AI Code Audit vs AI Code Review in 2026: The 14 Tools That Matter, Sorted by What They Actually Do#

Most articles call themselves "the complete list of AI code review tools" and then mix together products that do very different jobs. This guide does the opposite. It separates AI code review (forward-looking, runs at pull-request time, sees a diff) from AI code audit (backward-looking, runs against an existing codebase, sees full files or the whole repo). Then it walks through 14 tools, one by one, sorted by category, with pricing model, self-hosting, local-model support, and the honest tradeoffs of each.

Last updated: May 2026. Tools listed in current-market order, not endorsement order. All product claims are sourced from each vendor's documentation as of writing.

Why "audit" and "review" are two different jobs#

The phrases "AI code review" and "AI code audit" are often used as synonyms in marketing copy. In practice, the tools sit on opposite sides of three axes.

Axis 1: when does the tool run? A review tool runs on a pull request, just before merge. It sees the diff and reasons about the change. An audit tool runs against the code already in the repository, on demand or on a schedule. It sees full files, sometimes the whole repo, and reasons about what is there regardless of when it was written.

Axis 2: what is the unit of analysis? Review tools think in diffs: lines added, lines removed, files touched in this PR. Audit tools think in files, modules, or repositories: every function, regardless of when it was authored.

Axis 3: what is the deliverable? Review tools deliver inline PR comments and a summary, consumed by the author and merger. Audit tools deliver a report (Markdown, JSON, dashboard), consumed by a tech lead, an inheriting maintainer, an auditor, or a due-diligence buyer.

Both jobs are useful, and both can use the same underlying LLMs. They are still different jobs, and a tool optimised for one is rarely good at the other. A team that already uses Copilot Code Review on every PR may still have no answer for "we just inherited a 200k-line repository, where do we start?". Conversely, an audit tool that produces a 40-page report on Monday morning is poor at gating a Tuesday-afternoon merge.

The market has reflected this split through 2025 and 2026: review-first tools (CodeRabbit, Copilot Code Review, Greptile, Qodo) have stayed focused on the PR ceremony, while a smaller cluster (Anatoly, Sourcegraph Cody, Augment Code) targets the audit deliverable. A third group (Snyk Code, Codacy, SonarQube with AI, Korbit) sits at the intersection or in a specialty niche such as security.

How this list was assembled#

The 14 tools below were selected by three criteria:

1. Active product in May 2026: the vendor publishes pricing, the marketing site is current, and the product is reachable from a developer account in under five minutes.
2. AI-native or AI-significant: the tool either was built around LLM analysis from the start, or has shipped a non-trivial LLM-based feature that is part of the current pitch.
3. Distinct positioning: tools that effectively duplicate another entry's positioning were dropped, to keep the list useful rather than exhaustive.

Pricing references are stated at high level only. Vendor prices move every quarter; the official pricing page is always the canonical source. Free tiers are noted because they affect adoption, but feature gating shifts and is not detailed exhaustively.

No tool here paid for inclusion or placement, and the order within each category follows current market visibility, not endorsement.

The 14 tools at a glance#

The table below maps every tool to its primary category, pricing model, self-hosting story, and local-model support. "Local models" means the tool can run with an LLM hosted on the user's hardware (Ollama, LM Studio, vLLM, or an enterprise-hosted endpoint), with no code leaving the perimeter.

Tool	Category	Pricing	Self-hosted	Local models
CodeRabbit	Review	SaaS, free OSS tier	No	No
GitHub Copilot Code Review	Review	Bundled with Copilot seats	No	No
Greptile	Review (codebase-aware)	SaaS, per-dev	No	No
Qodo Merge	Review	Open source + paid SaaS	Yes (OSS)	Yes (via BYO key)
Bito	Review + IDE	Freemium + paid	No	No
Cursor BugBot	Review (in-IDE + PR)	Bundled with Cursor Team	No	No
CodeAnt	Review + SAST	SaaS	Yes (enterprise)	No
Anatoly	Audit	Open source (AGPL-3.0)	Yes (local CLI)	Yes (Ollama, LM Studio, any OpenAI-compatible)
Sourcegraph Cody	Audit-adjacent (search + AI)	Free + Enterprise	Yes (Enterprise)	Yes (Enterprise)
Augment Code	Audit-adjacent (codebase IDE assistant)	SaaS, per-dev	No	No
Snyk Code (DeepCode AI)	Specialty: security audit	Freemium + Enterprise	Yes (Enterprise)	No
Codacy	Hybrid: quality + AI	SaaS + self-hosted	Yes (Enterprise)	No
SonarQube (Sonar AI CodeFix)	Hybrid: SAST + AI	Free + commercial	Yes (Server)	Limited
Korbit	Review + mentor	Freemium + paid	No	No

