Vector Search Is Becoming Data Infrastructure, Not a Standalone Database Choice

The growing adoption of retrieval-augmented generation (RAG) has led many teams to prioritize vector databases before establishing a proper data model, which experts argue is the wrong approach. Major cloud platforms are absorbing vector search into existing infrastructure: AWS has made S3 Vectors generally available, Google supports vector indexes in BigQuery, and Cloudflare Vectorize integrates retrieval with edge applications. This shift signals that specialized vector databases are not obsolete, but the assumption that every use case requires one is fading. Production systems demand answers to complex questions around data freshness, deletion ownership, tenant isolation, and security filtering — concerns that go far beyond picking an index. Vector search is increasingly being treated as a data infrastructure problem, with at least five distinct workload types — including hot product retrieval, permission-sensitive enterprise retrieval, and cost-sensitive cold archiving — each carrying different technical and operational requirements.