What is Weaviate?
Weaviate is an open-source vector database for building AI search, retrieval-augmented generation and recommendation applications. It was founded in 2019 (originally as SeMI Technologies) by Bob van Luijt, Etienne Dilocker and Micha Verhagen, and is headquartered in Amsterdam, Netherlands. The core database is open source; Weaviate Cloud is a separate managed service with a free tier and usage-based paid plans.
Read the full overview
Sources checked 27 September 2026: official website, documentation, GitHub repository, pricing page.
Key capabilities
- Vector + hybrid search: Combines vector similarity with keyword search.
- Client SDKs & APIs: Python, Go, TypeScript/JavaScript clients plus GraphQL and REST.
- Deployment choice: Docker, Kubernetes or embedded self-hosting, or managed Weaviate Cloud.
Vector database and AI search infrastructure are the primary use cases.
Shortlist Weaviate when you're building RAG or semantic search and want the flexibility to self-host (Docker/Kubernetes) or start on a free managed cloud tier and scale into usage-based paid plans.
Check which embedding models and integrations you need, and compare self-hosted cluster costs against Weaviate Cloud's per-dimension pricing (roughly $45-$400+/month) before committing.
Technical details
- Access
- Self-hosted
- Source model
- Open source
- Founded
- 2019
- Headquarters
- Amsterdam, Netherlands
- Pricing model
- Freemium
- API
- Not specified
- Website
- weaviate.io ↗
Official resources
What to verify for your environment
Start from the users, systems and operating responsibilities the tool needs to support.
- Confirm current features, licensing and support terms with the publisher.
- Validate deployment, data location, access control, backup and recovery requirements.
- Test integrations, export paths and a representative operational workflow before committing.
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