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

Large language models (LLMs) are the foundation for generative AI and agentic AI applications that power many use cases from chatbots and search assistants to code generation tools and recommendation engines. As we have seen with growing database workloads, the rising use of AI applications in production is driving customers to seek ways to optimize cost and performance. Most AI applications invoke the LLM for every user query, even when queries are repeated or very similar. For example, consider an IT help chatbot where thousands of customers ask the same question, invoking the LLM to regenerate the same answer from a shared enterprise knowledge base. Semantic caching is a method to reduce cost and latency in generative AI applications by reusing responses for identical or semantically similar requests by using vector embeddings. As detailed in the Impact section of this post, our experiments with semantic caching reduced LLM inference cost by up to 86 percent and…