AWS Bedrock simplifies access to powerful foundation models, while Couchbase's vector store capabilities provide the storage and retrieval efficiency needed to build high-performance AI applications. By combining these two components, businesses can create scalable, cost-effective, and efficient AI solutions that can handle large-scale vector search efficiently. The integration of AWS Bedrock with Couchbase enables seamless access to foundation models, bridging the gap between Large Language Models (LLMs) and enterprise data, and providing a seamless integration with other AWS services like Lambda, S3, and API Gateway. This combination also leverages serverless architectures, which provide zero infrastructure management, auto-scaling, and cost efficiency, making it an attractive solution for AI-powered search, recommendation, and knowledge retrieval applications.