"That doesn't mean Pinecone, Weaviate, Milvus, or the other purpose-built vector vendors are doomed, but it does call into question the premise behind their VC pitch decks. For most enterprise applications, vector support is a feature, one that should be tightly woven into an existing data estate. This matters because the hardest part of production AI isn't nearest-neighbor search: It's context. Proliferating data siloes"
Vector support should be treated as a feature integrated into the existing data estate for enterprise applications. The idea that traditional databases cannot handle vectors has become outdated because major database platforms now provide native vector storage and indexing. Oracle AI Database can store vector embeddings with HNSW and IVF indexes, SQL Server 2025 adds a native vector data type with vector search and indexes, MongoDB automates embeddings in Atlas Vector Search, and Postgres supports vectors via pgvector. Purpose-built vector vendors can still be useful, but the premise behind their growth narratives is weakened. The hardest production AI challenge is context, and creating additional databases increases data silos and operational complexity.
#vector-databases #enterprise-data-architecture #rag-and-retrieval #data-silos #vector-searchindexing
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