Cubic
AI code reviewer for complex codebases that checks every PR against your whole repo and scans for bugs nightly
If your bug pattern is cross-file — a rename that breaks a caller three directories away — Cubic's repository-wide context and nightly codebase scans are the argument for paying per developer: Team is $40/dev/mo monthly or $30/dev/mo billed annually, Pro $99 monthly or $79 billed annually. Test the claim on the free Starter tier's 20 PR reviews first. It is a weaker fit for solo devs who just want linting, and the metering bites fast: 40,000 reviewed lines per developer on Team, 80,000 on Pro. GitHub-only today — GitLab and Bitbucket are not supported yet.
Verified 6d ago · liveness 77/100 · cite: rightaichoice.com/tools/cubic
- Engineering teams on monorepos where bugs cross files and reviewers can't see the whole picture
- Platform and release teams that scan the whole codebase nightly before a launch
- Teams that want senior engineers' review standards captured as plain-English rules
- Open-source maintainers who can use the free 20-review Starter tier
- Solo developers on hobby projects with no formal review workflow
- Teams looking for a lightweight linter rather than AI review
- Organizations that require self-hosted or fully offline deployment
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip Cubic if your team works on GitLab or Bitbucket, or if you only want a linter rather than an AI reviewer that needs whole-repo context.
Reviewed lines are metered per developer — Team covers 40,000 and Pro 80,000, so a team reviewing large diffs can hit the cap before the month ends.
Team at $40/dev/mo monthly ($30 billed annually) is priced for small-to-mid engineering teams that want repository-wide review plus auto-approve. Pro at $99 monthly ($79 billed annually) suits teams needing nightly scans, fix PRs and Confluence/MCP. Max Pro at $200/dev/mo monthly is for very high review volume. Against cheaper diff-only reviewers Cubic costs more; against per-seat enterprise review platforms it undercuts on entry.
In short
Cubic — AI code reviewer for complex codebases that checks every PR against your whole repo and scans for bugs nightly. Best for Engineering teams on monorepos where bugs cross files and reviewers can't see the whole picture, Platform and release teams that scan the whole codebase nightly before a launch, Teams that want senior engineers' review standards captured as plain-English rules. Free to start; paid plans from $30/mo.
What's new in Cubic
Checked 6 days agoAcross the latest 5 updates: 3 feature updates and 2 changelog entries.
Merge queues and GitHub stacks
The Merge menu now works on branches requiring a merge queue, showing 'Queued to merge' while the PR waits; for a PR in a GitHub stack, one click merges it along with every unmerged PR below it.
Organization API keys and Members API
Admins can create API keys owned by the organization rather than a person, so seat-sync scripts survive an admin leaving, and can list members and toggle review seats and roles through the cubic REST API.
Improved auto-ultrareview high-risk classifier
The High-risk PRs policy for automatic ultrareviews now reviews the riskiest 15% of pull requests, down from more than 33%, and the classifier runs in typically under a second instead of about five.
Auto-approve after dismissing cubic's last issue
cubic re-checks approval when the last blocking issue is dismissed, resolving its thread or replying that the issue is wrong, and answers in the same thread with the rule or reason that stopped it.
Test files hidden by default in pull requests
PRs now open with test files hidden so the diff, file tree and line counts show implementation changes first, and cubic remembers the choice across pull requests.
What people actually say about Cubic — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
115 mentions across 7 sources (Hacker News, Product Hunt, App Store, Bluesky, Stack Overflow, GitHub, Lemmy) · researched Jul 5, 2026.
Average across the 7 sources that answered — each source counts once, not each post.
- +Detects multi-file, cross-PR bugs others miss.
- +Learns from your team's PR history and adapts standards.
- +Custom coding standards in plain English are easy to set.
- +Full codebase scanning with thousands of AI agents.
- +Integrates with project management tools like JIRA and Linear.
- −Lack of real user testimonials makes true quality unverified.
- −Pricing for Team tier ($30/dev/mo) may be steep for some.
- −Auto-fix feature risks introducing unintended bugs.
- −Dependence on senior engineer history may not suit all teams.
- −Setup and tuning for custom standards might take time.
- • Overages for PR reviews beyond plan limits may incur extra fees.
- • Custom training and advanced features may require Pro or Enterprise plans.
