Prompt Caching in LLMs: Cut the Cost and Latency of Your AI Application

Learn how prompt caching works in the Anthropic, OpenAI, and Gemini APIs: what the cacheable prefix is, how to order your prompt to maximize hits, breakpoints with cache_control, TTLs and pricing, and the mistakes that silently invalidate the cache. With production-ready Python code and metrics to measure your hit rate. Extended edition: the KV cache under the hood, Anthropic automatic caching and the 1-hour TTL, OpenAI prompt_cache_key and 24h retention, Gemini implicit and explicit caching, the math of the savings, multi-tenant and cache-aware routing, RAG, agents and compaction, self-hosted with vLLM and SGLang, security, observability and alerts, a CI smoke test, a migration guide, anti-patterns, a full case study, and an FAQ.