Weaviate is an AI-native vector database that empowers developers to build a new generation of software. It is designed to bring AI-native applications to life with less hallucination, data leakage, and vendor lock-in.
Key Features:
- Open Source: Weaviate is fully open source, providing transparency and community-driven development.
- AI-Native: Optimized for AI workflows, enabling efficient vector similarity searches and data object management.
- Hybrid Search: Combines vector and keyword search techniques for improved search experiences.
- RAG (Retrieval-Augmented Generation): Facilitates building trustworthy generative AI applications using your own data.
- Agentic AI: Supports the development of scalable, context-aware AI agents.
- Cloud, Model, and Deployment Agnostic: Runs anywhere and integrates with existing and future tech stacks.
- Flexible Cost-Performance Optimization: Offers efficient resource management tailored to specific use cases.
- Integrations: Seamlessly integrates with popular language model frameworks, cloud platforms, and data platforms like Google Cloud, AWS, Azure, Databricks, and more.
Use Cases:
- Hybrid Search: Enhance search experiences by merging vector and keyword techniques.
- Retrieval-Augmented Generation (RAG): Build reliable generative AI applications using your data.
- Agentic AI: Develop scalable AI agents for enterprise intelligence.
- Cost-Performance Optimization: Optimize AI infrastructure for real-time results, data isolation, and cost management.
