Searching "AI code review tool" isn't the same search as "AI coding assistant." Someone typing this already has a coding tool, or several, and a pull request pipeline they trust for shipping. What they want is a second pair of eyes on the diff, one that doesn't get tired on the fortieth review of the day. That's a narrower, more specific job than writing code, and it's worth treating as its own category rather than a feature bullet on a broader agent.
What an AI code review tool does
An AI code review tool connects to a git host, GitHub, GitLab, Azure DevOps, or Bitbucket, and reads a pull request the way a senior reviewer would: it looks at the diff, pulls in surrounding context from the rest of the repository, and leaves comments on lines that look wrong. It doesn't write the change. It reads one that a person or an agent already wrote, and flags what a busy human reviewer might miss, an off-by-one, a missing null check, a security pattern that's fine in isolation but wrong given how the function gets called elsewhere.
The better tools in the category go further than comments: one-click fixes, generated unit tests for the changed code, merge-conflict resolution, and increasingly a planning step that turns a Linear or Jira ticket into a review checklist before anyone opens an editor. None of that changes the core job. It's still downstream of the change, reviewing what already exists rather than producing it.
Review is not delivery proof
The 2025 DORA report on AI-assisted software development makes a point worth sitting with: AI doesn't replace code review, it makes code review more critical, because faster generation is exposing testing and review as the bottleneck teams weren't equipped to handle at the new pace. The same report finds that 30% of developers report little or no trust in AI-generated code. A review tool exists precisely because that trust gap is real. It doesn't close it.
Here's the distinction that matters for a software house billing a client: a review tool judges whether a diff looks correct. It doesn't attest that the delivered feature meets the requirement that was actually agreed, that the change was authorized by someone with the standing to authorize it, or that the review that happened is still valid after the next merge touches the same file. Those comments live inside the pull request, on that git host, in that host's format. If the team migrates repos or the reviewer leaves, the reasoning behind an approval doesn't travel with the code, the same gap that shows up when a coding agent's own session log never leaves the tool that produced it.
The METR randomized controlled trial from mid-2025 is a useful check on how much review buys you on its own: experienced developers using AI tools on real tasks in codebases they knew well were measured 19% slower, even though they believed, after the fact, that they'd been about 20% faster. METR has since flagged that result as specific to that setup, not a permanent verdict on AI tooling, but the gap between felt speed and measured speed is exactly why a diff looking clean in review isn't the same claim as a delivery being verifiably correct.
CodeRabbit, Greptile, and the job they sell
CodeRabbit and Greptile are the two names that come up most in this exact search, and they sell close to the same job with a different angle. CodeRabbit's paid tiers start at $24 per user per month for single-repo agentic review with one-click fixes, and $48 per user per month for Pro Plus, which adds multi-repo analysis across up to ten repositories, custom pre-merge checks, generated unit tests, merge-conflict resolution, and the issue planner. Self-hosting, SSO, and audit logging sit behind a custom Enterprise plan, according to CodeRabbit's own pricing page.
Greptile's pitch is narrower and more codebase-centric: a semantic graph of the whole repository, built before it reviews a single pull request, aimed at catching bugs that span multiple files rather than issues visible in one diff alone. Its Pro tier runs $30 per seat per month with 50 review credits included and $1 per additional review, unlimited repositories once you're on a paid seat, and Enterprise unlocks self-hosted deployment, SSO/SAML, and GitHub Enterprise support, per Greptile's pricing page.
Both are honest about what they are: a layer that reads a diff and a codebase, then comments. Neither produces a portable, signed record of why a specific change was approved, separate from the git host it ran on. That's not a knock on either product, it's outside the job they're built to do.
What to ask before buying
- Does it already support the git host the team standardizes on, and does it fit the existing PR workflow, or does adopting it mean changing how reviews happen.
- What does the free or entry tier actually rate-limit, and does the team's PR volume clear it without an upgrade.
- Does it produce fix suggestions and generated tests, or comments only that still need a human to act on.
- Does it reason across the whole codebase, or only the lines in the current diff, if the bugs that matter most in this codebase tend to span files.
- Is self-hosting or data residency a requirement now or soon, since both vendors gate it behind Enterprise pricing.
- If the team switches git hosts or review tools next year, does anything about a past approval move with the code, or does it stay behind in that tool's history.
Where review sits on the line from requirement to release
Review is one checkpoint on the line from a requirement to a release that a client or a regulator can actually check, not the whole line. How to choose an AI coding assistant covers the categories around it, completion, agent, and review, and why judging one on another category's job is the wrong test. The best AI for coding roundup places CodeRabbit and Greptile against the rest of the field on the same criterion.
A good review tool catches more bugs before merge than a rushed human pass would, and that's worth paying for on its own. What it can't do is stand in for the layer above every tool in this category, the one that keeps intent, authorization, execution, and review linked and provable together, past the point where the pull request itself is closed and forgotten.