InfoWorld AI·
Every hot, new workload gets its own database. Briefly. You know the drill. From search to JSON (documents) to graph, as an industry we have this weird fixation with building new databases. DB-Engines now tracks 434 of them. We’re now doing it again with vector databases, which were hailed almost overnight as the essential new persistence layer for AI. The story was simple and, for a time, convincing: Traditional databases don’t understand vectors! AI applications need vectors! So AI applications must need vector databases. Right? Nope. After all, that first premise became untrue almost immediately. I would say that, wouldn’t I? I mean, I work for Oracle, and Oracle AI Database 26ai can store vector embeddings alongside business data, and it supports HNSW and IVF vector indexes. But it’s not just Oracle. Literally every database that developers have used for years has vector support now. Microsoft has added a native VECTOR data type to SQL Server 2025, along with vector search and vect
Read full articleVector databases are now core retrieval infrastructure for RAG and agentic AI. This guide compares nine production options on architecture, pricing, and scale. The post Best Vector Databases in 2026: Pricing, Scale Limits, and Architecture Tradeoffs Across Nine Leading Systems appeared first on MarkTechPost.
