Build a read-through semantic cache with Amazon OpenSearch Serverless and Amazon Bedrock

In the field of generative AI, latency and cost pose significant challenges. The commonly used large language models (LLMs) often process text sequentially, predicting one token at a time in an autoregressive manner. This approach can introduce delays, resulting in less-than-ideal user experiences. Additionally, the growing demand for AI-powered applications has led to a high volume of calls to these LLMs, potentially exceeding budget constraints and creating financial pressures for organizations.

This post presents a strategy for optimizing LLM-based applications. Given the increasing need for efficient and cost-effective AI solutions, we present a serverless read-through caching blueprint that uses repeated data patterns. With this cache, developers can effectively save and access similar prompts, thereby enhancing their systems’ efficiency and response times. The proposed cache solution uses Amazon OpenSearch Serverless and Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like ...