Viability Score
How well maintained and how widely used is Cubic? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- AI code review agent with inline feedback on every GitHub PR in seconds
- Repository-wide review context — flags changes that break code elsewhere, including in another repo
- Nightly codebase scans using thousands of AI agents to find bugs and security issues
- Automated triage: notify issue owners and create tickets for scan findings
- One-click 'Fix with cubic' commits from PR comments
- Background and coding agents that fix issues and resolve tickets when a fix merges
- Auto-create fix PRs on a schedule or before a big release
- Automatic PR descriptions and summaries from code changes
- Plain-English custom review rules enforced on every PR
- Learns from senior engineers' past PR review comments
- Custom agents to enforce team coding standards
- Intent/context from Jira, Linear, Asana, Notion, Confluence and linked GitHub Issues
- Ultrareview with intelligent diff ordering and grouped related changes
- Change-at-a-glance diagram of what changed before reading code
- AI chat and guided tours over a pull request
About Cubic
Cubic is an AI code review platform for engineering teams whose pull requests span many files. Install the GitHub App (2-click install, 7 days free, no credit card) and every new PR gets inline feedback in seconds, checked against your entire codebase and your team's rules — not just the diff in front of you. It flags changes that break code elsewhere, even in another repo, and writes AI PR descriptions explaining what a change actually does. Beyond PR review, Cubic runs thousands of AI agents overnight to scan your codebase for bugs and security issues, triages findings to owners and tickets, and can create fix PRs on a schedule or before a big release. One-click 'Fix with cubic' commits handle simple problems; background and coding agents handle harder ones and resolve the ticket when the fix merges. Review rules are written in plain English and enforced on every PR, and Cubic learns from your senior engineers' past review comments. It pulls intent from Jira, Linear, Asana, Notion, Confluence and linked GitHub Issues. You can review in GitHub, in the cubic.dev web app (swap github.com for cubic.dev in any PR link), in the desktop app for macOS, Windows and Linux, or in your terminal via the cubic CLI. Aimed at teams that can't afford bugs — Cal.com, n8n, Better Auth and Browser Use are on the customer list. SOC 2 Type I compliant, with AI providers contractually barred from training on your code.
Behind the Verdict
Cubic's differentiating claim is architectural rather than cosmetic: every PR is reviewed against the whole codebase, so it can flag a change that breaks code in another file or another repo, which diff-only reviewers structurally cannot do. That is reinforced by context plumbing — reviews read linked GitHub Issues (acceptance criteria are checked against the PR), Jira, Linear, Asana, Notion and Confluence, and it has access to library and framework documentation to check APIs and deprecations. Customization is unusually literal: you write review rules the way you would say them ('use design tokens', 'no process.env', 'migrations use lock_timeout') and Cubic enforces them on every PR, while learning from your senior engineers' past review comments turns their habits into rules. On throughput, auto-approve clears low-risk PRs and a 1-to-5 merge confidence score tells you which ones need a human; from October 2026 the high-risk classifier that triggers automatic ultrareviews targets the riskiest 15% of PRs, down from over 33%, so review quota goes further. Reviewing itself got more ergonomic: test files are hidden by default, Ultrareview groups related changes into reading order, related files are ordered logically rather than alphabetically, and a change-at-a-glance diagram previews the diff. The scan side is the second product: thousands of agents run nightly, file findings, notify owners, create tickets, and can open fix PRs, with scan findings filable as GitHub issues in private repos. Weaknesses are real. It is GitHub-only — GitLab and Bitbucket are explicitly not yet supported. Usage is metered in reviewed lines per developer with tier-gated custom agents and private wikis, codebase scans capped at 3 repos on Pro and absent from Team, and the MCP and Confluence integrations reserved for Pro and above. Cubic does not name the underlying models, saying only that it uses multiple providers with different models for different review tasks, so you are trusting the benchmark claim rather than verifying the stack. Large PRs are also bounded: if a PR has more than 200 eligible files, up to 200 highest-priority files are reviewed. It fits platform and release teams, monorepo shops, maintainers with a PR backlog, and any team standing up a gate for agent-generated PRs; it does not fit hobby projects, teams wanting a lightweight linter, or organizations requiring self-hosted deployment.
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Real-world workflow fit
Concrete scenarios for the personas Cubic actually fits — and what changes day-one when you adopt it.
They install the cubic GitHub App on the main repo and write a handful of plain-English rules; every incoming PR gets inline feedback checked against the whole codebase, low-risk PRs are auto-approved, and the rest land in the review inbox with a merge confidence score.
Outcome: Reviewers stop spending time on nit-picks and safe dependency bumps, and cross-file breakages surface before merge instead of in production.
They schedule a nightly codebase scan, which runs thousands of agents, files findings to the right owners as tickets, and creates fix PRs; harder issues go to background coding agents that close the ticket when the fix merges.
Outcome: Dormant bugs and vulnerabilities are found and assigned on a schedule rather than discovered during a release crunch.
Cubic learns from their past PR review comments and turns those patterns into rules, while the local cubic CLI applies the same rules in the terminal before anyone pushes.
Outcome: Their review standards run on every PR without them reviewing every PR, and juniors catch issues before opening one.
Use Cases
- Review every pull request with inline AI feedback that is checked against your whole codebase, not just the diff.
- Enforce team coding standards by writing review rules in plain English that Cubic applies to every PR.
- Run nightly codebase scans with thousands of AI agents to find dormant bugs and vulnerabilities.
- Auto-approve low-risk PRs and focus human review on the ones with a low merge confidence score.
- Let senior engineers' review comments become rules so their standards run on every PR.
- File codebase scan findings as GitHub issues, individually, in bulk, or automatically after each scan.
