A lot of AI teams watch outputs closely, but have less visibility into whether prompt structure is staying stable enough for good cache reuse and reliable behavior over time. Curious how others are thinking about prompt drift, cache fragmentation, and governance in production. Are you measuring it directly, or mostly discovering it after cost or behavior starts moving?
CachePilot gives you production telemetry for OpenAI apps: cache behavior, request structure, and policy effects, without storing raw prompts. BYOK, normal streaming, and visibility into what is actually happening under the hood.
