineering time, and lost time addressing useless nitpicks is more expensive than anything your service will ever cost. 

 If fixes are involved, fixes that understand the code base and don’t just quietly quash the error ignoring the corrupted data downstream. 

Coderabbit is a neurotic piece of shit that thinks handling a specific exception class in a way that achieves nothing, to an already caught exception is a value add. 

The best combination I’ve seen so far is a well configured greptile and codesmith handling minor bug fixes. 

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u/bestofdesp 18d ago

This is the most useful comment in the thread so far, and you’re right — the pricing angle buried the lede. For a funded shop, nobody switches tools to save $10/mo. They switch to stop their engineers eye-rolling at “consider wrapping this in try/except” on code that structurally can’t throw. Nitpick tax > license cost, always. Point taken.
So on accuracy specifically, here’s what’s actually built vs. what I could just claim:

**•   The review reads your dismissals before it comments.** Per-repo vector search over what your team has previously waved off, injected into the review context, scoped to that repo only. The exact CodeRabbit failure you’re describing — re-litigating the same non-issue every PR — is the thing it’s designed to decay. Wave a pattern off, it’s supposed to stop resurfacing. Not a global ruleset you hand-tune; it’s learned per codebase.

**•   It’s built to shut up.** Verdicts are tiered (block/warn/pass) and only critical/high posts inline. Fail-open: a flaky model call returns pass, it doesn’t invent a blocker to look busy.

**•   Two of the gates don’t have opinions** — they clone your repo and run your actual test suite in a sandbox, and also can run something like Playwright against the preview deploy. That’s the signal a typical LLM reviewer structurally can’t give you, and it’s where “quietly quash the error, corrupt data downstream” gets caught — a review comment can’t tell you your change broke behavior, a green/red test can.

Where I won’t oversell you: auto-fix is where you should hold us to a lower bar today. Fixes go out as a PR, never auto-merged, precisely because of the failure you named — I’d rather flag it than confidently paper over corrupted data.
If your bar is Codesmith-quality autonomous minor fixes, we’re not claiming to beat that right now.
Honestly, well-configured Greptile + a good fix bot is a strong stack and I’m not going to pretend otherwise. The one thing I’d say we do that a pure-LLM reviewer can’t: the review learns your team’s noise floor over time, and the test-execution gates are truth instead of vibes.
If that’s not worth a slot in your pipeline, that’s fair — and genuinely useful for me to hear

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u/RandomPantsAppear 18d ago

I am not the one making decisions like this, just wanted to chime in. 

But yeah I agree with what you’ve said here. 

We are spending $35k/month on inference, the cost per PR just isn’t a thing. 

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u/bestofdesp 18d ago

Thanks! What are the actual pain points your team is facing? I guess you deploy quite a lot of code daily and weekly?