ToolsSeptember 15, 2026via AWS Machine Learning Blog
Optimizing cost and latency with Amazon Bedrock prompt caching
Why it matters
Prompt caching is now practical for production workloads on Bedrock. Practitioners running repeated inference on the same context (RAG, multi-turn agent reasoning, tenant isolation) can materially reduce costs and latency — this post shows six implementation patterns.
Key signals
- Up to 90% reduction in input token costs with prompt caching
- Six implementation scenarios: message content, system prompt, tool definition, mixed TTL, tenant isolation, LangChain integration
- Uses Converse API
- AWS Bedrock feature post with practical patterns
- Six scenarios documented: message content, system prompt, tool definition, mixed TTL, tenant isolation, LangChain integration
- Uses Amazon Bedrock Converse API
- Addresses both cost and latency optimization
- Vendor tutorial with implementation patterns
The hook
Up to 90% savings on input tokens. Here's how to implement prompt caching in Bedrock.
Prompt caching in Amazon Bedrock can cut input token costs by up to 90% when you repeatedly send the same context to foundation models. This post walks through six practical prompt caching scenarios using the Converse API: message content, system prompt, tool definition, mixed TTL, tenant isolation,…