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.

Wispr Flow: Dictation That Works Everywhere — Stop typing. Start speaking. 4x faster.

Stop typing. Start speaking. 4x faster.

I built CachePilot because too many AI apps quietly lose performance and cost efficiency from prompt drift, unstable request structure, and weak production controls. Most teams can see output quality, but they cannot clearly see whether their prompts are staying stable enough for cache reuse, whether policies changed behavior, or why costs moved. CachePilot sits in front of OpenAI and gives that layer real visibility and control. The goal was simple: make prompt stability, cache behavior, and governance measurable without forcing teams to store raw prompts. So CachePilot uses hash-first telemetry, receipts, BYOK, streaming support, and policy controls to help teams observe first and govern when ready.

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?

Wispr Flow: Dictation That Works Everywhere — Stop typing. Start speaking. 4x faster.

Stop typing. Start speaking. 4x faster.

CachePilot

Telemetry for OpenAI apps in production

Telemetry for OpenAI apps in production

Forum Threads

How are you handling prompt drift and cache fragmentation in production?

Forum Threads

How are you handling prompt drift and cache fragmentation in production?