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

https://qdrant.tech/blog/series-a-funding-round/

by francoismassot • 2 years ago

131 166 2 years ago

qdrant.tech

redwood3• 2 years ago

Anyone using Qdrant in prod?

andre-z6• 2 years ago

Many: https://testimonial.to/qdrant/all

https://techcrunch.com/2024/01/23/qdrant-open-source-vector-...

redwood3• 2 years ago

I should have been clearer in my question: It would be great to hear directly from people who are using them about their successes and what their experience has been like

swalsh2• 2 years ago

I built a little proof of concept that uses it in the RAG pipeline, it's been proving quite useful, so we're just starting the move to production.

It's probably going to stay, but I'm also evaluating databricks new vector store as we're using databricks for all the analytics parts of the app already, and having them all on the same infrastructureis appealing.

crucio• 2 years ago

We are for a few projects. We've been using them for over a year and have been impressed. We have 10s millions of items in there with lots of daily inserts/deletions etc. There's been a couple of gotchas but generally it is quite predictable and scalable.

We use 768 dimensional vectors for our items with several other payload filters (e.g. language). Performance has been good and I think the qdrant team focus on the right features without creeping into other areas.

inertiatic2• 2 years ago

I do, and it's very very rough around the edges to be honest. Lots of things broken, things are even breaking between releases suddenly in unexpected places. Or at least, I'm used to working with more robust data stores. If my work was more high stakes, I'd have already advocated for moving our vector search to something more robust. Thankfully it's not and I can just maintain what we're making with not too much stress, and enjoy seeing this OS project grow from a user perspective (haven't seen a data store go through this very initial phase in my career yet).

Support from the team is great however, and congrats to them for this round!

esafak5• 2 years ago

Please elaborate. What would you have moved to, for example? This is valuable information.

clbrmbr• 2 years ago

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.

jjackson5324• 2 years ago

It's a series A. Thinking about the moat in a hot market like vector DBs is a great way to miss out on unicorns.

tw198414• 2 years ago

the OP argued that it is not a hot market - companies like openai is going to eventually use its own while small players are going to just use openai's assistant APIs, they don't have to operate their own "vector database".

it is also worth to mention that even if there is going to be a market called "vector databases", which is highly unlikely, you can't just written off all existing regular databases and pretend that they are not going to just walk in and take over.

all in all, there is no reason to believe it is a hot market. it is much better to ask is there going to be a market at all.

lyjackal• 2 years ago

Qdrant is open source (with hosted offerings).

mindvirus• 2 years ago

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.

danielbln• 2 years ago

We're using Postgres with the pg_vector extension for basically all of our projects. We know and love Postgres, it has a big track record, the extension is supported on all major managed cloud offerings, no new tooling needed, pg_vector supports HNSW indices for performance as well.

Once in a whole supabase slips into a project, but that's basically just Postgres with some bells and whistles on top.

I got nothing bad to say about Pinecone, We aviate, Chroma etc. but when it comes to dbs, I like to go with the devil I know.

andre-z6• 2 years ago

You should use whatever works best for you unless you face some limitations. The issue is that vector databases are not databases but search engines. It is ACID vs BASE. A few thoughts on this https://qdrant.tech/articles/dedicated-service/

Fendyfd5• 2 years ago

There are multiple vectordb available in the market, open source ones include Milvus, Qdrant, Weaviate etc. Cloud services include Zilliz Cloud (managed milvus), Qdrant Cloud, Weaviate Cloud etc. Try using a benchmark tool to evaluate them. Here is an open-source option for your reference: VectorDBBench (https://github.com/zilliztech/VectorDBBench)

tw198414• 2 years ago

why would anyone use a "benchmark" tool from a vendor (zilliz here) to test the performance of its competitors?

swalsh2• 2 years ago

I've been using Qdrant. Can't speak highly enough of the core functionality. It's fast, good accuracy, easy to use etc.

I think there are some things I wish were easier, for example finding and updating points, and the UI could be better.

bootsmann2• 2 years ago

I think one of the big advantages of qdrant is how easy it is to do a poc because it allows you to have an “in-memory” version of the database similar to sqlite. One of the big competitors, Milvus, comes with a fairly intricate docker-compose you have to spin up to try it.

J_Shelby_J• 2 years ago

Only for python!

avereveard2• 2 years ago

Well sqlite now has a vector extension so it's super convenient for testing. Between that and fts5 sqlite can stand in for any advanced search service as far as poc are concerned.

weinzierl• 2 years ago

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.

sroecker• 2 years ago

Yes, used by Grok: https://twitter.com/qdrant_engine/status/1721097971830260030 Oh wow, completely missed that they're German. Should have noticed their "Impressum"..

treprinum5• 2 years ago

Is Open AI using it in their assistants API for retrieval? Answer performance of those is really bad and retrieval is slow compared to Pinecone.

simonw3• 2 years ago

Yes, it's used for the RAG implementation - though we only know that due to information leaked in an error message I believe: https://twitter.com/altryne/status/1721989500291989585

shanghaikid• 2 years ago

Congratulations.

