Weaviate

AI-Native Vector Database

An open-source vector database with native hybrid search, built-in vectorization modules, AI agents, and sub-100ms queries — designed from the ground up for AI applications.

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Click on any feature node to explore Weaviate's capabilities

Core Capabilities

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Hybrid Search

  • Vector similarity (HNSW)
  • BM25 keyword search
  • Combined ranking
  • Metadata filtering
  • Geo-location queries
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Built-in Vectorization

  • OpenAI embeddings
  • Cohere integration
  • HuggingFace models
  • Custom model support
  • Image & multimodal
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AI Agents

  • Query Agent for natural language
  • Transformation Agent for data ops
  • Personalization Agent
  • Generative search
  • RAG integration

Performance

  • HNSW graph indexing
  • 8-bit rotational quantization
  • 50% memory compression
  • Sub-100ms queries
  • Billion-scale capacity
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Schema & Types

  • Flexible schema definition
  • Cross-references
  • Multi-tenancy
  • Data versioning
  • CRUD operations
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Deployment

  • Docker single-node
  • Kubernetes clusters
  • Weaviate Cloud
  • Serverless option
  • Horizontal scaling

Why We Deploy Weaviate

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AI-Native Design

Built from scratch for AI workloads. Not a retrofitted database — every feature is designed for semantic search and retrieval.

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Hybrid Search Excellence

Combine vector similarity with keyword search in a single query. Get the best results for any search scenario.

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No Embedding Pipeline

Built-in vectorization modules handle embeddings automatically. Connect your model of choice and store data directly.

Production Performance

Sub-100ms queries at billion-vector scale. 8-bit quantization cuts memory by 50% without sacrificing accuracy.

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Native AI Agents

Query Agent understands natural language. Transformation Agent handles data operations. Built-in intelligence.

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Open Source Freedom

Self-host with full control, or use managed cloud. No lock-in, predictable pricing, and active community.

Common Use Cases

Organizations deploy Weaviate to power semantic search, RAG applications, and AI-driven experiences.

RAG Applications
Retrieval-augmented generation for LLM apps
Semantic Search
Find content by meaning, not just keywords
Recommendation Systems
Similar products, content, and user matching
Knowledge Base
AI-powered document Q&A systems
E-commerce Search
Product discovery with natural language
Image Search
Find similar images via multimodal embeddings
AI Memory
Long-term memory for AI assistants
Anomaly Detection
Find outliers in high-dimensional data

Ready for AI-Native Search?

We can help you deploy and integrate Weaviate for your semantic search and RAG applications.