CodeRabbit
by CodeRabbit
AI code review and agentic change management for teams shipping human- and machine-written code
CodeRabbit is an AI code review platform that reads every pull request with full repository context, then comments, ranks and auto-fixes issues before merge. It is built for engineering organisations whose review capacity has been overwhelmed by AI-generated code, and it now extends past review into triage, blast-radius analysis and continuous post-merge security monitoring.
CodeRabbit is a SaaS platform that automates code review by analysing each pull request against the whole repository rather than the diff alone, posting PR summaries, line-level comments, committable one-click fixes and an in-PR chat developers can argue with. Founded in 2023 by Harjot Gill, Guritfaq Singh and Vishu Kaur and headquartered in San Francisco, it installs on GitHub and GitLab, runs conventional linters and SAST tools alongside its language-model layer, and learns team-specific preferences over time so repeat nitpicks decay. On 12 August 2026 the company raised a $143 million Series C at a $1.5 billion valuation, co-led by Atomico and Smash Capital with participation from BMW i Ventures, Datadog, SineWave Ventures and Hirtle Callaghan alongside existing backers CRV, Scale Venture Partners, Flex Capital and Pelion Venture Partners, and used the announcement to launch Agentic Change Management, a deliberate repositioning from reviewer to control layer for software change. That release added three parts: Triage, which scores incoming pull requests by value, dependencies, urgency, risk and reviewer fit so consequential work routes to humans while low-risk changes automate; Change Stack, which replaces the file-by-file diff with semantic diffs, blast-radius analysis and architectural impact diagrams for very large changes; and CodeRabbit Security, which continuously scans shipped code for logic and dependency vulnerabilities, generates fixes and routes them back through the pull-request workflow. The company reports more than 17,000 customers, six million repositories and over two million reviews a week, naming NVIDIA, BMW, Adyen, JFrog, Indeed, Swiggy, Visma and Trivago. It is SOC 2 Type II compliant and states it does not train on customer code.
A VP of Engineering or platform lead whose merge queue has outgrown human review capacity because coding agents now open most of the pull requests.
Every pull request gets a repository-aware review and a risk score within minutes, so human reviewers spend their attention only on the changes that actually carry risk.
At a Glance
- Category
- Developer Tools
- Pricing
- Subscription, Usage-based, Freemium
- Target Market
- CTOs, VP Engineering, Enterprise Developers, Platform Engineering Teams, Security Engineers
- Deployment
- Cloud-first, Self-hosted
- Founded
- 2023
- Headquarters
- San Francisco, United States
- Team Size
- 201-500
- Customers
- 17,000+ customers across roughly 6 million repositories
Key Features
- ✓Repository-context code review
Reviews each pull request against the entire codebase rather than the diff, catching cross-file and architectural breakage a diff-only reviewer cannot see.
- ✓PR Triage and risk scoring
Scores incoming pull requests by value, dependencies, urgency, risk and reviewer fit, routing consequential work to humans and automating low-risk changes.
- ✓Change Stack impact analysis
Replaces the file-by-file diff with semantic diffs, blast-radius analysis and architecture diagrams so reviewers can reason about very large changes.
- ✓Continuous security monitoring
Scans shipped code and dependencies for logic flaws and known vulnerabilities, generates candidate fixes and routes them back through normal pull requests.
- ✓Coding-agent loops and one-click fixes
Emits prompts that Claude Code or Codex can execute plus committable suggestions, so findings become applied fixes rather than dormant comment threads.
- ✓Adaptive learnings and review profiles
Learns team preferences over time and offers Quiet, Chill and Assertive profiles so teams can dial verbosity down as the model calibrates.
- ✓Linter and SAST integration
Runs deterministic static analysis alongside the language-model layer, so rule violations are not left to probabilistic judgement alone.
Capabilities
Use Cases
- •Gating AI-generated pull requests
An engineering org where coding agents open most PRs uses CodeRabbit as first reviewer, so humans only see changes it flags as consequential.
- •Pre-merge security review
Security teams get every pull request scanned for logic flaws and vulnerable dependencies before merge, with generated fixes routed back into the same PR.
- •Reviewing very large refactors
A platform team ships a thousand-file migration and reviews it through blast-radius and architecture views instead of a diff nobody can read.
- •Standardising review across repositories
A company with hundreds of repositories encodes its coding standards once and gets consistent enforcement without staffing a reviewer on every team.