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

_mh564• 2 years ago

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.

Oras• 2 years ago

Do you expect a tech interview by just applying?

From your perspective, you're filling out an application, maybe writing a cover letter, but on the other side, there are 100+ applications like yours. Not all of them are qualified, CVs are not a trustable source anyway.

That's why companies add tests to filter first, then interview later.

_mh564• 2 years ago

I don't expect interviews. But I also don't want to spend 20 hours working before getting a "Unfortunately we've decided not to move forward" message.

As a thumb rule, I'm happy to put 4x more effort than the company. If they interview me for 1 hour, I spend 4 hours doing the take-home. Anything more feels like exploitation.

0xedd• 2 years ago

Don't be ridiculous.

HR gets paid to talk to candidates. I don't get paid to apply. The initial screening call is what allows a company to gauge the relevancy of a candidate. Let him speak about some of the topics and see how in-depth they go. Either the HR is familiar enough with the tech to understand proficiency (think a student listening to a maths professor) or they let a TL have a short conversation. I've overheard unqualified HR do their jobs badly, too; They laughed at picking them by looks and "feels". But, that's out of scope here.

A large company has millions to invest in different areas. Intrinsically, it has a much larger margin of error. You accidentally overprovisioned some resources and cost the company 10k? Tis but a scratch. You POC some personal project and accidentally get billed 10k? That is not the same.

A company can spend money on hiring. It is expected to. A private person can't spend money on applying to jobs. It isn't expected.

It's interesting to see how the shift goes from the self to the company [and to the country]. A little bit of communist propaganda goes a long way, eh, comrade NPC?

redwood3• 2 years ago

That's a pretty smart way for them to seed the ecosystem with open source connectors! Are you implying that that's what they were really trying to do here? Or do you think it was a genuine filter technique?

simonw3• 2 years ago

If they're paying you for your time, this is kind of smart.

If they're not then it's a scandal.

_mh564• 2 years ago

Edited :|

cj• 2 years ago

They can easily make it not a scandal by changing things so applicants contribute to an open source project that they don't directly benefit from as a business. Easy solution

guappa4• 2 years ago

Why pay when you can get free labour?

infecto5• 2 years ago

This sits somewhere in the middle for me. On one hand I probably would not do this exercise for most companies but I probably would for a company I was excited about. It kind of makes sense if they are getting high volume of applications, you definitely will miss great candidates doing this but does it not also servce as a self-selection for the type of candidate they want?

Curious if the co-founder who was posting in here will share his take.

lysecret• 2 years ago

Yes I agree. I think you could easily add if the pr is merged we reward it with some money etc and it would be perfect.

_mh564• 2 years ago

To give more context from an employee's point of view [just my opinion and might be completely wrong]

For a job switch, I need to spend time in three different stages:

---------------

Preparation:

Leetcode (Blind 75) : 150 hours

System Design + DBMS + OS + Networking : 100 hours

Behavioural Questions (preparing STAR format answers): 10-20 hours

------------

Application:

Avg time for sending 500 applications: 20 hours (Assuming 1 application every 2.5 minutes)

-------------

Interviews:

Let's say I got 25 callbacks and 10 of them asked for takehome.

Person to person interviews time: 25 * 3 = 75 hours

Takehomes: 10 * 6 hours = 60 hours.

-------------

All in all, I'm already spending 415 hours of unpaid work to get x% of salary increment. Not including the side projects or hackathons we may need.

So having a takehome exercise asking to make an active contribution to the company is....bad. Sure i can reject it but not everyone will. which is what led us into the multiple rounds of algo interviews hellhole.

I apologize if what I'm saying is harsh. All I want is for leadership to see us as humans with families and not monkeys jumping through hoops.

pclmulqdq4• 2 years ago

On homework problems for jobs, I have a strict policy of "no more than 4 hours of free work, and I retain full copyrights to that work." A lot of people are picking up on the first clause of that policy, and companies seem to be adapting, but the second clause still isn't common yet.

shizcakes• 2 years ago

I don’t see a single such submission in their pull requests, open or closed, in the past couple weeks.

simonw3• 2 years ago

Read that comment more closely: they didn't ask for PRs to one of their own repos, they asked for a brand new repo to be created, and a link to that repo to be emailed to them.

nijave• 2 years ago

You could always release and license under BUSL or AGPL or some other business non friendly and relicense if you got the job.

LudwigNagasena• 2 years ago

You can simply publish it without any license, in which case you reserve all rights.

osigurdson• 2 years ago

This is actually kind of brilliant.

anonylizard• 2 years ago

Most of the prestigious and elite indie game firms (Those that pay very well, fully remote, have a hugely successful product that can be sold for decades) basically only hire modders into their team.

