SQL Server Vector Search

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.

Combining dense vector embeddings with sparse representations in a single unified model. Captures both semantic meaning (dense) and exact term matching (sparse) for superior retrieval performance.

A search architecture that combines dense vector embeddings (semantic search) with sparse representations like BM25 (lexical search) to achieve better overall search quality. The industry standard approach for production RAG systems in 2026.

AstraDB is a serverless, cloud-hosted vector database built on Cassandra, offering fully managed infrastructure with auto-scaling, auto-sharding, pay-per-use pricing, automated backups, and multi-region/multi-cloud deployments. Ideal for enterprise RAG pipelines, production AI applications, and hybrid vector-wide-column workloads. Provides easier operations than self-hosted Milvus, with greater durability compared to Zilliz.

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.

High-performance vector database with SQL support.