Prompt Caching Can Slash LLM API Costs by Up to 80% With Minimal Code Changes

Teams running large language models in production often pay full token-processing costs repeatedly for identical system prompts, tool definitions, and static context sent with every request. Prompt caching addresses this by allowing providers to store the attention state of a stable prompt prefix, so subsequent requests using that exact prefix are charged at roughly one-tenth the normal input token rate. The cost reduction is substantial: a 10,000-token system prompt sent across 1,000 daily requests can yield a 70–80% cost saving once caching is active. The technique requires prompts to be structured with static content at the top and dynamic, per-user content at the bottom, since even a single character change in the cached prefix invalidates the cache. Both major API providers support prompt caching natively, though their implementation syntax differs slightly.