- Gate agent-generated PRs: have Claude Code, Codex or Cursor pick up cubic's comments and push fixes.
- Catch the same issues locally in the cubic CLI before opening a PR.
Limitations
- GitHub only: the docs state GitLab and Bitbucket are not supported yet, so non-GitHub shops cannot use Cubic today.
- Usage is metered in reviewed lines per developer — 40,000 on Team, 80,000 on Pro, 200,000 on Max — with tier-gated limits on custom review agents and private wikis.
- Codebase scans cover 3 repos on Pro and are not included in Team; Confluence and MCP integrations, AI coding tracking and the API, and premium support are Pro-and-above.
- Large PRs are bounded: if a PR contains more than 200 eligible files, cubic reviews up to 200 highest-priority files.
- The site says cubic uses multiple model providers with different models for different review tasks but does not name them, so which models power a given review cannot be verified from the published material.
as of 2026-10-02
Verification history
We have re-verified Cubic 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Cubic's pricing actually pencils out — and where peers do it cheaper.
Team at $40/dev/mo monthly ($30 billed annually) is priced for small-to-mid engineering teams that want repository-wide review plus auto-approve. Pro at $99 monthly ($79 billed annually) suits teams needing nightly scans, fix PRs and Confluence/MCP. Max Pro at $200/dev/mo monthly is for very high review volume. Against cheaper diff-only reviewers Cubic costs more; against per-seat enterprise review platforms it undercuts on entry.
Setup time & first value
How long it actually takes to get something useful out of Cubic — broken out by persona, not the marketing-page minute.
Installing the GitHub App is a 2-click flow and Cubic reviews new PRs automatically within minutes. First value: same-day for a solo reviewer; roughly an hour for a team that wants plain-English rules and auto-approve policies configured; a day or two for a release team wiring nightly scans, ticket creation and Slack notifications.
Switching to or from Cubic
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a diff-only AI reviewer: keep it installed during the trial, let Cubic review the same PRs on the free Starter tier, and compare which findings are cross-file rather than diff-local.
- →From a manual review checklist: translate each checklist item into a plain-English cubic rule and let it enforce them on every PR.
- →From a linting-only setup: run lint for style and cubic for cross-file logic, since cubic checks repository context rather than formatting.
- →From human-only review: enable auto-approve for low-risk PRs first and keep human approval for anything below your merge confidence threshold.
- ↗To a linter or static analysis tool: move formatting and style enforcement there and keep cubic only if you still need repository-wide logic review.
- ↗To a GitLab-native reviewer: only if GitHub support is a blocker, and only once that vendor covers your host.
Integrations
Resources & Guides
- Documentationcubic.dev
Docs · Cubic
Full product docs from cubic.dev
- Resourcecubic.dev
Changelog · Cubic
Helpful link from cubic.dev
- Documentationcubic.dev
Introduction · Cubic
Full product docs from cubic.dev
- Documentationcubic.dev
Ide · Cubic
Full product docs from cubic.dev
- Documentationcubic.dev
Mcp Server · Cubic
Full product docs from cubic.dev
Tutorials & Learning
YouTube returned 6 videos for “Cubic”, and we withheld 6: 6 could not be judged, because “Cubic” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Cubic.
Official links
Tools that pair well with Cubic
Common stack mates teams adopt alongside Cubic, with the specific reason each pairing earns its keep.
Mira
Mira is a self-hosted, Apache 2.0 AI code reviewer that indexes your whole repo and reviews pull requests with the LLM of your choice.
Ito AI
Runtime code review that builds and runs your app on every pull request to catch behavioral bugs static reviewers miss.
Moderne
Moderne is a deterministic code-change layer that sequences repositories into a Lossless Semantic Tree so transformations land identically everywhere.
Featured Head-to-Head Comparisons
Cubic vs Poolside Ai
For teams that ship fast and need automated, high-quality PR reviews with proactive bug catching, Cubic is the better choice—it's freemium, tops benchmarks, and integrates deeply with GitHub. For regulated enterprises requiring custom models, air-gapped deployment, and long-horizon agent planning, Poolside AI is unmatched. Choose based on your need for speed vs. security and customizability.
Cubic vs Bito
Choose Cubic if your team's top priority is catching deep, multi-file bugs during code review and maintaining high code quality with minimal reviewer overhead. Choose Bito if your organization relies heavily on AI coding agents (Cursor, Claude Code) and needs a live knowledge graph to provide cross-repo context for planning, design, and code generation.
Cubic vs Cognition Ai
Choose Cubic if your priority is code quality and catching hard-to-find bugs during PR review—it's the #1 AI code reviewer on benchmarks, learns your team's standards, and scans entire codebases proactively. Choose Cognition AI if you need an autonomous AI engineer to handle end-to-end tasks like bug triage, legacy modernization, and cross-platform builds, backed by a $10M productivity guarantee. For most teams, Cubic is the safer, more focused tool for preventing bugs; for large enterprises automating entire engineering workflows, Devin is transformative.
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