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Open-source vector database for semantic search, RAG, and agentic AI at billion-scale
Dutch open source vector database powering semantic search, RAG and agentic AI, built to scale to billions of objects with hybrid search and model integrations.
What Weaviate does
Weaviate is an open-source vector database built for AI applications: semantic search, retrieval-augmented generation (RAG), and agentic workflows. The company is headquartered in Amsterdam (Weaviate B.V.) with a globally distributed team, and the platform is used by a community of over 50,000 AI developers as the data layer behind LLM-powered products.
Capabilities
Weaviate ingests unstructured data, generates embeddings automatically, and serves semantic and hybrid (keyword + vector) search at billion-scale. SDKs are available in Python, Go, TypeScript, and JavaScript alongside GraphQL and REST APIs. Out-of-the-box features include intelligent ranking, auto-scaling infrastructure, pre-built database agents, and tight integrations with OpenAI, Cohere, Hugging Face, and other embedding providers. Enterprise controls include RBAC, SOC 2, and HIPAA compliance.
Deployment and differentiators
The platform can run in Weaviate Cloud (shared or dedicated), self-hosted on Kubernetes, or via Docker for development. Against Pinecone (US, closed-source) or Milvus (Chinese origin), Weaviate's positioning is open-source transparency, European headquarters under GDPR, and a single system that consolidates what would otherwise be multiple AI infrastructure components. A free tier and managed cloud trials are available through the Weaviate Console.
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Head-to-head comparisons
See how Weaviate stacks up side-by-side against the non-EU incumbent on jurisdiction, GDPR, pricing, and features.
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