Purpose-built vector databases, like Pinecone, Weaviate, and Milvus, excel in managing vector scale and latency, making them ideal for enterprises needing efficient vector search capabilities alongside operational databases. These systems cater to high-performance workloads that require advanced vector features, but they also necessitate managing a separate database system, which can complicate infrastructure.
Multi-model databases, such as SurrealDB, offer a compelling alternative by integrating relational, document, graph, and vector data into a unified platform. As an open-source solution, SurrealDB facilitates ACID transactions and row-level permissions, while supporting vector and hybrid searches. Its event-driven capabilities, including live queries and change feeds, ensure synchronization between systems without additional brokers. For teams seeking efficiency, multi-model databases significantly reduce architectural complexity, bridging the gap between records, semantic indexing, and event streaming, ultimately enhancing productivity and governance. This convergence streamlines AI workloads and improves overall data management.
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