The rest of this article walks each tool entry by entry: what it does, what it costs in practical terms, what it does well, and where it falls short. Read the section that matches your situation rather than top-to-bottom.

Review-first tools (run at pull-request time)#

These seven tools optimise for the PR moment: a developer opens or updates a pull request, the tool reads the diff, and it leaves inline comments and a summary. The deliverable is the PR conversation itself.

1. CodeRabbit#

CodeRabbit is the most visible AI PR-review product in 2026. It integrates with GitHub, GitLab, and Bitbucket, posts inline comments and a top-level summary on every PR, and lets reviewers chat with the bot to refine its take.

What it does well. The UX has been refined longer than most competitors. The PR-summary section, with its "changes" walkthrough and sequence diagrams for cross-file flows, is the artefact most teams remember. Path-level rules (.coderabbit.yaml) let a team scope what the bot says where, and the bot learns over time from accept/reject signals on its suggestions.

Pricing. Free for public repositories and individual open-source maintainers; paid tiers start in the mid-double-digit dollars per developer per month for organisations. Pricing details and the current free-tier limits live on the CodeRabbit pricing page.

Where it falls short. CodeRabbit is a PR-time review tool, period. It does not produce a report on an existing codebase. If your problem is "we inherited 80k lines of vibe-coded TypeScript and we do not know what is wrong with it", CodeRabbit will not help, because there is no PR to review. It is also cloud-only: code leaves your perimeter to reach the bot, which is a hard blocker for some regulated environments.

Best for. Teams that already merge through PRs and want the PR ceremony to catch more issues with less reviewer fatigue.

2. GitHub Copilot Code Review#

GitHub shipped a code-review feature as part of the broader Copilot suite during 2024 and matured it through 2025. Where Copilot Chat completes code in the editor, Copilot Code Review reads PR diffs and posts review comments through the native GitHub review UI.

What it does well. Zero setup if Copilot is already paid for. Reviewers see Copilot's comments alongside human comments, in the same thread, with no separate dashboard. For organisations already on Copilot Enterprise, there is no procurement step.

Pricing. Bundled with Copilot Pro, Business, and Enterprise. The current bundling and per-seat economics are on the GitHub Copilot pricing page.

Where it falls short. The feature is less customisable than dedicated review tools: rule files, path-scoped behaviour, and team-specific conventions are thinner. GitHub-only, by definition. Like every cloud SaaS, code is sent to a vendor endpoint to be analysed.

Best for. Teams already on Copilot who want a "good enough" PR-review layer without buying a second tool.

3. Greptile#

Greptile builds a graph of the entire codebase, then uses that graph as context when reviewing pull requests. The pitch is "review with whole-repo awareness", which addresses the most common failure mode of pure-diff review: a change that is locally correct but breaks an invariant established somewhere else in the codebase.

What it does well. Codebase-graph context produces review comments that reference functions and types in other files, not just the diff. Greptile also offers a chat-with-codebase product alongside the PR reviewer, which leans toward audit-adjacent territory without claiming to be an audit tool.

Pricing. SaaS, per-developer. The current rate is on the Greptile pricing page.

Where it falls short. Still cloud-only. Still oriented around the PR moment rather than producing a standalone audit deliverable. The graph index needs to be kept fresh, which adds latency for very large repositories.

Best for. Teams whose PR review fails not because reviewers miss the line but because they miss the context.

