A curated registry of vector databases, vector stores, vector-search libraries, and benchmarks for AI, RAG, and semantic-search applications.
The goal is to be the definitive place to discover and compare production-grade vector data systems — from managed cloud services and self-hosted engines to embedded libraries and hybrid search platforms.
Inclusion criteria: Resources must provide native vector storage, indexing, or similarity search as a core capability. General SQL databases without vector extensions are out of scope. See CONTRIBUTING.md for the full quality bar.
- Managed Vector Databases
- Self-Hosted Vector Databases
- Embedded Vector Stores
- Graph + Vector
- Time-Series + Vector
- Vector Search Libraries
- Hybrid Search
- Benchmarks
- Related Awesome Lists
- Contributing
- License
Fully managed cloud services that provide vector storage, indexing, and similarity search without infrastructure overhead.
- Pinecone
Official— Managed vector database with metadata filtering, hybrid search, and no-index tuning. - Zilliz Cloud
Official— Fully managed Milvus service with GPU indexing and enterprise security. - Weaviate Cloud
Official— Managed vector search engine with built-in vectorization and GraphQL interface. - Qdrant Cloud
Official— Managed Qdrant cluster service with quantization and hybrid search. - DataStax Astra DB
Official— Managed Cassandra-compatible database with vector search for RAG. - Chroma Cloud
Official— Managed service for the Chroma embedded vector store. - Azure AI Search
Official— Cloud search service with vector and hybrid retrieval. - AlloyDB AI
Official— Google Cloud managed PostgreSQL with vector search and AI integrations. - SingleStore
Official— Distributed SQL database with SingleStore-V integrated vector search. - MyScale
Official— ClickHouse-based managed vector database for structured + vector data.
Production-ready vector databases you can deploy on your own infrastructure.
- Milvus
Official— Open-source distributed vector database with GPU support and cloud-native architecture. - Qdrant
Official— Open-source vector database in Rust with filtering, quantization, and hybrid search. - Weaviate
Official— Open-source vector search engine with modular AI integrations. - pgvector
Official— PostgreSQL extension for vector similarity search with HNSW and IVFFlat indexes.- Install:
CREATE EXTENSION vector;or use the pre-built Docker image.
- Install:
- Chroma
Official— AI-native open-source embedding database for local and production deployments. - Vespa
Official— Big data serving engine with native vector search, ranking, and tensors. - Vald
Official— Cloud-native, highly scalable distributed vector search engine. - Marqo
Official— End-to-end vector search engine with built-in embedding generation. - OpenSearch
Official— Open-source search and analytics suite with k-NN vector search. - Elasticsearch
Official— Distributed search engine with dense and sparse vector search. - Redis Stack
Official— In-memory data platform with vector similarity search via RediSearch. - Couchbase
Official— Distributed NoSQL database with vector search for RAG applications.
Lightweight, in-process vector stores and libraries for local development and edge deployments.
- Chroma
Official— Popular embedded vector store with simple Python/JS APIs. - FAISS
Official— Facebook AI Similarity Search library for efficient nearest-neighbor search. - LanceDB
Official— Serverless vector database built on the Lance columnar format. - SQLite-vec
Community— SQLite extension for vector search, designed for edge and embedded use. - Voyager
Official— Spotify's approximate nearest-neighbor search library with a Python/Java/C++ API. - HNSWLib
Community— Header-only HNSW implementation with Python bindings. - Annoy
Official— Spotify's approximate nearest-neighbor library using random projections. - ScaNN
Official— Google's scalable nearest-neighbor search library. - USearch
Official— Fast, compact, and broadly compatible single-file similarity search engine. - Vectorlite
Community— SQLite-like embedded vector database with HNSW indexing.
Graph databases that combine vector similarity search with relationship traversal for GraphRAG and knowledge-graph workloads.
- Neo4j
Official— Graph database with native vector indexes and GenAI integrations. - ArangoDB
Official— Multi-model database with ArangoSearch and vector search for documents and graphs. - TigerGraph
Official— Native parallel graph database with TigerVector hybrid graph+vector search. - Memgraph
Official— In-memory graph database with native vector search and graph traversal. - Kùzu
Official— Embedded property graph database with built-in vector and full-text search. - FalkorDB
Official— Low-latency graph database with vector support for RAG. - Amazon Neptune
Official— Managed graph database with vector search and Neptune Analytics.
Databases that combine time-series/analytics workloads with vector search, often via SQL extensions.
- TimescaleDB
Official— Time-series PostgreSQL extension with pgvector and pgvectorscale for time-aware hybrid search. - ClickHouse
Official— Real-time OLAP database with vector distance functions and ANN indexing. - DuckDB
Official— In-process analytical database with array distance functions and vector-search extensions. - StarRocks
Official— Fast OLAP engine with native HNSW/IVFPQ vector indexes and hybrid queries. - Apache Doris
Official— Real-time OLAP data warehouse with vector index support for high-dimensional data.
Low-level libraries and algorithms for building approximate nearest-neighbor indexes.
- FAISS
Official— Comprehensive library for efficient similarity search and clustering of dense vectors. - HNSWLib
Community— Fast HNSW graph-based nearest-neighbor library. - Annoy
Official— Approximate nearest-neighbor library optimized for memory-mapped indexes. - ScaNN
Official— Google's library for vector similarity search at scale. - USearch
Official— Single-file similarity search library compatible with many index formats. - NMSLIB
Community— Similarity search library and toolkit with non-metric space support. - DiskANN
Official— Disk-resident ANN index from Microsoft for billion-scale datasets. - Vearch
Official— Scalable vector search system built on Faiss for deep-learning retrieval.
Search engines that combine keyword/BM25, filtering, and vector similarity in a single platform.
- Meilisearch
Official— Instant search engine with vector search, typo tolerance, and faceting. - Typesense
Official— Open-source typo-tolerant search engine with vector and semantic search. - Algolia
Official— Managed search platform with hybrid neural and keyword search. - Manticore Search
Official— Open-source search engine with SQL-like queries and vector search. - Quickwit
Official— Cloud-native search engine for logs and traces with vector search support. - Apache Solr
Official— Open-source search platform with dense vector search via the DenseVector field type.
Tools and datasets for measuring vector database and embedding performance.
- VectorDBBench
Official— Open-source benchmark tool for vector databases and cloud services. - ANN-Benchmarks
Community— Standardized benchmarking suite for approximate nearest-neighbor algorithms. - Big-ANN Benchmarks
Official— NeurIPS competition benchmark for billion-scale ANN search. - BEIR
Community— Heterogeneous benchmark for information retrieval with zero-shot evaluation. - MTEB
Community— Massive Text Embedding Benchmark leaderboard for embedding models. - VIBE
Community— Vector Index Benchmark for Embeddings using modern embedding datasets.
Discover complementary resources for building RAG, agent, and local-LLM systems.
- Awesome RAG — Curated list of open-source tools for retrieval-augmented generation.
- Awesome MCP Servers — Curated list of Model Context Protocol servers, including vector-store integrations.
- Awesome Local LLM — Curated list of tools and models for running LLMs locally.
Read CONTRIBUTING.md for the quality bar, entry format, and PR process.
This list is released into the public domain under CC0-1.0.
Enterprise AI Atlas is maintained by Vibe Coding Agency. We prototype and ship agentic systems, MCP servers, and enterprise AI integrations for teams that need working software fast — without hiring a full AI engineering team.
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