How Prompt Caching Works: Managing TTL, Refresh Cycles, and Cost Savings

Prompt caching stores AI model responses for a set duration to reduce latency and token costs, with platforms like Claude defaulting to a five-minute time-to-live window. Each time a cached prompt is reused within that window, the cache refreshes at no extra cost, making it efficient for high-frequency requests. Research suggests effective caching can cut input token costs by up to 90 percent compared to processing full prompts each time. Engineers must fine-tune TTL settings based on how frequently underlying data or prompts change, as a static window can produce stale or irrelevant responses. Advanced strategies such as randomized refresh delays and heartbeat mechanisms help prevent cache overload and maintain response freshness under variable workloads.