Best Vector Databases Software (2026)

Pinecone is a managed vector database built specifically for AI applications, handling embedding storage, similarity search, and metadata filtering at scale. Free tier with 100K vectors; Starter at $70/month; usage-based beyond that. The first major standalone vector database in production.

Weaviate is an open-source vector database with native support for multi-modal search, combining vector similarity with structured filtering and built-in vectorization modules. Self-hosted free; Weaviate Cloud starts at $25/month. Stronger feature set than Pinecone for multi-modal use cases.

Milvus is an open-source vector database designed for billion-scale similarity search, maintained by Zilliz. Zilliz Cloud is the managed version. Self-hosted for free; Zilliz Cloud from $0.10/CU/hour. Built for production-scale AI applications with high QPS requirements.

Chroma is an open-source embedding database designed for AI application development, particularly prototyping and local development. Free and fully open-source; Chroma Cloud in beta. The most widely used vector database for Python notebook experimentation.

Qdrant is an open-source vector database written in Rust, optimized for high-performance similarity search with rich filtering, named vectors, and on-disk indexing. Free self-hosted; Qdrant Cloud from $0/month free tier. Fastest growing vector database in production deployments.

MongoDB Atlas is the fully managed cloud database service for MongoDB, adding vector search capabilities via Atlas Vector Search. Runs on AWS, Azure, and GCP. Free tier (512MB); paid from $57/month for dedicated clusters. Vector search is available on M10+ clusters.

pgvector is an open-source PostgreSQL extension that adds vector similarity search to any PostgreSQL database. Free and self-hosted. Handles the majority of RAG use cases below 1 million vectors without dedicated infrastructure. The lowest-friction path to vector search for teams already on PostgreSQL.

Databricks Vector Search is a managed vector database integrated into the Databricks Data Intelligence Platform, storing vector embeddings alongside Delta Lake tables. Available on Databricks pay-as-you-go pricing (DBU-based). Best for teams already running ML pipelines on Databricks.

Elasticsearch's vector search uses HNSW dense vector fields and k-NN search, available from version 8.0+ with kNN support GA in 8.4. Part of the existing Elastic Stack; no extra licensing for self-hosted. Elastic Cloud adds managed hosting from $95/month.