weaviate
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
💡 Why It Matters
Weaviate addresses the need for efficient data retrieval in machine learning and AI applications. By combining vector search with structured filtering, it allows engineers to implement complex queries on large datasets, making it particularly valuable for ML/AI teams. With a steady growth of 585 stars over 96 days, it demonstrates stable community interest and is considered a production-ready solution. However, it may not be the best choice for teams that require a simple key-value store or those who do not need the advanced capabilities of vector search.
🎯 When to Use
Weaviate is a strong choice when teams need to perform hybrid searches that combine vector and structured data queries in a scalable manner. Teams should consider alternatives when their use case does not involve complex data retrieval or if they require a more straightforward database setup.
👥 Team Fit & Use Cases
This open source tool for engineering teams is ideal for data scientists, ML engineers, and AI researchers who require advanced search capabilities. It is commonly integrated into applications that involve recommendation systems, image search, and information retrieval platforms.
🎭 Best For
🏷️ Topics & Ecosystem
📊 Activity
Latest commit: 2026-02-13. Over the past 97 days, this repository gained 585 stars (+3.9% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.