Bedrock workloads commonly include repetitive inference patterns—such as classification results, prompt templates generating deterministic outputs, FAQ responses, document tagging, and other predictable or low-variability tasks. Without a caching strategy (API-layer cache, application cache, or hash-based prompt cache), these workloads repeatedly invoke the model and incur token costs for answers that do not change. Because Bedrock does not offer native inference caching, customers must implement caching externally. When no cache layer exists, cost increases linearly with repeated calls, even though responses remain constant. This issue appears most often when teams treat all workloads as dynamic or generative, rather than separating deterministic tasks from open-ended ones.
Bedrock charges for tokens (or inference units) per request. Repeatedly invoking a model with identical or highly similar prompts generates full cost each time. Caching can eliminate unnecessary calls and reduce both cost and latency.
