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Best Vector Databases 2026
Find vector database platforms for storing and querying high-dimensional embeddings — the technical foundation of AI applications including semantic search, RAG systems, recommendation engines, and similarity matching. These specialized databases handle the dense vector operations that traditional databases perform poorly at. Compare query performance, filtering capabilities, managed vs. self-hosted options, and pricing at scale.
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Best Vector Databases 2026 - Frequently Asked Questions
What is a vector database?▾
A vector database stores data as high-dimensional numerical vectors (embeddings) and enables fast similarity search — finding the most semantically similar items to a query vector across millions or billions of stored vectors. Traditional databases use exact matching; vector databases use approximate nearest neighbor (ANN) algorithms to find the closest vectors by cosine or dot product similarity.
What are the most popular vector databases?▾
Pinecone is the leading managed vector database — fully hosted, fast, and production-ready without infrastructure management. Weaviate and Qdrant are popular open-source options you can self-host or use managed. pgvector adds vector capabilities to PostgreSQL — a practical choice if you are already using Postgres. Chroma is lightweight and popular for local development and prototyping.
What is an embedding and how do I create one?▾
An embedding is a numerical representation of text (or images) as a dense vector — similar concepts produce numerically close vectors. You create embeddings using embedding models: OpenAI's text-embedding-3-small, Cohere's embed-v3, or open-source models via Sentence Transformers. Feed your text to the embedding model API; store the returned vector in your vector database alongside metadata for filtering.