Prompt caching is an inference optimization where API providers store and reuse precomputed KV cache states for repeated prompt prefixes, reducing latency and cost for requests sharing common context.
Prompt caching is an inference optimization where API providers store and reuse precomputed KV cache states for repeated prompt prefixes, reducing latency and cost for requests sharing common context. When multiple API requests begin with the same system prompt or few-shot examples, the provider skips reprocessing those tokens entirely.
Every token in a prompt must be processed through the model's attention layers before generation begins. For a 10,000-token system prompt followed by a 100-token user message, the model spends 99% of its prefill computation on tokens that are identical across requests. Prompt caching stores the KV states for these shared prefixes, allowing subsequent requests to start generation immediately after the unique suffix is processed.
Anthropic, OpenAI, and Google all offer prompt caching with different mechanics. Anthropic caches exact prefix matches and charges 90% less for cached tokens with a 10% write surcharge. OpenAI automatically caches prefixes longer than 1,024 tokens with 50% cost reduction. The cache typically persists for 5-60 minutes depending on demand, making it effective for applications with sustained traffic patterns.
Prompt caching reduces both latency and cost for the most common production pattern: many requests sharing a large system prompt. Applications with 5,000+ token system prompts see 50-90% cost reductions and 2-5x faster time-to-first-token, transforming the economics of context-heavy agent systems.
A customer support agent with a 8,000-token system prompt (company policies, product catalog, response guidelines) serves 10,000 requests per hour. With prompt caching, the system prompt is processed once and reused across all requests, reducing per-request latency from 3 seconds to 400ms and cutting API costs by 85%.
Aaron is an engineering leader, software architect, and founder with 18 years building distributed systems and cloud infrastructure. Now focused on LLM-powered platforms, agent orchestration, and production AI. He shares hands-on technical guides and framework comparisons at fp8.co.