- •Open-source maintainer triage
A maintainer receiving unsolicited contributions gets free automated first-pass review on public repositories, filtering obvious problems before spending personal time.
Ideal For
Best For
- ✓Teams where AI coding agents generate most pull requests and human reviewers cannot keep pace with the volume
- ✓Enforcing organisation-wide coding standards and security policy consistently across hundreds of repositories
- ✓Catching logic bugs, race conditions and auth mistakes that linters and static-analysis rules do not model
- ✓Triaging a large merge queue so high-risk changes reach senior reviewers and low-risk ones merge automatically
- ✓Open-source maintainers who want free automated first-pass review on public repositories
Not Ideal For
- ✗Solo developers and small teams on tight budgets — at $24 to $72 per developer per month the per-seat cost compounds quickly relative to the PR volume a small team actually produces
- ✗Teams that need a deterministic, reproducible merge gate: because the final layer is a language model, a second run over the same pull request routinely surfaces findings it ignored the first time
- ✗Anyone expecting to remove humans from review entirely — practitioner accounts describe overzealous security flags and nitpick noise that still require a person to adjudicate
Deployment
Market & Ratings
17,000+ customers across roughly 6 million repositories
Market Analysis
Pros
- ✓Catches genuine defects — race conditions, auth mistakes, edge cases — that linters and static-analysis rules do not model
- ✓Setup is genuinely straightforward: authorise the GitHub app on your repositories and scanning starts on the next pull request
- ✓Improves with use, with practitioners reporting the analysis has sharpened over time and style-related false positives falling noticeably after a few weeks
- ✓Findings convert into action via one-click committable fixes and prompts that Claude Code or Codex can execute directly
- ✓Free forever on public repositories, which is a real option rather than a token tier for open-source maintainers
Cons
- ✗Nitpick noise and false positives are the single most consistent complaint across G2, Reddit and Hacker News; even a clean large pull request can draw twenty or more comments in verbose mode
- ✗Non-deterministic behaviour: resolving every comment often triggers a second review that raises entirely new issues the first pass ignored
- ✗Slow on large diffs, with independent accounts describing waits of several minutes and up to roughly twenty minutes on large or complex pull requests
- ✗Security checks skew overzealous, flagging patterns that are safe in the specific context, so every finding still needs a human to adjudicate
- ✗Free-tier rate limits bite quickly on active repositories — one team documented 200 files per hour and three back-to-back reviews before throttling
Pricing
Open Source
$0
- ✓Free reviews forever on public repositories
- ✓AI code reviews on PRs and CLI
- ✓Rate limits apply
Essentials
From $24/developer/mo
- ✓AI code reviews on PRs and CLI
- ✓1-click fixes and learnings
- ✓Coding-agent loops and agentic chat
- ✓Linters/SAST, Jira and Linear integrations
- ✓5 MCP connections, 5 PR reviews per developer per hour
Team
From $48/developer/mo
- ✓Everything in Essentials
- ✓PR Triage
- ✓Custom pre-merge checks
- ✓Finishing touches: unit tests and merge-conflict resolution
- ✓Post-merge actions
- ✓10 MCP connections, 8 PR reviews per developer per hour
Advanced
From $72/developer/mo
- ✓Everything in Team
- ✓Continuous security monitoring
- ✓Security review on every PR
- ✓Blast radius and architectural impact analysis
- ✓15 MCP connections, 10 PR reviews per developer per hour
Enterprise
Contact for pricing
- ✓Everything in Advanced
- ✓Custom RBAC, SSO and audit logging
- ✓API access and self-hosting
- ✓Multi-org support
- ✓SLA support and dedicated CSM
List pricing is published and charged per developer: $24, $48 and $72 per developer per month billed annually for Essentials, Team and Advanced, rising to $30 and $60 on monthly billing. Public repositories are free forever and there is a 14-day trial with no card. The real meter is the hourly cap on PR reviews per developer (5, 8, 10 and 12 by tier) plus MCP connection limits, which a busy team hits before it hits the seat cost. Self-hosting, SSO, RBAC, audit logging and API access are Enterprise-only and unpriced. Add-ons bill separately at $0.25 per reviewed file and $0.40 per agent minute.
Security & Compliance
Connect
Sources
This page was written from 5 sources, 3 on domains other than coderabbit.ai.
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