Weaviate
AI-native vector database with built-in vectorizers
About Weaviate
Weaviate is an open-source, AI-native vector database designed to support modern machine learning and semantic search applications. It provides built-in vectorization capabilities, hybrid search combining keyword and vector retrieval, and a flexible data model. Weaviate offers APIs such as GraphQL and REST, enabling developers to build applications such as chatbots, recommendation systems, and AI search engines. Its modular and scalable architecture makes it suitable for both self-hosted deployments and managed cloud environments. Weaviate emphasizes developer productivity and fast query performance, enabling efficient retrieval of semantic information from large datasets.
Key features
Pricing
Common use cases
Common questions about Weaviate
Is Weaviate free to use?
Yes, Weaviate is open source under the BSD-3-Clause license. You can self-host it at no licensing cost, though you'll need to manage infrastructure and operational costs.
Can I get support for Weaviate?
Weaviate has community support through documentation, forums, and GitHub issues. Some open-source databases also offer commercial enterprise support contracts.
What are the main use cases for Weaviate?
Weaviate is commonly used for semantic search, generative search (rag), classification, and similar applications requiring semantic similarity search.
Does Weaviate integrate with popular AI tools?
Most vector databases integrate with LangChain, LlamaIndex, and popular embedding providers. Check the Weaviate documentation for specific integration guides and examples.
Comparisons featuring Weaviate
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Compare it side-by-side with other vector databases to find the best fit for your project.