4. Qodo Merge#

Qodo Merge (formerly Codium PR-Agent) is the most popular open-source AI PR reviewer in 2026. The reviewer logic is a Python repository under github.com/qodo-ai/pr-agent, the prompts are inspectable, and operators can run the agent with their own LLM provider keys.

What it does well. Open source, inspectable, self-hostable. Multi-LLM by design (OpenAI, Anthropic, Azure OpenAI, Google, and any OpenAI-compatible endpoint). Operators can patch prompts to match team conventions instead of negotiating with a vendor roadmap. Qodo also offers a paid hosted tier for teams that want the convenience without the operations.

Pricing. The open-source agent is free; the hosted Qodo Merge SaaS has per-seat pricing on the Qodo pricing page.

Where it falls short. Self-hosted setup is heavier than installing a GitHub App. The UX is less polished than CodeRabbit out of the box, and the documentation assumes some comfort with Python and GitHub Actions.

Best for. Engineering teams with the appetite to host their own agent, especially in environments where the choice of LLM provider matters (data residency, compliance, cost).

5. Bito#

Bito blends an in-IDE AI assistant with a PR-time AI code reviewer. The pitch is "one product for both jobs": developers chat with the assistant in their editor, and the same product posts PR reviews on GitHub or GitLab.

What it does well. The combined IDE + PR positioning reduces tool sprawl for small teams. Free tier is generous for individuals trying the product.

Pricing. Freemium with paid Team and Enterprise tiers, listed on the Bito pricing page.

Where it falls short. The combined positioning means Bito's PR review is less specialised than a pure-play reviewer like CodeRabbit, and its IDE assistant is less specialised than Cursor or Copilot. Cloud-only.

Best for. Small teams that want one paid tool covering both jobs rather than two.

6. Cursor BugBot#

Cursor, the AI-native IDE that overtook many traditional code editors during 2024 and 2025, added a PR-review feature called BugBot. It uses Cursor's existing repo index to review pull requests on GitHub, with the goal of "the same agent that wrote the code reviews it".

What it does well. Continuity for teams already on Cursor: same underlying index, same model preferences, same workspace conventions. BugBot's review tends to track Cursor's overall code-understanding capabilities.

Pricing. Bundled with Cursor Team and Business tiers; details on the Cursor pricing page.

Where it falls short. Tied to the Cursor ecosystem. Teams not on Cursor do not gain much from adopting it for the review feature alone. Cloud-only.

Best for. Teams already standardised on Cursor.

7. CodeAnt#

CodeAnt is a newer entrant that combines AI PR review with traditional static analysis (SAST). The pitch is "one CI gate for security and quality", reducing the typical situation where Snyk catches one class of issue and CodeRabbit catches another.

What it does well. Combined SAST + AI review in one tool simplifies CI configuration. Enterprise self-hosted option for organisations that cannot send code to a vendor SaaS.

Pricing. SaaS with enterprise self-hosting available. See the CodeAnt pricing page for the current tiers.

Where it falls short. Smaller community and ecosystem than the older players. The SAST core is solid but is not a replacement for a mature, language-specific SAST in security-critical contexts.

Best for. Teams that want a single CI gate combining security and AI review, and that are comfortable adopting a younger product.

Audit-first tools (run against existing codebases)#

These three tools work backwards: the code exists, you point the tool at it, the tool reports on what it found. The deliverable is a report or a queryable index, not PR comments.

8. Anatoly#

Anatoly is an open-source (AGPL-3.0) code-audit agent that operates on existing codebases. It runs locally as a CLI, evaluates each source file along seven axes (utility, duplication, correction, overengineering, tests, best_practices, documentation), and produces a Markdown report with a verdict scoreboard and an evidence chain back to specific files and lines.

What it does well. Anatoly is the only tool in this list whose first-class job is "audit a codebase that already exists". The first run is heavy: every file is evaluated, every axis is a separate LLM call, and the report can run tens of pages on a real project. Every subsequent run is incremental at near-zero token cost: a SHA cache skips unchanged files, a watch mode re-audits on save, and a Claude Code PostToolUse hook re-audits exactly the files that an in-IDE coding session touched.

