Vector DBPaid

SINGLESTORE

Distributed SQL database with native vector search for AI

ABOUT

Building AI applications that need both fast transactional queries on structured data AND vector similarity search typically requires maintaining two separate database systems — one for operational data (PostgreSQL, MySQL) and another for embeddings (Pinecone, Qdrant). This dual-database architecture introduces data synchronization complexity, increased operational overhead, and higher latency for combined queries. SingleStore solves this by providing a single SQL database that natively supports vector search alongside transactions and real-time analytics, so developers can store customer records, product inventory, and vector embeddings all in one place and query them with standard SQL.

INSTALL

pip install singlestoredb

INTEGRATION GUIDE

1. Store customer profiles and their embedding vectors in the same SQL table for unified transactional and semantic queries 2. Build real-time recommendation systems that filter by structured criteria (price, category, availability) while ranking by vector similarity 3. Run hybrid search queries that combine full-text search with vector similarity in a single SQL statement for more accurate RAG retrieval 4. Ingest streaming data with real-time vector embedding generation and immediate indexing for low-latency retrieval 5. Replace separate OLTP and vector databases with a single platform to simplify your AI application's data architecture

SINGLESTORE

ABOUT

INTEGRATION GUIDE

TAGS