35+ Vector Databases Ranked & Compared
Compare vector databases ranked by GitHub stars, AI adoption, and search performance.
In-memory data store used as a database, cache, message broker, and streaming engine
Lightning-fast, typo-tolerant search engine with AI-powered hybrid search
Lightning-fast, typo-tolerant search engine with AI-powered hybrid search
High-performance cloud-native vector database built for scalable similarity search and AI applications
High-performance cloud-native vector database built for scalable similarity search and AI applications
High-performance open-source vector database for next-generation AI applications
High-performance open-source vector database for next-generation AI applications
The most popular document database for modern applications
Open-source AI-native vector database for building LLM-powered applications with embeddings
Open-source AI-native vector database for building LLM-powered applications with embeddings
Fast, typo-tolerant open-source search engine with built-in vector and semantic search
AI-native vector database with hybrid search and built-in model integration
AI-native vector database with hybrid search and built-in model integration
Open-source multimodal vector database built on the Lance columnar format with local-first deployment
Open-source multimodal vector database built on the Lance columnar format with local-first deployment
Lightweight, lightning-fast in-process vector database powered by Alibaba's Proxima engine
Lightweight, lightning-fast in-process vector database powered by Alibaba's Proxima engine
GPU-native vector and multimodal data lake for AI agents with deep learning integrations
GPU-native vector and multimodal data lake for AI agents with deep learning integrations
Open-source big data serving engine combining search, recommendation, and real-time AI at scale
Alibaba's battle-tested MySQL branch with built-in DuckDB analytics and vector search
Alibaba's battle-tested MySQL branch with built-in DuckDB analytics and vector search
AI-native tensor search engine with built-in embedding generation for multimodal vector search
AI-native tensor search engine with built-in embedding generation for multimodal vector search
Open-source graph-vector database built from scratch in Rust with compiled queries
AI-native database for LLM applications with blazing-fast hybrid search across vectors, text, and tensors
AI-native database for LLM applications with blazing-fast hybrid search across vectors, text, and tensors
Ultra-fast in-memory graph database using GraphBLAS, optimized for GraphRAG and knowledge graphs
Distributed SQL database for real-time analytics on massive datasets with PostgreSQL compatibility
Distributed SQL database for real-time analytics on massive datasets with PostgreSQL compatibility
Fast open-source vector search and clustering engine for C++, Python, JavaScript, and 10+ languages
Fast open-source vector search and clustering engine for C++, Python, JavaScript, and 10+ languages
In-memory graph database tuned for dynamic analytics environments with real-time performance
Transactional relational-graph-vector database using Datalog for query — the hippocampus for AI
Transactional relational-graph-vector database using Datalog for query — the hippocampus for AI
Embedded property graph database built for speed with Cypher, vector search, and full-text search
Cloud-native HTAP database with MySQL compatibility, Git-style data versioning, and AI-native capabilities
Cloud-native HTAP database with MySQL compatibility, Git-style data versioning, and AI-native capabilities
Highly scalable distributed vector search engine built on Cloud-Native architecture with NGT
Highly scalable distributed vector search engine built on Cloud-Native architecture with NGT
SQL vector database built on ClickHouse for high-performance AI applications with filtered search
SQL vector database built on ClickHouse for high-performance AI applications with filtered search
Multi-model database supporting graphs, documents, key-value, vectors, time-series, and search in one engine
Multi-model database supporting graphs, documents, key-value, vectors, time-series, and search in one engine
Neuro-symbolic AI platform combining RDF knowledge graphs, vector store, and SPARQL in a transactional graph database
Neuro-symbolic AI platform combining RDF knowledge graphs, vector store, and SPARQL in a transactional graph database
Multi-model NoSQL database for enterprise applications with SQL++ support
Multi-model NoSQL database for enterprise applications with SQL++ support
Enterprise cloud search service with vector search, semantic ranking, and AI-powered agentic retrieval
Enterprise cloud search service with vector search, semantic ranking, and AI-powered agentic retrieval
Edge-optimized on-device database with vector search for mobile, IoT, and embedded AI applications
Enterprise-grade multi-model database with AI-native capabilities
Enterprise-grade multi-model database with AI-native capabilities
Fully managed vector database built for high-performance AI applications at scale
Fully managed vector database built for high-performance AI applications at scale
Serverless vector database for AWS with pay-per-request pricing and built-in vectorizers
Serverless vector database for AWS with pay-per-request pricing and built-in vectorizers
Enterprise cloud-native distributed vector database with GPU acceleration and multi-model support
Enterprise cloud-native distributed vector database with GPU acceleration and multi-model support
Serverless vector and full-text search database built on object storage for low-cost high-scale workloads
Serverless vector and full-text search database built on object storage for low-cost high-scale workloads
What is a Vector Database?
A vector database stores, indexes, and searches high-dimensional vector embeddings — numerical representations of data generated by AI models. When you embed text, images, or audio into vectors, a vector database lets you find similar items through approximate nearest neighbor (ANN) search, enabling semantic search (finding results by meaning, not keywords), recommendation engines, and retrieval-augmented generation (RAG) for LLMs. Purpose-built vector databases like Qdrant, Milvus, Weaviate, and Chroma are optimized for this workload, while PostgreSQL (with pgvector) and Redis offer vector search as an add-on feature.
When to Use a Vector Database
Vector databases are essential when your application needs similarity search over AI-generated embeddings. Key use cases include: RAG pipelines where an LLM retrieves relevant context before answering, semantic search that understands meaning beyond keyword matching, image and audio similarity search, recommendation engines, anomaly detection, and AI agent memory. If your dataset is small (under 100K vectors), pgvector in PostgreSQL is often sufficient. For larger datasets with low-latency requirements, a dedicated vector database like Qdrant, Milvus, or Weaviate provides better indexing and query performance.
Frequently Asked Questions
What is the difference between a vector database and a traditional database?
Do I need a separate vector database or can I use PostgreSQL with pgvector?
Which vector database is best for RAG applications?
What are vector embeddings?
What is the fastest growing vector database in 2026?
Browse by Category
Explore databases organized by type, data model, and architecture.
Manage Vector Databases Visually
1bench is a modern GUI client that supports all major vector databases and many more.
Get Started