Three further capabilities separate it from PR-time reviewers:

• ANATOLY.md team-convention file. A markdown file dropped at the repository root tells the LLM your team's conventions, your priorities, your "we accept this trade-off because" exceptions. The agent calibrates its judgments against those conventions instead of an opinionated default. Auditors working under client NDA use it as a kickoff deliverable that codifies what the client considers acceptable.
• Local-model support. Anatoly speaks to any OpenAI-compatible endpoint, including Ollama, LM Studio, and self-hosted vLLM. Local RAG embeddings (Jina CPU or GGUF GPU) round out a fully air-gapped audit pipeline. The use case is described in the local-LLM code-audit guide.
• Self-documenting via anatoly docs scaffold. A separate command reverse-engineers the codebase and writes a structured documentation tree under .anatoly/docs/. On every subsequent run, those docs become business context for the audit (RAG-indexed, gap-detection at no extra token cost). A public example is the anatoly-bench slot-engine docs.

Pricing. Anatoly is free (AGPL-3.0). The cost is LLM tokens consumed by the audit. The first run on a small project is in the low single dollars; on a 50k-line repo it can reach the low tens of dollars. Every subsequent run on unchanged files is $0, by design.

Where it falls short. Anatoly is a CLI, not a SaaS. There is no dashboard yet (the report is a report.md plus per-axis JSON). Heavy first run on large repositories, which is the point but is honest to flag. AGPL-3.0 has implications for commercial reuse that some organisations need to evaluate.

Best for. Tech leads inheriting an existing codebase, auditors producing defensible findings under NDA, solo founders triaging a codebase before scaling, and engineers who want a continuous quality co-reviewer in their IDE rather than at the merge gate. Use cases include tech due diligence, Claude Code audit hooks, and vibe-coded-codebase recovery.

9. Sourcegraph Cody#

Sourcegraph has long been the dominant code-search product for large engineering organisations. Cody is its AI layer: code search and natural-language Q&A across an indexed codebase, with editor integrations and a chat UI.

What it does well. Codebase-wide search and Q&A is exactly what an inheriting maintainer needs in the first hour. "Where is the auth middleware?", "What calls this function?", "Summarise this service" are first-class queries. Enterprise self-hosting and local-model support are available on the Enterprise plan.

Pricing. Free for individuals, Enterprise pricing on request from the Sourcegraph pricing page.

Where it falls short. Sourcegraph is search-and-Q&A first, audit-as-deliverable second. There is no equivalent to "produce a Markdown report with a verdict per file and an evidence chain back to defects". Audit-shaped output has to be assembled by hand from interactive queries.

Best for. Large organisations that need codebase-wide AI search and are willing to assemble audit-style answers themselves from query results.

10. Augment Code#

Augment Code positions itself as a deeply codebase-aware AI assistant: an IDE coding partner that maintains a continuously updated understanding of your entire repository, not just the open file.

What it does well. The codebase-awareness pitch is genuine. Augment's recent PR-review and codebase-Q&A features are competent. The IDE assistant is the strongest piece.

Pricing. SaaS, per-developer. See the Augment pricing page.

Where it falls short. The audit-shaped output (report, evidence chain) is not Augment's deliverable. Cloud-only, which is a blocker for regulated work.

Best for. Teams that want codebase-aware AI primarily as a daily IDE assistant, with audit-adjacent capability as a side benefit.

Hybrid and specialty tools#

These four tools live at the intersection of categories or in a specialty niche. They overlap with both review and audit in different proportions.

11. Snyk Code (DeepCode AI)#

Snyk Code is the AI-enhanced static-analysis component of the broader Snyk platform, descended from the DeepCode acquisition. Its specialty is security-focused code audit: vulnerability detection, taint analysis, secure-coding violations.

What it does well. Snyk has a decade of security expertise and an integration story with most CI providers, package managers, and registries. The "DeepCode AI" layer adds LLM-assisted explanation and fix suggestions to traditional SAST results.

Pricing. Freemium with paid Team, Business, and Enterprise tiers. Details on the Snyk pricing page.

