We have multiple teams kind of working in silos so we haven't really consolidated on a single enterprise solution yet. That said, the team I'm on has consolidated on using qdrant with different collections. We've also started using a sort of hungarian notation in collection names as we've just ran into the problem of multiple embedding models.
Curious to hear what criteria each team considers important (top-3) for choosing the VDB they chose. There are so many vector databases available and in my experience it's actually not the most critical component in the overall GenAI/RAG architecture, although it gets the most attention.
