from Hacker News

Qdrant, the Vector Search Database, raised $28M in a Series A round

by francoismassot on 1/23/24, 10:34 AM with 167 comments

  • by francoismassot on 1/23/24, 1:19 PM

    Congrats to Qdrant's team, $28M for a Series is really nice.

    There are a lot of OSS vector search databases out there, we could probably list the main ones:

    - Qdrant: https://github.com/qdrant/qdrant

    - Weaviate: https://github.com/weaviate/weaviate

    - Milvus: https://github.com/milvus-io/milvus

    What else?

  • by lettergram on 1/23/24, 1:03 PM

    Not to knock Qdrsnt, but generally the whole “vector search database” rush is insane.

    I’ve been working with vectors for over a decade; particularly with embeddings used in AI. We’re talking projects from 100k to 100B+ records, used for AI applications

    Postgres, particularly with pgvector and derivatives, can handle to millions of records very rapidly no problem. It’s very cheap, scales great, and is accurate.

    I’m sure some of these open source solutions are improvements. That said, weigh vendor lock in, cost, risk and in the end it usually makes very little sense.

  • by tw1984 on 1/23/24, 1:22 PM

    I don't think such business model is going to last. There is no reason for AI giants like OpenAI to stick with such external "vector databases". There is not much technical stuff there. Unless you want to argue that "vector searching" is just some labor work when compared to AI, in that case, sure.

  • by clbrmbr on 1/23/24, 11:30 AM

    What’s the mote here? Seems to be a risky investment when it’s such a crowded space and likely to be decent open source alternatives for those with small budgets and homegrown solutions for companies with bigger budgets and requirements.

  • by weinzierl on 1/23/24, 11:33 AM

    A while ago I read in a thread here that they are used in OpenAI's products and at another popular company. I am not sure but vaguely remember X/Grok.

    They are also a Rust shop.

    Who says Germany has no cool startups.

    EDIT: Yes, it was Grok.

  • by mindvirus on 1/23/24, 11:32 AM

    Congrats to them!

    What have your experiences with vector databases been? I've been using https://weaviate.io/ which works great, but just for little tech demos, so I'm not really sure how to compare one versus another or even what to look for really.

  • by tw1984 on 1/23/24, 2:25 PM

    We have to be honest - "vector" database is a low tech stuff when compared to today's AI. You shouldn't be expecting to walk into the battle of AI, which is arguable the most important one in our life time, to dig a chunk of significant profit from major AI players' pocket by just having some low tech stuff. They use external "vector databases" for now because they don't want to invest R&D resources on such non-key issues for now.

    for now is the keyword here.

    When the company grow to 10k or 30k people, there will be teams competing for visibility, someone is going to build their inhouse "vector database" to get his/her slice of the pie. Do you still believe that any AI major player is going to reply on some external vector databases?

  • by hartator on 1/23/24, 3:54 PM

    > For example, it can automatically map ‘frontend engineer’ to ‘web developer’

    Small revolution indeed.

    Ref: https://qdrant.tech/use-cases/

  • by anonzzzies on 1/23/24, 11:49 AM

    The sourcecode is very readable of this product. And good license, no agpl or worse stuff.

  • by anonzzzies on 1/23/24, 11:47 AM

    Offtopic: Is there a good OSS mixed (vectors + traditional) that can be embedded in our own solution and allows storing indexes in a pluggable kv storage? Besides rolling one, I cannot really find anything. Rust or Go would be best.

  • by softwaredoug on 1/23/24, 3:02 PM

    Someone has to ask the question: How many vector DBs do we really need? How do the vector DB companies differentiate themselves? And why do we need a company at all when there are increasingly awesome open source options?

    I genuinely ask - there are a lot of other problems in the RAG, fine tuning, AI/LLM, retireval space, to solve. And more and more vector retrieval is, while not 100% solved, at least is something the community has a grasp on the tradeoffs. Solved to the point that squeezing a bit more recall out of vector retrieval isn't the problem anymore.

  • by shanghaikid on 1/23/24, 11:38 AM

    Congratulations.

    What do you think you milvus? https://milvus.io/. The difference seems significant from the architecture perspective.

  • by infecto on 1/23/24, 12:26 PM

    I am excited to see how the vector search space plays out. Most of my work is not constrained by a low latency chat type user experience and I have not touched most of the vector search apis. I wonder what the difference is between competitors. The way I picture it is everyone is starting up their own Elasticsearch hosted solution and while there are some differences in functionality, the real bet is cost and scale.

  • by redwood on 1/23/24, 11:29 AM

    Anyone using Qdrant in prod?

  • by yujian on 1/23/24, 3:01 PM

    Good on them, I know the crustaceans are out here happy about this raise for a Rust based Vector DB!

    (now I'm gonna plug what I work on)

    If you're interested in a more scalable vector database written in Go, check out Milvus (https://github.com/milvus-io/milvus)

  • by rvz on 1/23/24, 11:59 AM

    Well deserved funding round for a company that underpins most of the AI hype happening all over the place and probably always overlooked by many analysts.

    Let’s see what they can do in a year or more with that new capital.

  • by braza on 1/23/24, 6:23 PM

    Outside AI and LLMs, there are some solid use cases for those Vector Search Databases? Maybe I am not seeing something, but it’s hard to see it gaining traction outside tech companies.

  • by ancorevard on 1/23/24, 5:30 PM

    Honest question, how long before EU makes it unattainable for Qdrant to remain in Germany/EU?

  • by wahnfrieden on 1/23/24, 5:32 PM

    What’s the best vector db for text similarity that can run in browser front ends too?

  • by yding on 1/23/24, 6:55 PM

    Congrats! Amazing milestone.

  • by spullara on 1/23/24, 6:15 PM

    Honestly there is no reason, except huge scale, to have a separate vector db. Every normal database and search engine now support vector search.

  • by _mh56 on 1/23/24, 11:46 AM

    I applied to Qdrant a while back and got this response:

    "We are getting many applications for this position. Usually, a test task would help preselect suitable candidates. However, since we develop open-source software, we rely on contribution.

    You can build an open-source Qdrant connector to another framework or library. The simplest one would be, for example, a Streamlit data connector. But other ideas are more than welcome!

    No limitations and no deadline. As long as this job position is online, we accept submissions. After you are done, send us an email to career@qdrant.com with the link to the repo. We will review it and get back to you asap."

    No interviews, conversation before this email. Hope they see and fix this.

    Edit : No Pay.