Selecting a vector store for Amazon Bedrock Knowledge Bases
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The article explains how to choose among the three customer-managed vector-store backends supported by Amazon Bedrock Knowledge Bases: Amazon OpenSearch Service, Amazon Aurora PostgreSQL with pgvector, and Amazon S3 Vectors. It describes the role of vector databases in retrieval-augmented generation, where embedded document chunks are retrieved by semantic similarity and passed to a large language model as context. OpenSearch is presented as a strong fit for real-time product catalog search, especially when low latency, hybrid keyword-and-vector search, filtering, and aggregations are important. S3 Vectors is positioned for cost-sensitive deep-research and large-scale workloads, with elastic scaling, batch-oriented processing, and sub-second retrieval in the reported Wikipedia tests. Aurora with pgvector is recommended for customer-facing chatbots that need relational data integration and flexibility to compare HNSW and IVFFlat indexing, with both tested approaches delivering sub-50-millisecond median latency in the described workload. The selection guidance recommends evaluating latency, query patterns, hybrid-search requirements, and cost sensitivity, while noting that a tiered architecture can combine S3 Vectors with OpenSearch or Aurora for performance-critical subsets. Because the benchmarks used separate workloads rather than a direct apples-to-apples comparison, the article advises teams to test the options with their own data and query patterns.
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