From $65 per month · Free plan🇺🇸
Graph database with vector search, GraphRAG building blocks and Aura Agent, as managed AuraDB or self-managed.
Best vector and AI databases (October 2026)
This category covers databases that store and search the vectors behind AI search, retrieval-augmented generation and agent memory.
17tools in this category
7countries with local strength
7can host data in the EU
How results are ranked: How we review
Results
From $20 per month · Free plan🇺🇸
Fully managed vector database with integrated embedding models, full-text search and an assistant component for RAG applications.
Usage-based · Free plan🇩🇪
Open-source vector database from Berlin, available as managed cloud, hybrid cloud in your own infrastructure or private cloud.
From $73 per month · Free plan🇺🇸
Multi-tenant graph database for GraphRAG and agent memory, with vector and full-text search, self-hosted or as a cloud service.
From $1 usage-based · Free plan🇺🇸
Distributed graph database with vector search for GraphRAG, fraud detection and customer analytics, in the cloud or self-managed.
From $16 per monthAvailable internationally
Serverless search engine on object storage for vector, full-text and hybrid search, with regions in North America, Europe and Asia Pacific.
From $0.05 usage-based · Free trial🇳🇴
Search and vector platform with hybrid retrieval and machine-learned ranking in the query path, open source or as Vespa Cloud.
From $45 per month · Free plan🇳🇱
Open-source vector database from Amsterdam with hosted embedding models and a Query Agent for natural-language queries.
From $197 per month · Free plan🇺🇸
Managed vector database built on Milvus with hybrid search, a free tier, AWS, Google Cloud and Azure, plus bring-your-own-cloud.
Free🇺🇸
Open-source embedding database with a four-function API, usable locally or as managed Chroma Cloud.
Contact for pricing · Free plan🇬🇧
In-memory graph database with vector search, GraphRAG tooling and MCP support, as open-source Community Edition or Enterprise.
Free🇨🇳
Open embedding model from the Beijing Academy of AI for more than 100 languages, with dense, sparse and multi-vector retrieval in one model.
Free🇺🇸
Embedded open-source database for multimodal AI data on the Lance columnar format, with vector, full-text and SQL search.
Free🇭🇰
Open-source RAG from the University of Hong Kong that builds a knowledge graph from documents and answers questions via graph and vectors.
Free🇩🇪
Qdrant's official MCP server that gives AI assistants a semantic memory based on the Qdrant vector database.
FreeAvailable internationally
Distributed open-source vector database under the LF AI & Data Foundation, built for billions of vectors and hybrid search.
FreeAvailable internationally
Open-source PostgreSQL extension that stores vectors next to your other data and searches them by similarity.
How to choose vector and AI databases (October 2026)
This category covers databases that store and search the vectors behind AI search, retrieval-augmented generation and agent memory. It is split into three areas: dedicated vector databases, graph databases with vector search for knowledge graphs and GraphRAG, and embedding stores that are embedded in an application or extend an existing database such as PostgreSQL. You will find managed cloud services and open-source projects you can run yourself.
The AI part of these systems is similarity search. Text, images or other content are turned into vectors by an embedding model, and the database returns the entries closest to a query, often combined with keyword search and filters. Some products also create embeddings themselves, rerank results or translate natural-language questions into queries, and graph databases add the relationships between entities so that a language model receives connected facts.
Before you choose, decide who operates the database. Open source gives you control but brings updates, backups and monitoring into your team, while managed services charge by usage, memory or storage and often set a monthly minimum. Check the available regions, whether single sign-on and audit logs are included in your plan, and the license of open-source projects, since some restrict offering the software as a service.
Questions about vector and AI databases (October 2026)
Do I need a dedicated vector database?
Not always. If you already run PostgreSQL, an extension such as pgvector can be enough for moderate volumes. Dedicated vector databases help with very large collections, many tenants or features such as integrated embedding and hybrid search.
When is a graph database useful for AI?
When answers depend on relationships between entities, such as customers, contracts and products. GraphRAG combines graph traversal with vector search so that the language model receives connected facts.
Can I keep vectors in the EU?
Most managed services offer EU regions, sometimes only on paid plans, and open-source databases can run in your own data center. Choose the region when you create the index.
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