Where it falls short. Security only. Snyk Code will not tell you a function is overengineered, dead, or duplicated. It will tell you it has an SQL-injection vulnerability.

Best for. Security audits and CI security gates. A separate tool is still needed for code-quality audit or PR review.

12. Codacy#

Codacy is a long-running code-quality platform that combines aggregated linter and static-analysis results with dashboards and trend reporting. AI-enhanced findings and suggestions were layered on through 2024 and 2025.

What it does well. Multi-language quality dashboards, historical tracking, team-level adoption is straightforward. The platform integrates with CI and pull requests, so it covers both moments with one configuration.

Pricing. SaaS with self-hosted Enterprise option; pricing on the Codacy pricing page.

Where it falls short. Codacy's core is aggregated static analysis; the AI layer enhances findings rather than producing audit-shaped output natively. Teams looking for "a report I can hand to a client" still need to export and reformat.

Best for. Engineering organisations that want dashboards and trend lines first, with AI-assisted findings as a layer on top.

13. SonarQube + Sonar AI CodeFix#

SonarQube is the enterprise-default static-analysis tool in many large organisations. Sonar AI CodeFix, shipped during 2024, adds AI-generated remediation suggestions on top of SonarQube findings.

What it does well. SonarQube has the deepest enterprise integration of any tool in this list: it sits in the CI quality gate, it integrates with every major IDE, and it has language coverage that newer products are still catching up to. The AI CodeFix layer reduces the "now what?" gap between finding a violation and fixing it.

Pricing. Community Edition is free; Developer, Enterprise, and Data Center editions have commercial pricing. Sonar AI CodeFix is a paid add-on. See the SonarQube pricing page.

Where it falls short. The Sonar product is rule-based static analysis with an AI assist, not an LLM-native audit. Coverage is excellent for traditional defects (smells, bugs, vulnerabilities) and limited for the kind of judgment calls an LLM is good at (overengineering, duplication that no rule encodes, missing tests of a particular class).

Best for. Enterprises that already gate CI on SonarQube and want LLM-assisted remediation on top of rule-based findings.

14. Korbit#

Korbit is an AI mentor and PR reviewer with an explicit educational angle. The pitch is "the bot does not just flag, it explains, so junior developers learn from each review".

What it does well. The educational framing produces review comments that are longer and more pedagogical than CodeRabbit's terser style. For teams with a high proportion of junior engineers, that depth helps.

Pricing. Freemium and paid tiers on the Korbit pricing page.

Where it falls short. Smaller market presence than CodeRabbit or Copilot Code Review. The pedagogical depth is double-edged: senior engineers may find the comments verbose. Cloud-only.

Best for. Teams that want PR review to double as mentorship for junior contributors.

Five questions to ask before choosing#

The 14 tools above cover most of the 2026 landscape, and no single one is "best" for every team. Five questions narrow the choice fast.

1. Is the problem a new pull request, or an existing codebase? If the answer is "the code we are about to ship", you want a review-first tool (CodeRabbit, Copilot Code Review, Greptile, Qodo Merge). If the answer is "the code we already have, and we do not fully understand it", you want an audit-first tool (Anatoly, Sourcegraph Cody, Augment Code) or one of the hybrids. The wrong choice here wastes the most effort.

2. Does code need to stay inside your perimeter? Regulated industries, NDA work, and government contracts often forbid sending source code to a SaaS endpoint. That eliminates most of the list and leaves: Anatoly (local-first), Qodo Merge (self-hosted with BYO local LLM), Sourcegraph Cody (Enterprise self-hosted), SonarQube Server, and CodeAnt Enterprise. Snyk Code Enterprise can be on-prem but its DeepCode AI layer historically requires cloud calls. Always verify the current architecture with the vendor; cloud dependencies move.

3. What is the deliverable? If the deliverable is "a report I hand to a client or my CTO", you want a tool whose first-class output is a structured audit report: Anatoly produces one out of the box, Snyk Code produces a security-shaped one, the others require glue. If the deliverable is "PR comments that block merge", every review-first tool qualifies.

