The vector database
directory
Compare, discover, and choose the perfect vector database for your AI project. In-depth analysis, benchmarks, and technical guides.
Explore by category
Find the ideal solution based on your use case, infrastructure requirements, and budget.
Featured databases
The most popular solutions in the vector search ecosystem.
What is a vector database?
A vector database stores data as high-dimensional vectors — numerical representations of text, images, or audio generated by AI models. It enables semantic similarity search instead of exact keyword matching, making it the key building block for RAG applications, intelligent search, and recommendation systems.
Embeddings
Numerical vectors generated by AI models that capture the semantic meaning of your content.
Similarity Search
Find the most relevant data using cosine distance or dot product instead of exact keyword matching.
RAG
Retrieval-Augmented Generation: ground LLM responses with your own private and up-to-date data.
Vector Database FAQ
What is a vector database used for?
Vector databases store and query high-dimensional embeddings from AI models, enabling semantic similarity search, RAG (Retrieval-Augmented Generation) for LLMs, recommendation systems, image search, anomaly detection, and personalization. They power applications that need to understand meaning and context rather than just exact keyword matches.
Which vector database should I choose?
Choose based on your deployment preferences and scale. For simplicity and rapid deployment, use managed cloud services like Pinecone or Weaviate Cloud. For maximum control and data sovereignty, choose open-source solutions like Qdrant or Milvus. If you already run PostgreSQL or Redis, their vector extensions (pgvector, Redis Stack) let you add semantic search without new infrastructure.
Are vector databases necessary for RAG applications?
While not strictly required for small datasets, vector databases are essential for production RAG applications. They provide fast semantic similarity search across millions of embeddings, enabling your LLM to retrieve relevant context in milliseconds. Without a vector database, RAG applications struggle with scale, performance, and accuracy.
How much do vector databases cost?
Costs vary widely by deployment model. Managed cloud services typically start with free tiers and scale to $100-$1000+ monthly based on storage and query volume. Self-hosted open-source solutions require infrastructure costs (compute, storage, networking) but no licensing fees. For many teams, managed services offer better ROI when factoring in operational overhead.
Can I use a vector database with my existing database?
Yes. Most architectures use a hybrid approach: a vector database for semantic search and embeddings, plus a traditional database for structured data and business logic. Some databases like PostgreSQL (pgvector), MongoDB, and Elasticsearch now offer vector search extensions, letting you combine both in a single system.
Find the right vector database for your project
Compare features, pricing, and deployment models side by side.