Lower cost and latency for AI using Amazon ElastiCache as a semantic cache with Amazon Bedrock

This article explains how to implement semantic caching using Amazon ElastiCache for Valkey with Amazon Bedrock to reduce AI application costs and latency.

• Semantic caching reuses LLM responses for identical or semantically similar queries using vector embeddings
• Experiments showed up to 86% LLM cost reduction and 88% latency improvement with semantic caching
• ElastiCache for Valkey provides microsecond-latency vector search with 95%+ recall at best price-performance
• Solution uses Amazon Titan embeddings, Amazon Nova Premier LLM, and LangGraph orchestration
• Cache hits return millisecond responses without LLM inference; cache misses invoke LLM and store results
• Evaluation on 63,796 real chatbot queries achieved 91% accuracy at 0.75 similarity threshold
• Best practices include caching stable responses, handling multi-turn conversations, setting TTLs, and personalizing outputs

Semantic caching with ElastiCache enables significant cost and performance improvements for generative AI applications by intelligently reusing cached responses for similar queries.