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Vector Databases

Databases and libraries that store embeddings and run similarity search for RAG and AI applications, from managed and serverless services to open-source engines and vector features in existing databases.

32 topics•5 categories•Updated October 2026

Managed & Serverless Vector Databases

5 entries

Hosted services that run the database for you.

01PineconeFully managed, developer-friendly, and easily scalable vector database built for production AI applications. 02Zilliz CloudFully managed Milvus service by its creators. Simplifies deploying and scaling vector search by eliminating complex infrastructure management. 03Upstash VectorA serverless vector database from Upstash with per-request pricing, designed for serverless and edge applications. 04turbopufferA serverless vector and full-text search engine built on object storage. 05DataStax Astra DBA managed, Cassandra-based database service with built-in vector search.

Open-Source Vector Databases

7 entries

Self-hostable engines built for vector search.

06WeaviateOpen-source vector database that lets you store data objects and vector embeddings from your favourite ML models, scaling seamlessly into billions of objects. 07MilvusStore, index, and manage massive embedding vectors generated by deep neural networks and other machine learning models. 08QdrantA vector database and vector similarity search engine deployed as an API service, providing fast nearest-neighbour search for high-dimensional vectors. 09ChromaThe AI-native open-source embedding database. Makes it easy to build LLM apps by making knowledge, facts, and skills pluggable for LLMs. 10ValdA highly scalable distributed vector search engine designed for large-scale approximate nearest-neighbour search workloads. 11LanceDBDesigned for multimodal, built for scale. From agents to models, from search to training — one platform for all your AI data and workloads. 12ZvecA lightweight, lightning-fast, and in-process vector database for enabling high-Performance semantic search,

Vector Search in Existing Platforms

12 entries

Add vector search to databases and search engines you may already run.

13Vespa.aiAn AI Search Platform for large-scale applications combining big data, vector search, machine-learned ranking, and real-time inference. 14Redis Vector DatabaseBuild intelligent, AI-powered applications with Redis Enterprise — delivering ultra-low latency vector similarity search at scale. 15SingleStoreDBDelivers built-in similarity search on vectors to add persistent memory for generative AI applications — no separate vector store needed. 16YugabyteDB AIUnique approach to vector indexing that addresses the limitations of single-node PostgreSQL systems when dealing with large-scale vector datasets. 17Supabase VectorVector search with pgvector on Supabase's managed Postgres platform. 18pgvectorOpen-source vector similarity search for PostgreSQL. 19ElasticsearchSearch engine with vector search capabilities. 20OpenSearchOpen-source search and analytics suite with vector search. 21MongoDB Atlas Vector SearchVector search in MongoDB Atlas. 22Azure AI SearchMicrosoft's search service with vector and hybrid retrieval. 23Amazon S3 VectorsVector storage and search built into Amazon S3. 24CouchbaseNoSQL database with vector search support.

Libraries

1 entries

25FAISSFacebook AI Similarity Search — a library for fast search of embeddings of multimedia documents that are similar to each other, optimised for billion-scale datasets.

Related Pages

7 entries

Related directories on this site.

26Database SystemsRelational, NoSQL and specialised database systems. 27Databases for AI Model TrainingDatabases for features, embeddings and AI model engineering. 28Knowledge GraphsKnowledge graph technologies. 29Serverless ComputingServerless computing hub. 30Serverless Platforms for AI AgentsServerless platforms for AI agents. 31Generative AIGenerative AI resources. 32Chunking Strategies for Enhanced RAG QualityChunking strategies for retrieval-augmented generation.

Frequently Asked Questions

What is a vector database?

A database that stores embeddings, the numerical representations of text, images or other data, and finds the items most similar to a query vector quickly. It underpins semantic search and RAG.

Do I need a dedicated vector database?

Not always. If you already run PostgreSQL, Elasticsearch or MongoDB, their vector features may be enough. Dedicated engines help at very large scale or with specialised filtering and latency needs.

What is a serverless vector database?

One that scales to zero and charges per request or per storage, so you do not manage clusters. Examples listed here include Pinecone, Upstash Vector and turbopuffer.

What is the difference between FAISS and a vector database?

FAISS is a library for similarity search that runs in your own process. A vector database adds storage, persistence, filtering, replication and an API.

Are the links checked?

Links were checked when this page was updated. Vespa blocked automated checks, and Milvus redirects, so open them yourself if they fail.