What stands out most about Weaviate is how well it handles both vector search and agent-based workflows in one place. The Collections feature makes organizing and querying data intuitive, and the Agents functionality adds a powerful layer for building AI-driven applications. For personal projects, it strikes a great balance between capability and ease of use, you get enterprise-grade vector database features without needing a complex setup

I really loved Weaviate's native hybrid search feature. It's perfect for handling complex queries in our insurance claim documents, where adjusters often need to use alphanumeric policy codes with natural language questions. This feature combines BM25 keyword scoring with HNSW vector similarity out of the box, which is fantastic. Another aspect I like is the recent upgrade to the Python client (V4 API), especially the type hinting, which integrates seamlessly with our Python codebase. This has made development much smoother and easier for us. I also appreciate Weaviate's ability to manage both data residency and security constraints effectively and the option to tune infrastructure to control storage costs.

I like that Weaviate makes it fairly straightforward to work with vector data and search through it. The documentation is useful when setting things up, and the dashboard makes it easy to keep track of the projects and collections. It also works well for experimenting with AI and search-related use cases.