Explore all databases
Browse every vector database in our directory. From embedded solutions to planet-scale distributed systems.
Vector databases are specialized data stores designed for AI and machine learning workloads. Unlike traditional databases that search for exact keyword matches, vector databases enable semantic similarity search by storing and querying high-dimensional embeddings generated by AI models.
Whether you need an open-source self-hosted solution, a fully managed cloud service, or a hybrid approach, this directory helps you find the right vector database for your RAG application, semantic search engine, or recommendation system.
Choosing the right vector database
Deployment Model
Self-hosted open source, fully managed cloud, or hybrid deployment? Consider your team's operational capabilities and infrastructure requirements.
Scale & Performance
Evaluate query latency, throughput requirements, and dataset size. Some databases excel at small-scale applications, others handle billions of vectors.
Features & Integrations
Check for filtering capabilities, multi-tenancy, hybrid search, and integrations with your existing AI tools and frameworks.
Cost Structure
Compare pricing models: pay-as-you-go cloud services vs. infrastructure costs for self-hosted solutions. Factor in operational overhead.
Community & Support
Active development, documentation quality, community size, and availability of enterprise support can significantly impact your success.
Data Sovereignty
If you handle sensitive data or operate in regulated industries, consider where your vectors are stored and who has access.
21 databases
Frequently Asked Questions
What is a vector database?
A vector database is a specialized database designed to store, index, and query high-dimensional vectors (embeddings) generated by machine learning models. Unlike traditional databases that perform exact keyword matching, vector databases enable semantic similarity search, making them essential for AI applications like RAG, recommendation systems, and intelligent search.
Which vector database is best for production use?
The best vector database depends on your specific requirements. For rapid deployment with zero infrastructure management, consider managed cloud solutions like Pinecone or Weaviate Cloud. For maximum control and data sovereignty, open-source options like Qdrant or Milvus offer self-hosting flexibility. If you already run PostgreSQL or Redis, extensions like pgvector or Redis Stack can add vector capabilities without introducing new infrastructure.
How do vector databases differ from traditional databases?
Traditional databases excel at exact matches and structured queries using SQL. Vector databases are optimized for approximate nearest neighbor (ANN) search across high-dimensional embeddings. They use specialized indexing algorithms like HNSW, IVF, or DiskANN to find semantically similar items in milliseconds, even across millions or billions of vectors.
What are the main use cases for vector databases?
The most common use cases include: RAG (Retrieval-Augmented Generation) for grounding LLM responses with your own data, semantic search that understands meaning rather than just keywords, recommendation engines that find similar products or content, image and video search, anomaly detection, and personalization systems.
Are vector databases expensive to run?
Costs vary significantly by deployment model and scale. Managed cloud services typically charge based on storage and query volume, starting from free tiers and scaling to hundreds or thousands per month at high volume. Self-hosted open-source solutions require infrastructure costs (compute, storage, networking) plus operational overhead. For many teams, the operational simplicity of managed services justifies the premium.