NEWS · CODING · #226
Optimizing cost and latency with Amazon Bedrock prompt caching
An AWS Machine Learning post describes prompt caching in Amazon Bedrock, saying it can cut input token costs by up to 90% when the same context is repeatedly sent to foundation models. The article walks through six practical prompt caching scenarios for the Converse API: message content, system prompt, tool definition, mixed TTL, tenant isolation, and LangChain integration.
KEY POINTS
- An AWS Machine Learning post describes prompt caching in Amazon Bedrock, saying it can cut input token costs by up to 90% when the same context is repeatedly sent to foundation models.
- The article walks through six practical prompt caching scenarios for the Converse API: message content, system prompt, tool definition, mixed TTL, tenant isolation, and LangChain integration.
- Prompt caching can materially reduce token costs and latency for repeated contexts in production apps and shows practical integration patterns (including LangChain and tenant isolation) for Bedrock users.
WHY IT MATTERS
Prompt caching can materially reduce token costs and latency for repeated contexts in production apps and shows practical integration patterns (including LangChain and tenant isolation) for Bedrock users.