AWS compares Bedrock RAG vector stores: OpenSearch vs pgvector vs S3 Vectors
AWS ML Blog · rss · 2026-09-17
AWS ML Blog compares the three customer-managed vector store backends for Amazon Bedrock Knowledge Bases across real RAG use cases.
- Amazon OpenSearch Serverless suits e-commerce catalog search with hybrid semantic + keyword retrieval, low-millisecond latency, and faceted filtering.
- Amazon Aurora PostgreSQL with pgvector (IVFFlat/HNSW, up to 2,000 dimensions) fits structured data with vector search needs.
- Amazon S3 Vectors targets large-scale cost-sensitive workloads, cutting vector storage costs up to 90% with sub-second queries.
- The post also quantifies OpenSearch optimizations: embedding sizes (1024/512/256), binary embeddings, and disk-optimized storage, noting benchmarks used Serverless Classic collections since NextGen isn't yet compatible with the Bedrock Retrieve API.
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