What it does
Codeflash is built around cutting infrastructure cost and improving latency, memory and throughput. Its codeflash-agent works with the whole codebase in view, looks for optimizations, and the company's performance engineers audit every one before a pull request reaches your team. Each PR carries benchmark numbers and a rationale, and every change is run against your current tests as well as regression tests it generates. An engagement starts by choosing an objective such as cost, p99 latency, cold start or GPU utilization, and reproducing a baseline on a representative workload. The agent then explores in an isolated sandbox. Afterwards a continuous mode can review every new PR to catch regressions, and the site lists Claude Code, Cursor and GitHub as integrations. The site cites a 90% infrastructure cost cut at one customer and merged speedups in projects such as vLLM and Hugging Face Diffusers, and says it has done work on ML inference, training and data processing. It states that it is SOC 2 Type 2, never trains on your code, and can run as SaaS, in your cloud or on-prem. These results are the vendor's own claims.
Capabilities
- Agent that searches the whole codebase for cost, latency, memory and throughput gains
- Each change is benchmarked and verified with existing and generated tests
- Human performance engineers review each optimization before it reaches you
- Continuous mode that reviews new PRs for regressions
- Sandboxed execution, with your code never used for training
- SaaS, your cloud or on-prem deployment
When to use it
- Your cloud bill or latency is high and nobody has time for performance work
- You run machine learning inference or data pipelines in production
- You want code written by AI agents checked for slowdowns on every PR
