Prompt Caching

Prompt Caching stores the intermediate KV-cache state for frequently-reused prompt prefixes — dramatically reducing latency and cost when the same large prompt appears across many requests. The technique is especially valuable for RAG systems where a long static prompt (system instructions plus retrieved knowledge base context) appears across many user questions. Anthropic introduced prompt caching for Claude in August 2024, offering up to 90% cost reduction and significantly lower latency for cached portions. OpenAI followed with automatic prompt caching for repeated prefixes. Google Gemini supports context caching for very large contexts. The pattern is foundational to economical operation of RAG, agents, and any application with large recurring prompt contexts. Tools and SDKs from major providers expose caching controls — typically requiring the cacheable content to be at the start of the prompt. AI governance, AI compliance, and AI risk management programs document cache hit rates as part of cost tracking — supporting responsible AI through visible efficiency metrics in enterprise AI deployments at scale.

Centralpoint Captures Cache Savings in Your Metering: Oxcyon's Centralpoint AI Governance Platform tracks both cached and uncached tokens across OpenAI, Gemini, Llama, and embedded models — showing real savings. Centralpoint keeps prompts and skills on-prem and embeds cache-aware chatbots into your portals via one JavaScript line.


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