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Goal-built vector databases
Pinecone, Weaviate, and Milvus deal with 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 that you must handle and function one other, separate database system.
Multi-model databases
SurrealDB is one concrete strategy to this convergence It’s an open-source, multi-model database that mixes relational, doc, graph, and vector information 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 provides event-driven options (LIVE SELECT, change feeds) to maintain brokers and UIs in sync with out further brokers.
For a lot of groups, this reduces the variety of transferring elements between the system of report, the semantic index, and the occasion stream.