4. Who is the user? A solo founder, a 5-person startup, a 50-person product team, and a 500-engineer enterprise have very different budget tolerances and procurement processes. The open-source options (Anatoly, Qodo Merge) and the freemium tiers (Sourcegraph Cody Free, Codacy Free) absorb the low end. CodeRabbit, Greptile, and Copilot Code Review absorb the middle. SonarQube and Snyk Code dominate the enterprise end through procurement inertia as much as feature parity.

5. Are you adopting a tool, or adopting a workflow? PR-review tools embed in an existing workflow (the team already opens PRs; the bot joins the conversation). Audit tools sometimes require a new workflow (running a CLI, parsing a report, tracking findings over time). That second adoption cost is real and is the most common reason audit-shaped tools fail to land in teams that bought them. Plan for it explicitly.

FAQ#

What is the difference between AI code review and AI code audit?#

AI code review runs at pull-request time on a diff: a developer opens a PR, the tool reads the changes, the tool posts inline comments. AI code audit runs against an existing codebase regardless of recent activity: the tool reads full files or the whole repository, and the deliverable is a report. The two jobs use the same underlying LLMs but target different moments and different artefacts.

Can one tool do both?#

A few tools attempt both (Greptile leans review with codebase context, Sourcegraph Cody leans search/audit with PR features). In practice, the depth on each side is uneven. Most teams that need both jobs end up running two tools rather than one half-good general-purpose tool.

Which AI code review tool is free?#

CodeRabbit is free for public repositories and individual open-source maintainers. Qodo Merge is free as a self-hosted open-source agent (you pay LLM tokens). Korbit, Bito, and Codacy have free tiers with reduced limits. Sourcegraph Cody has a free individual tier. GitHub Copilot Code Review is bundled with paid Copilot subscriptions; there is no separate free version.

Which AI code audit tool works without sending code to the cloud?#

Anatoly is the most direct answer: open-source CLI, OpenAI-compatible local endpoints (Ollama, LM Studio, vLLM), local RAG embeddings. Sourcegraph Cody on the Enterprise plan supports self-hosted models. SonarQube Server is self-hostable, but its AI CodeFix layer is a separate consideration; check the current architecture.

Are AI code reviewers accurate?#

Accuracy depends on the codebase, the model, and the task. PR-time review is generally easier than full-codebase audit because the unit of analysis is bounded (a diff). Audit-time analysis benefits from team-convention files (Anatoly's ANATOLY.md, CodeRabbit's .coderabbit.yaml, and similar) because the LLM otherwise applies opinionated defaults that may not match the team's accepted tradeoffs. Empirical work on prompt design (for example, the concision-discipline finding) shows that small prompt changes can shift recall noticeably; precision and recall on a specific codebase are best measured rather than assumed.

How much does an AI code audit cost in LLM tokens?#

For Anatoly on a small-to-medium TypeScript repo (~10k to 50k lines), the first run is typically a few dollars to a few tens of dollars in LLM cost, depending on the chosen model. Every subsequent run on unchanged files is free, due to the SHA cache. Other audit-style outputs (Sourcegraph Cody Q&A, Augment Code interactions) bill differently, usually per seat plus consumption.

Closing#

The "AI code review tools" category has matured to the point where most teams already have a default: GitHub Copilot Code Review for organisations on Copilot, CodeRabbit for everyone else who lives on GitHub or GitLab. The "AI code audit" category, defined as backward-looking analysis of existing codebases with a structured report deliverable, is younger and more open. The honest version of this guide is: the two questions to ask are not "which tool is best" but "which of the two jobs do I need right now", and "does code need to stay inside my perimeter". Once those are answered, the list of candidates is short, and the choice is easy.

For teams currently weighing an audit-shaped need, the Anatoly repository and the anatoly-bench public benchmark are the most direct way to see what audit-first output looks like. For teams in the PR-review category, the choice between CodeRabbit, Copilot Code Review, Greptile, and Qodo Merge usually comes down to two factors: where your code already lives (GitHub, GitLab, Bitbucket), and whether you can or want to self-host. Both factors are settled in a one-hour evaluation.

This list will be revisited in late 2026. Vendors and feature sets move quickly enough that a six-month update is the right cadence.