# Optimizing cost and latency with Amazon Bedrock prompt caching

DevFeed: [Optimizing cost and latency with Amazon Bedrock prompt caching](<https://devfeed.tech/articles/optimizing-cost-and-latency-with-amazon-bedrock-prompt-caching-26941.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/optimizing-cost-and-latency-with-amazon-bedrock-prompt-caching/>)

Author: Daniel Abib

Published: 2026-09-15T16:18:19Z

Content type: tutorial

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [Amazon Bedrock](<https://devfeed.tech/topics/amazon-bedrock.md>), [Caching](<https://devfeed.tech/topics/caching.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [API](<https://devfeed.tech/topics/api.md>), [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [LangChain](<https://devfeed.tech/topics/langchain.md>), [Multi-tenancy](<https://devfeed.tech/topics/multi-tenancy.md>)

Tags: [advanced-300](<https://devfeed.tech/tags/advanced-300.md>), [amazon-bedrock](<https://devfeed.tech/tags/amazon-bedrock.md>), [api](<https://devfeed.tech/tags/api.md>), [caching](<https://devfeed.tech/tags/caching.md>), [foundation-models](<https://devfeed.tech/tags/foundation-models.md>), [integration](<https://devfeed.tech/tags/integration.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [latency](<https://devfeed.tech/tags/latency.md>), [pricing](<https://devfeed.tech/tags/pricing.md>), [technical-how-to](<https://devfeed.tech/tags/technical-how-to.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

## AI overview

This AWS post explains how Amazon Bedrock prompt caching can reduce repeated input-token costs by up to 90 percent and lower time to first token when requests reuse the same context. It presents six scenarios using the Converse API, including document, system prompt, tool definition, mixed TTL, tenant-isolated, and LangChain caching.

## Source 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 isolation, and LangChain integration.