# Sql

> Browse 12 items tagged with Sql.

## Items

- [DuckDB](https://vectordb.ever.works/pl/items/duckdb) — Embeddable SQL OLAP engine with VSS extension for low-latency HNSW vector search on local files, ideal for edge AI prototyping and analytics. SQL-first approach for on-device vector ops vs cloud vector DBs like Qdrant.
- [SurrealDB](https://vectordb.ever.works/pl/items/surrealdb) — Multi-model database with vector search, graph queries, full-text keyword search, and real-time subscriptions for hybrid vector+keyword+graph retrieval. Ideal for multimodal RAG in full-stack apps. More versatile than pure vector DBs like Qdrant with embedded/multi-model support.
- [YugabyteDB](https://vectordb.ever.works/pl/items/yugabytedb) — PostgreSQL-compatible distributed SQL DB with HNSW vector search + keyword/full-text + relational/graph joins/aggs for hybrid queries. Scales ACID vector workloads for multimodal RAG. Unifies vectors with SQL unlike pure vector DBs like Qdrant.
- [Infinity](https://vectordb.ever.works/pl/items/infinity) — High-performance vector database with SQL support.
- [TiDB Vector Search](https://vectordb.ever.works/pl/items/tidb-vector-search) — Open-source distributed SQL database with integrated vector search for storing embeddings alongside relational data, offering strong SQL-based filtering, hybrid search, and high scalability for production RAG and AI applications.
- [Crate](https://vectordb.ever.works/pl/items/crate) — Crate is an open-source, self-hosted distributed SQL database with native vector data types and similarity search for AI applications. Key features include horizontal scaling, PostgreSQL compatibility, Lucene-based indexing, and Apache 2.0 license. Ideal for RAG and real-time analytics, providing free self-hosting vs managed vector DBs like Pinecone for cost control.
- [YDB](https://vectordb.ever.works/pl/items/ydb) — YDB is an open-source distributed SQL database with vector search capabilities under Apache License 2.0. It supports high-performance vector similarity search for AI and machine learning applications.
- [ruvector-postgres](https://vectordb.ever.works/pl/items/ruvector-postgres) — PostgreSQL extension providing 230+ SQL functions as pgvector replacement, enabling vector search, graph queries, and AI features directly in relational databases.
- [SQL Server Vector Search](https://vectordb.ever.works/pl/items/sql-server-vector-search) — Native vector search capabilities in SQL Server 2022 and Azure SQL, enabling vector similarity search alongside traditional relational data. Supports storing vectors as varbinary and performing approximate nearest neighbor queries.
- [CockroachDB](https://vectordb.ever.works/pl/items/cockroachdb) — CockroachDB is a cloud-native, distributed SQL database that now supports vector data, combining traditional SQL queries with efficient vector search capabilities, ensuring data resilience, availability, scalability, and strong consistency.
- [SingleStoreDB (formerly MemSQL)](https://vectordb.ever.works/pl/items/singlestoredb-formerly-memsql) — SingleStoreDB is an enterprise database that has supported vectors since 2017, in addition to exact keyword match, and recently announced support for additional vector indexes.
- [MindsDB Milvus Integration](https://vectordb.ever.works/pl/items/mindsdb-milvus-integration) — MindsDB provides an integration with Milvus, enabling users to connect and manage vector data using SQL-like queries. This integration brings federated AI query capabilities across structured and unstructured data with Milvus as the vector database backend.

---

_Canonical page: https://vectordb.ever.works/pl/tags/sql_