Weaviate
Free tierThe open-source AI-native vector database built for production-scale search, RAG, and agents
Free tier available·All audiences·Powered by Snowflake, ModernBERT (via Weaviate Embeddings); external LLMs connectable·API available·Open source
Key strengths
Open-source vector databaseBillion-scale architectureBuilt-in embeddings (no external pipeline required)Hybrid search (vector + keyword)Multi-tenancyDeployment-agnostic (cloud or self-hosted)RAG and agentic AI supportSOC 2 Type II and HIPAA compliance
Free tier + paid plans · from $45 USD/mo
Amsterdam, Netherlands
Founded 2019
Self-hostable
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Technical Use Cases
Weaviate is built for engineering teams developing AI-powered applications that require fast, semantically aware data retrieval. Key use cases include:
- Retrieval-Augmented Generation (RAG) — store and retrieve document chunks as vectors; feed results directly into connected LLMs for grounded, context-aware generation
- Semantic & hybrid search — combine ANN vector search with BM25 keyword ranking for high-relevance results across large corpora
- Agentic AI applications — use the Query Agent to enable autonomous, multi-step reasoning over your Weaviate data store
- Multi-tenant SaaS platforms — leverage native multi-tenancy to isolate customer data within a single cluster at billion-scale
- Enterprise AI pipelines — integrate with Snowflake, Databricks, AWS, Azure, and GCP for end-to-end data and ML workflows
- Self-hosted AI infrastructure — deploy on-premises or in a private cloud for data-sovereignty or compliance requirements (SOC 2 Type II, HIPAA)
- Embedding-native ingestion — use built-in Weaviate Embeddings (Snowflake, ModernBERT) to vectorize data at write time without an external pipeline
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