Purpose-built vector databases
Pinecone, Weaviate, and Milvus focus on vector scale and latency; many enterprises pair them with operational databases when they need specialized retrieval at scale. This is great when embedding and vector search is a key, large‐scale workload, requiring high performance and advanced vector features. The downside is that you need to manage and operate another, separate database system.
Multi-model databases
SurrealDB is one concrete approach to this convergence It’s an open-source, multi-model database that combines relational, document, graph, and vector data with ACID transactions, row-level permissions, and live queries for real-time subscriptions. For AI workloads, it supports vector search and hybrid search in the same engine that enforces company governance policies, and it offers event-driven features (LIVE SELECT, change feeds) to keep agents and UIs in sync without extra brokers.
For many teams, this reduces the number of moving parts between the system of record, the semantic index, and the event stream.
