Optimizing cost and latency with Amazon Bedrock prompt caching
aws ·
Amazon Bedrock's prompt caching feature significantly reduces both latency and input token costs by storing snapshots of partially processed inputs for reuse. When users repeatedly send the same context, such as system prompts or long documents, to foundation models, this infrastructure-level solution can lower costs by up to 90 percent on cache hits. The mechanism works by placing a 'cachePoint' marker in requests; if subsequent requests match the cached prefix, the model skips redundant computation. Key operational factors include cache scope per AWS account, minimum token thresholds that vary by model, and configurable time-to-live periods. Pricing structures introduce specific categories for cache writes, which cost more than standard input, and cache reads, which are heavily discounted. The article details six practical implementation scenarios using the Converse API, ranging from basic message content caching to advanced multi-tenant isolation. These examples demonstrate how developers can optimize Retrieval Augmented Generation applications and agentic workflows without compromising prompt quality. By leveraging these patterns, organizations can achieve substantial savings while maintaining high-performance inference speeds.
aws