Depends, short term there is a lot of experimentation and innovation in this space obviously. But long term what matters (for investors) is defensible moats. Most or these products look a bit like one trick ponies to me with some features that are extremely likely to be borrowed by competitors, if they work.
Vector databases are not about hosting LLMs or AI models, they are about storing and comparing embeddings vectors. You generate those with an AI model. OpenAI provides a few of those but GPT 4 is not typically what you'd use for this.
Model training and inference is typically not what a vector database does. You need it but to populate a vector database with content. Qdrant is not an exception to this, it uses third party models and inference technology for this (all the usual suspects basically). I just looked at their documentation to confirm this (but do correct me if I'm wrong).
Additionally, it lists all the classic use cases for vector search as its use cases (image search, semantic search, recommendations, similarity search, etc.). I'm sure it's awesome. But in the end it stores and compares vectors using an open source (and possibly patented?) algorithm. Which means if their approach is particularly good and novel, it will get copied in no time by other open source vector search capable products (i.e. most serious databases and search engines at this point). If it hasn't been already. I don't see dedicated vector database products having any inherent advantage here. Rather the opposite since they lack a lot of features that you might also need.
