Prompt Caching Can Cut LLM API Costs by Up to 75% in Production Apps

Large language model API costs scale rapidly in production, as developers are billed for the same input tokens repeatedly across thousands of daily requests. Prompt caching addresses this by storing the computed state of a static prompt prefix on the provider's servers, so repeated requests sharing that prefix are charged at roughly 90% less for those cached input tokens. The technique can reduce overall token costs by 50–75% on heavy workloads, though output token pricing remains unchanged. A critical requirement is that all static content — system prompts, few-shot examples — must appear at the very beginning of the prompt, since any variation in that prefix invalidates the cache entirely. Developers are advised to keep dynamic elements such as user queries and session data strictly at the end of the prompt structure to maximize cache hits.