Improve speed and reduce cost for generative AI workloads with a persistent semantic cache in Amazon MemoryDB
Database Blog
This article discusses how to improve the speed and reduce the cost of generative AI applications by using a persistent semantic cache in Amazon MemoryDB.
Specifically, the article covers:
- The concept of a persistent semantic cache for storing vector embeddings and generated responses
- An overview of the solution architecture using Amazon MemoryDB, Amazon Bedrock, Knowledge Bases for Amazon Bedrock, and other AWS services
- Steps to deploy the solution using Terraform
- Testing the deployed solution and analyzing performance improvements with a semantic cache
- Cost savings calculations based on different cache hit ratios
- Conclusion and pointers for further learning
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