Find real performance bottlenecks — and keep them fixed.
CodePerformance analyzes your GitHub PRs or branches to surface real performance issues — from inefficient algorithms to blocking I/O, database misuse, and scalability risks — and helps your team track, fix, and re-verify them iteratively.
It’s performance review as a shared workflow, not a one-time optimization pass.
Performance issues don’t fail at discovery. They fail at follow-through.
Most teams spot performance problems reactively:
Fixes get pushed, assumptions are made — and no one checks if things actually improved.
It turns performance optimization into a clear, trackable process, so:
Hot paths are identified early
Catch performance regressions before they ever touch production.
Ownership is clear
Assign issues to the right team members and track progress transparently.
Fixes are verified, not assumed
AI confirms if your changes actually solved the bottleneck.
Performance improves with every iteration
Turn optimization from a chore into a continuous improvement workflow.
Performance work shouldn’t be guesswork. Now it isn’t.
What CodePerformance gives you
1. AI-powered performance issue detection
Every selected PR or branch is reviewed for meaningful performance risks, including:
- Algorithmic inefficiencies
- Blocking I/O in hot paths
- Database and external call misuse
- Memory and allocation pressure
- Concurrency and contention issues
2. Actionable, impact-driven fixes
Every issue comes with:
- Clear explanation of why the code is slow or inefficient
- File name & precise line numbers
- Relevant code snippet
- Concrete, implementation-ready fix
- Expected performance improvement
3. A shared performance dashboard
All performance issues live in one place — visible to the entire team.
- Assign issues
- Track progress
- Discuss and resolve collaboratively
4. Iterative review & verification
Fixes don’t end the story.
When you push changes to GitHub and re-run a review:
- AI verifies whether performance issues are actually resolved
- Resolved issues are closed automatically
- Unresolved ones stay open for further action
5. Full issue history & accountability
Every issue keeps its full history:
- Assignee changes
- Status updates
- Review cycles
Your team always knows:
- What was slow
- What was fixed
- What still needs attention
How CodePerformance works
Five simple steps to a faster application.
1. Connect GitHub
Link your GitHub account and choose a repository you want to optimize.
2. Select PR, branch, or files
Review an entire PR, a specific branch, or choose specific folders and files.
3. AI reviews your code for performance
Issues are identified across algorithms, I/O, databases, and memory — with clear severity and fixes.
4. Assign & fix collaboratively
Track issues using statuses: open → in progress → resolved → closed / rejected.
5. Re-review and verify
Push fixes, re-run CodePerformance, and let AI confirm what's truly improved.
Who uses CodePerformance
Built for engineering teams who care about speed and efficiency.
Teams scaling fast
Catch performance regressions before they hit production and slow down growth.
Teams battling infra costs
Identify inefficient code driving unnecessary load and burning budget.
Teams owning critical paths
Keep APIs, workers, and pipelines fast under heavy production load.
Startups without specialists
Get senior-level performance reviews without hiring specialized experts.
Open-source maintainers
Review performance impact of contributions with shared team visibility.
See it in action
Explore AI-generated CodePerformance results from popular open-source projects — exactly how bottlenecks, fixes, and verification appear.
Next.js
AI-reviewed PRs highlighting bugs, security risks, and performance issues in a modern React framework.
FastAPI
AI code review for a high-performance Python API, with clear issue severity and fix suggestions.
Express.js
PR and branch reviews for Node.js web apps, surfacing reliability and performance concerns.
React
Component-level code reviews identifying potential bugs and optimization opportunities.
Socket.io
AI analysis of real-time communication code, flagging edge cases and performance risks.
NestJS
Structured backend code reviewed for correctness, security, and maintainability.
Django REST Framework
AI-powered review of enterprise-grade APIs, highlighting security and logic issues.
Pandas
Function-level reviews for complex data processing code, with performance and correctness insights.
Hugo
Static site generator code reviewed for bugs and inefficiencies across core logic.
Rustlings
Educational Rust code reviewed for correctness and best-practice improvements.
Terraform
Infrastructure-as-code reviews identifying configuration risks and performance concerns.
ZAP (OWASP)
Security automation code reviewed with a focus on vulnerabilities and defensive gaps.
Pricing
Usage-based. Team-friendly. No seats. No contracts.
(50% off during early access)
- First 200 files reviewed free
- Unlimited users
- Pay only when AI reviews
Your whole team gets access.