Reducing Amazon Bedrock Inference Costs with Amazon ElastiCache

Amazon ElastiCache and Amazon Bedrock can be used together to optimize the performance and cost of AI model inference by implementing caching strategies. Hash-based caching can quickly retrieve exact matches, while semantic caching can find similar responses based on embeddings. When these strategies are combined, they can significantly reduce the number of calls to the AI model, thereby lowering inference costs and improving response times.

In this lab, you will learn how to leverage Amazon ElastiCache and Amazon Bedrock to implement efficient caching strategies for AI model inference.

Learning objectives

Upon completion of this intermediate-level lab, you will be able to:

• Implement hash-based and semantic caching strategies using Amazon ElastiCache and Amazon Bedrock
• Configure Amazon ElastiCache to store and retrieve model responses using embedding-based semantic similarity
• Tune a similarity threshold to balance cache hit rate against response accuracy
• Compress prompts to reduce token consumption before inference

Intended audience

• Candidates for the AWS Certified Generative AI Developer – Professional (AIP-C01) exam
• Cloud Architects
• AI Developers

Prerequisites

Familiarity with the following will be beneficial but is not required:

• Amazon Bedrock
• Amazon ElastiCache
• AWS Lambda

The following content can be used to fulfill the prerequisites:

• Implementing Caching Strategies with Amazon ElastiCache
• Invoking Amazon Bedrock Models Using the Bedrock Runtime and AWS Lambda