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The AI-Native, Open Source Vector Database

Purpose-built open source vector database for a new breed of software applications.

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The open source vector database developers love

Weaviate is an open-source vector database that simplifies the development of AI applications. Built-in vector and hybrid search, easy-to-connect machine learning models, and a focus on data privacy enable developers of all levels to build, iterate, and scale AI capabilities faster.

Bring the power of AI to more developers

Help developers build and scale AI-powered applications more easily with an open source, developer-friendly platform and ecosystem.

Easily connect to popular ML models

Build and iterate faster with integrations for 20+ ML models and frameworks. Quickly adopt and test new models as the ecosystem evolves.

Get the best of vector and keyword search

Improve semantic understanding and accuracy to deliver better insights. Leverage both vector search and BM25 keyword search - to create hybrid search - without any extra overhead.

Run securely, flexibly, and reliably—at scale

Run where you want, how you want. Weaviate is available as a self-hosted database, a managed service, or a Kubernetes package in your VPC.

Learn more about Trust and Security with Weaviate

Open Source Vector Database Features

Built-in hybrid search

Merge different search algorithms and re-rank results accordingly.

Advanced filtering

Apply complex filters across large datasets in milliseconds.

Out-of-the box RAG

Use proprietary data to securely interact with ML models.

Vectorizer modules

Easily generate new vector embeddings or bring your own.

Configurable backups

Back up as often as needed with zero downtime.

Native multi-tenancy

Scale horizontally and consume resources efficiently.

Vector index compression

Improve the memory footprint of large datasets.

Tenant
isolation

Ensure security with strict resource isolation.

20+ Ecosystem Integrations

Move faster with direct integration into the ML ecosystem.

“Weaviate's batteries-included approach, incorporating both model serving and multi-tenancy, has helped us quickly prototype and build our vector search at Stack.”

Constantine Kokkinos,
Stack Overflow

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