Prompt Caching Can Cut LLM API Costs by Up to 99%, Here's How It Works

Prompt caching is a technique that stores a large language model's response to a request and serves it directly from memory when the same request is made again, eliminating redundant API calls and token costs. Two main types exist: exact-match caching, which returns a stored response when a request is identical, and provider prefix caching, which reuses shared prompt segments like system instructions across varied requests. Exact-match caching can deliver sub-millisecond response times and eliminate token costs entirely on cache hits, while prefix caching typically cuts 50–90% off the cost of the cached portion. A real-world example shows a customer support bot handling 50,000 daily requests on GPT-4o could reduce monthly costs from $15,000 to around $6,750 by combining both methods. However, caching is unsuitable for use cases requiring varied outputs, such as creative writing or highly personalized responses, where cache hits would produce incorrect or stale results.