Comment by jillesvangurp | Hacker News Reader

Comment by jillesvangurp | Hacker News Reader

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There are a few more. Pinecone comes to mind.

And then there are traditional databases and search products that are integrating vector search capabilities as well: Postgres, Elasticsearch, Opensearch, Solr.

They each have their limitations of course but the 28M round suggests a moat that I'm not seeing that clearly in terms of tech. What's so special about qdrant relative to their competition?

At least they are Apache licensed for now. So, that's nice. But that also means e.g. Apache Lucene could borrow some code from them to beef up their vector search capabilities. Which would benefit Elasticsearch, Opensearch, and Solr which all depend on Lucene.

Which raises the question what the point is of QDrant long term and why investors are betting on this as opposed to other things.

It seems to me that the main challenge with vector search is inference cost (at index and query time), not storing the vectors. A secondary concern is the vector comparisons at query time. A good way to cut down on that is to reduce the overall result set using traditional search or query mechanisms. In other words, you need

I think there will be enough of market to justify a few more dedicated VectorDB vendors.

From the enterprise perspective, which of these vendors proved the best combination of security, availability, performance and pricing will matter. when we run benchmarks on our (self hosted) LLMs, we do not a clear idea of where we have bottlenecks and we end up assuming its the GPU/memory. And our pilot implementation will never go into production as the security model is nearly non existent in our implementations; the execs AND qa are getting the same RAG outputs. It is all very new to us and our teams. If a vendor can outperform its competition in our tests and show credible security model with segmentation of knowledge, that would be the choice.

I couldn't agree more. I would add reproducibility to the list of important things, above everything else you mentioned. I looked into Vanna after seeing it on here because generating SQL code by only embedding the schema and business logic seems like a nice, quick middle ground that doesn't require embedding an entire database. However, 88% accuracy in generating the correct query isn't good enough for deployment at the organization-level. "Give me sales for the last quarter as of end of prior month" should return the same result for everyone, without exception.

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.

With regards to competitors picking similar things up, we've this article explaining why a dedicated vector search service is a good idea: https://qdrant.tech/articles/dedicated-service/

Is Pinecone OSS?

I ask because this was the statement from the PP

   There are a lot of OSS vector search databases out there, we could probably list the main ones
   ...
   What else?

Pinecone is not OSS.

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