Weaviate
The AI-native vector database developers love
Founded
2019
Starting Price
0
Category
Developer Tools
Last updated March 12, 2026
About Weaviate
Weaviate is an open-source vector database that stores both objects and vectors, enabling AI-powered semantic search, hybrid search, and retrieval augmented generation (RAG) with built-in support for popular ML models. It combines vector similarity search with structured filtering, multi-tenancy, and replication for production-grade AI applications.
Pros & Cons
Pros
- Open-source with a generous self-hosted option, avoiding vendor lock-in
- Built-in RAG and hybrid search eliminate the need for additional orchestration tools
- Excellent developer experience with intuitive APIs and comprehensive documentation
- Native integrations with popular ML models (OpenAI, Cohere, HuggingFace) for automatic vectorization
- Strong community support and responsive customer service via Slack
Key Features
Vector & Semantic Search
Index and search data using vector embeddings for meaning-based retrieval, finding relevant results even when query terms don't exactly match stored content.
Hybrid Search
Combine vector similarity search with traditional keyword (BM25) search in a single query for more accurate and contextually relevant results.
Built-in RAG
Integrated retrieval augmented generation that combines search retrieval and LLM prompting in a single query, eliminating the need for separate orchestration tooling.
Automatic Vectorization
Built-in vectorizer modules that automatically generate embeddings at import time using integrated models from OpenAI, Cohere, HuggingFace, and others.
Reranking
Improve search quality by reordering initial search results using integrated reranker models for more precise relevance scoring.
Multi-Tenancy
Native multi-tenancy support for isolating data across different users or applications within the same Weaviate instance.
Pricing
Sandbox
0/month
- 14-day free trial
- Full access to core features
- Single cluster
- Community support
Flex
$45
Best For
Retrieval Augmented Generation (RAG)
Power LLM-based chatbots and Q&A systems by retrieving relevant context from your data and generating accurate, grounded responses with reduced hallucination.
Semantic & Hybrid Search
Build intelligent search experiences for e-commerce, documentation, or enterprise knowledge bases that understand user intent beyond exact keyword matches.
Recommendation Engines
Recommend similar products, content, or media based on vector similarity of user preferences and item characteristics, without relying on shared metadata.
Content Classification & Anomaly Detection
Automatically classify documents, detect anomalies, or identify patterns in large datasets using vector proximity and clustering techniques.
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