Optimizing cost and latency with Amazon Bedrock prompt caching
Up to 90% savings on input tokens. Here's how to implement prompt caching in Bedrock.
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.
The key facts
8 to knowUp 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
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: 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…