How to Implement Prompt Caching on Amazon Bedrock and Cut Inference Costs in Half

Introduction You're running a multi-turn support agent on Amazon Bedrock. Every API call sends a ~2,100-token system prompt — your agent's persona, rules, and the product documentation — along with the growing conversation history. The model doesn't remember any of this between calls. It reprocesses those tokens fresh every single turn, and you pay for every one of them. For a single five-turn conversation on Nova Pro, that adds up to 12,834 input tokens. Over 80% of that is the static system prompt, repeated identically across all five turns. Scale to 1,000 conversations a day and your monthly bill hits $384. Most of that is money spent processing the same static text, over and over. Amazon Bedrock's prompt caching fixes this. You mark a cache point in your prompt where the static content ends. Bedrock stores everything before that marker. On subsequent calls within the cache window, it reads from cache instead of reprocessing. Cache reads cost 90% less than regular input tokens. I ra