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Codeflash

Agent that benchmarks and verifies Python performance optimisations before proposing them as reviewable diffs

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freemiumFree is $0 with 25 function-optimization credits per month, community support, public GitHub projects only, local tracing only, and AI training permitted. Pro is $20 per user per month with a 14-day free trial, 500 optimization credits per user per month, advanced optimizations and developer code-quality metrics, priority support, private GitHub repositories, a user dashboard and analytics, a zero-data-retention policy and no AI training. Enterprise is custom-priced with unlimited optimization credits, admin usage analytics, on-premises deployment, 24/7 premium support, custom SLAs on suggested optimizations and onboarding services.View full pricing →

Visit Codeflash

https://codeflash.ai

About Codeflash

Codeflash automates Python performance engineering, which is normally the most senior and least scalable kind of code review a team can ask for. Its agent generates candidate rewrites of a function, benchmarks each one against the original, and — critically — verifies that the fast version still returns the same results before proposing it. That verification step is what separates it from a general coding assistant asked to 'make this faster': the optimisation is only surfaced if behaviour is provably unchanged under the benchmark harness. Optimisations arrive as reviewable suggestions rather than silent rewrites, and the free tier is deliberately scoped to public GitHub projects with local tracing so open-source maintainers can use it without a contract. The paid product adds private repositories, deeper optimisation classes, developer code-quality metrics, and a dashboard showing where the wins accumulated. Codeflash also sells the outcome rather than the tool for larger teams: the company pairs the agent with its own performance engineers who review and ship every change, and publishes case-study numbers including a claimed 90% infrastructure cost reduction and 9.2× pod density at one customer. Data posture is spelled out per tier — the free plan permits AI training on your code, while both paid tiers carry a zero-data-retention policy and no training, ever.

Key Features

Correctness verification before an optimisation is proposed
Benchmark-driven candidate selection rather than heuristic rewrites
GitHub integration with reviewable suggestions
Local tracing to capture real call patterns
Zero-data-retention and no-AI-training on both paid tiers
On-premises deployment on Enterprise
Human performance-engineer review available as a service

Codeflash Pros & Cons

Pros

  • +Verifying behaviour before suggesting a speedup is the whole ballgame, and few competitors do it
  • +Free tier is genuinely usable for open-source maintainers
  • +Cost and latency framing gives the output a number a finance team recognises
  • +Explicit no-training posture on paid plans

⚠️ Cons

  • Python-focused, so polyglot codebases get partial coverage
  • The free tier permits training on your code and is limited to public repos
  • Credit metering means large refactors need the Pro or Enterprise tier

Tags

pythonperformancebenchmarkingcode-optimizationgithubci-cd
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