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cubic
AI code review platform from London that reviews every pull request against the whole codebase and runs nightly scans for bugs and vulnerabilities.
Fast-moving software teams with complex codebases who want an AI first reviewer that learns their conventions and auto-approves low-risk changes.
Teams that want a free reviewer for large volumes of code, or companies that need a long vendor track record.
Verdict
cubic is an AI code review company in London, United Kingdom, founded in 2025 by Allis Yao and Paul Sanglé-Ferrière and backed by Y Combinator. Teams such as n8n, Legora, Cal.com, and Firecrawl install it on GitHub so it reviews each pull request with context from the whole codebase, turns senior engineers' past comments into rules, and scans the codebase for bugs on a schedule. It matters because AI coding tools have multiplied the code waiting for review, and cubic clears routine issues before a human opens the diff.
Score Breakdown
How cubic scores in the categories that matter to its buyers.
Pricing
cubic publishes per-developer plans billed monthly, with a 20% discount on yearly billing.
| Plan | Price | What stands out |
|---|---|---|
| Team | $40 / developer / month | 40,000 reviewed lines per developer, 5 custom review agents, auto approvals |
| Pro | $99 / developer / month | 80,000 reviewed lines, fix with coding agents, codebase scans for 3 repos |
| Max | $200 / developer / month | Pro with 160,000 reviewed lines per developer |
| Enterprise | Custom | Contact sales |
The Field at a Glance
How cubic compares with other AI code review vendors we reviewed, by Overall Score and relative typical engagement cost.
cubic scores 7.7 in AI code review, under CodeRabbit (7.9), level with Greptile (7.7), and ahead of Qodo (7.3). Relative cost lands mid-band for this subcategory.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| Reviewing AI-written code | Strong | Every PR is checked against the whole codebase. |
| Enforcing team conventions | Strong | Rules are written in plain English and learned from past reviews. |
| Security bugs across endpoints | Strong | n8n engineers saw it flag a second vulnerable endpoint. |
| Nightly bug scans | Mixed | Codebase scans start on the Pro plan. |
| Very large free usage | Weak | Plans cap reviewed lines per developer. |
| Long vendor history | Weak | The company started in 2025. |
Who it’s for
Good fit
- Startups shipping daily with AI coding agents
- Open-source projects with many contributors
- Legal and fintech teams with strict review standards
Poor fit
- Teams that want a free reviewer
- Buyers who need a decade-old vendor
- Repos hosted outside GitHub
Review Excerpts
Below are excerpts from public reviews. Our team scoured public reviews, forums, and chat rooms to get a balanced view of customers' experience with this company. Paid reviews and pay-for-play sites such as Clutch were excluded.
“cubic is the first port of call for my team. Every engineer clears its comments before a teammate even opens the review.”
“cubic immediately improved our review process. PRs move faster and quality is up.”
“cubic gets us to a better review more quickly, nit-picks are gone, and over time you can feel the velocity increase.”
Each plan caps reviewed lines per developer, 40,000 a month on the $40 Team plan, and codebase scans only start on the $99 Pro plan.
Methodology
This page is an independent evaluation of cubic for buyers comparing options in ai code review. AI Industry Reviews accepts no sponsorships, advertising, or pay-for-placement fees. cubic did not pay for this review.
What we scored
The headline number is an Overall Score on a 0-10 scale. Eight criteria sit under it in two groups.
- Buyer outcomes (for ai code review)
- Bug-finding depth
- Custom rules and learning
- Workflow fit with GitHub
- Maturity and track record
- Company & commercial
- Innovation & product leadership
- Project management & communication
- Pricing
- Contract fairness
Pricing measures whether the price looks fair for the value delivered, including packaging and renewal friction that show up in real buying cycles.
Score Composition
| Input | Weight | What it covers |
|---|---|---|
| Reviews | 40% | A proprietary read of what practitioners say about likes, complaints, and day-to-day use, including public review sites, forums, and private chat rooms. Paid reviews and pay-for-play sites such as Clutch are out of scope. |
| Product | 35% | Hands-on look at screens and workflows. |
| Pricing | 15% | Whether the price looks fair for what you get. |
| Docs & training | 10% | Docs, tutorials, and training material. |
How we balanced the evidence
The Overall Score is the simple average of the eight criteria. Recommendation language follows that score and the fit pattern described above.
Scope
cubic is graded here as ai code review. Criteria scores can move as more review volume and product checks are added.