Goal-built vector databases
Pinecone, Weaviate, and Milvus concentrate on vector scale and latency; many enterprises pair them with operational databases after they want specialised retrieval at scale. That is nice when embedding and vector search is a key, massive‐scale workload, requiring excessive efficiency and superior vector options. The draw back is that it’s good to handle and function one other, separate database system.
Multi-model databases
SurrealDB is one concrete method to this convergence It’s an open-source, multi-model database that mixes relational, doc, graph, and vector knowledge with ACID transactions, row-level permissions, and dwell queries for real-time subscriptions. For AI workloads, it helps vector search and hybrid search in the identical engine that enforces firm governance insurance policies, and it affords event-driven options (LIVE SELECT, change feeds) to maintain brokers and UIs in sync with out additional brokers.
For a lot of groups, this reduces the variety of shifting components between the system of file, the semantic index, and the occasion stream.
