AWS Benchmarks Vector Store Selection for Amazon Bedrock Knowledge Bases

AWS published technical guidance establishing clear architectural trade-offs for vector storage within Amazon Bedrock Knowledge Bases, directly comparing Amazon OpenSearch Service (managed and serverless), Amazon Aurora PostgreSQL with pgvector, and Amazon S3 Vectors. The guidance provides workload-specific blueprints evaluating vector similarity, storage tiering, latency envelopes, and hybrid search mechanics. For DevOps and AI infrastructure teams, this framework addresses the complex operational fragmentation surrounding vector data management. The proliferation of specialized vector databases has often forced practitioners to maintain dual-write patterns and fragmented data governance layers. By standardizing how managed Bedrock pipelines interact with core AWS storage engines, practitioners gain a predictable decision matrix: leveraging S3 Vectors for massive-scale, cost-optimized vector storage (reducing vector storage costs by up to 90 percent); Aurora PostgreSQL with pgvector for co-locating relational data with transactional HNSW and IVFFlat indexes; or OpenSearch Serverless when demanding hybrid BM25 lexical ranking combined with k-NN vector search. This shift fits into the broader enterprise trend where vector search ceases to be an isolated niche tool and integrates directly into primary storage and cloud data layers. Over the past two years, hyperscalers and open-source ecosystems have systematically embedded vector indexing into relational, document, and object stores. The industry has moved past evaluating standalone vector databases on raw nearest-neighbor speed alone; modern production concerns now prioritize cost-to-serve at high query volumes, tenancy isolation, and operational simplicity. S3 Vectors' ability to decouple vector persistence from active compute reflects the wider push to make billions of embeddings economically viable without keeping memory-heavy nodes hot 24/7. In practice, cloud architects should evaluate their query concurrency and data lifecycle before provisioning a vector backend. Workloads requiring sub-20ms tail latencies and combined semantic and keyword filters remain best suited for OpenSearch clusters or optimized pgvector deployments with dedicated compute. Conversely, archival knowledge stores, batch retrieval systems, and multi-tenant agent memory with bursty, infrequent access patterns should target S3 Vectors to drastically cut idle infrastructure costs while preserving vector recall.