CachePilot sits between application logic and the OpenAI Responses API so teams can control cost, latency, output budgets, tool access, policy receipts, and hash-first telemetry without prompt or output storage by default.
curl https://api.cachepilot.clclabs.ai/v1/responses \
-H "X-CachePilot-Key: cp_live_proj_abc" \
-H "Authorization: Bearer sk-your-openai-key" \
-d '{
"model": "gpt-4.1",
"input": "Run the production support workflow",
"tools": [{"type": "file_search"}]
}'
< X-CP-Policy-Version: 4
< X-CP-Output-Budget-Applied: 800
< X-CP-Skills-Applied-Hash: a3f8...c1e2
< X-CP-Prefix-Hash: 7b2d...f491Every governed request carries policy version, output budget, skills hash, prefix hash, and other X-CP receipt headers.
Track request structure, drift, latency, token usage, and cache behavior without storing prompts or outputs by default.
Apply route-level output budgets and policy controls before requests reach the OpenAI Responses API.
Control shell, hosted tools, tool schemas, and skill pipelines with explicit project policy.
Your application supplies the OpenAI key while CachePilot applies policy and records content-free receipts.
Pin golden runs and compare future workflow hashes against known-good production behavior.
Point a selected production OpenAI path through CachePilot and keep the baseline path visible for comparison.
Set output budgets, tool access, cache hygiene rules, and receipt requirements for that workflow.
Review governed-vs-passthrough behavior across cost, latency, drift, and auditability before rollout.
Start with a technical teardown or a 14-day governed gateway pilot. The first decision should come from measured workflow data, not a generic demo.
Use the calculators and leak-audit tools before deciding which workflow to route through CachePilot.