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Cut Amazon Bedrock Costs with a 3-Layer Caching Pipeline on AWS Lambda + ElastiCache
The author addresses the cost of using Amazon Bedrock for AI-powered applications, especially with repeated user queries. They present a three-layer caching pipeline built within a single AWS Lambda function, utilizing ElastiCache (Redis). The first layer employs hash-based caching for exact duplicate questions, providing the fastest retrieval. The second layer uses semantic similarity, converting prompts into vectors and comparing them to cached vectors to catch paraphrased questions. The third layer implements prompt compression, removing filler words to reduce token usage when a Bedrock call is necessary. The Lambda function's handler sequentially checks each cache layer, proceeding to Bedrock only on a miss and then storing the response and prompt's vector. Tests show the pipeline effectively reduces unnecessary Bedrock calls. The pattern is most effective with high query volumes, similar questions, and verbose prompts, while performance is optimized by incorporating a vector search for stored embeddings and CloudWatch metrics. The author recommends starting with hash caching and then progressively integrating the semantic and compression layers to optimize costs. This approach provides significant cost savings by minimizing Bedrock invocations.