Like, you had to have actively developed mods for them, for free, for years, and be famous in the community, then they'll hire you (If you want).

This works because the working conditions there are far far better than your average game company. And probably much more fun than say a bank.

mpawelski• 2 years ago

What concrete companies you are talking about?

sireat• 2 years ago

For some reason this does not seem as exploitative as most of the interview circuit elsewhere.

Over the years I've read/heard plenty of stories (here on HN and elsewhere) of people getting hired for their open source contributions to some stack that some company is using/developing.

So here I am willing to give some slack here to Qdrant. They get extremely qualified candidates who can jump right in, and candidates get told the rules of the game up front. It feels fine?

Surely much better than fake take home tests, whiteboard tests, leetcode onslaught, and 7 layers of interviews.

So if creating a high quality repo is 60-90% of your job interview that seems pretty good. As long as they are not ghosting high quality contributions that is.

I will change my view if they get 20 high quality connectors out of this and noone gets hired from that pool of candidates.

wdroz• 2 years ago

No Pay

From this job posting [0]

Compensated Interview Tasks: We value your time and effort; candidates will be compensated for completing interview tasks.

[0] -- https://join.com/companies/qdrant/10275180-core-rust-enginee...

anonzzzies2• 2 years ago

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.

anonzzzies2• 2 years ago

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

treprinum5• 2 years ago

Why is AGPL bad? It would prevent Amazon from taking it from the founders and making money off it without giving anything back, like they did to dozens other products.

JoachimS• 2 years ago

It also makes it less interesting for other potential customers to use it. Reducing the potential market is probably not what a VC funding a startup wants.

menaerus4• 2 years ago

IMHO it looks arcane to understand or to debug, and with most likely a lot of negative performance implications, due to its shared-ptr-in-disguise all-over-the-place design.

    $ git grep "Arc<" | wc -l
    451

It could be probably related to the fact that the main author of the codebase is coming from the Java/Scala world. Or perhaps it's the Rust safety guarantees.

nemothekid4• 2 years ago

Qdrant is a async Rust project, so there will be lots of Arc. Rust safety guarantees doesn't really let share references across threads haphazardly.

rvz• 2 years ago

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.

infecto5• 2 years ago

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.

ankit219• 2 years ago

I think alpha lies in how good the embedding space is rather than which db you use to store and retrieve. A typical tradeoff between accuracy and performance, and here accuracy will be more important in many cases esp for businesses and enterprises. With that, and existing database providers introducing their own support for vectors, this space might be commoditized in near term.

Re embeddings, you would likely get better results if you train your own embeddings model. A popular approach is ColBERT, which anecdotally outperforms vector search in border cases[1]. Second is training an embedding model using initial layers of an LLM. [2]. In Colbert's case once it's trained, you dont need a db to store the vectors.

[1] https://twitter.com/arjunkmrm/status/1744741903646773674 [2]: https://huggingface.co/intfloat/e5-mistral-7b-instruct

infecto5• 2 years ago

I agree with you. I was ignoring the accuracy/performance tradeoff. Even in that space while there is certainly a lot of innovation left, there is already so much that is available commercially open source. If that holds true, you are really left with competing on price and scale in the long run.

lettergram2• 2 years ago

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.

therealdrag02• 2 years ago

What’s you use for 100B records?

lettergram2• 2 years ago

Idk if I can talk about my exact project. But I worked alongside these folks:

https://www.capitalone.com/tech/machine-learning/learning-em...

Running an R&D group in the department.

francoismassot• 2 years ago

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?

andre-z6• 2 years ago

These are the major ones, correct.

mritchie7123• 2 years ago

We use pgvector which if you're already using postgres should be in the running for your use case.

I also like https://github.com/lancedb/lancedb

tajd• 2 years ago

This website for comparing vector database solutions might be handy https://vdbs.superlinked.com/

lmeyerov• 2 years ago

It's funny taking a numbers view. The most popularly used might not even be these, but vector indexes in existing popular OSS DBs and storage systems people are already using. Afaict earliest would be faiss on disk and vectors in opensearch & elasticsearch, and I'd be curious how say databricks, pgvector and other big ones are getting picked up now that they are out. Most of these supported fast & large-scale indexes even early on (ivfpq, ...) by wrapping faiss and friends. ~All OSS DBs we use now, esp managed, have or are getting vector indexes.

Another one most similar to qdrant we track internally is lancedb. They are clever by supporting an embedded architecture, so an architectural reason to prefer over most existing OSS DBs. In our survey 2 years ago, we predicted specialized vector DBs having regular OSS DBs be the elephant in the room, and missed embedded as a fundamentally different category: https://gradientflow.com/the-vector-database-index/ .

(Good luck to qdrant! I'm happy they waited before raising, hopefully this means they can operate more healthily than otherwise and easier to maintain the discipline to do that!)

jillesvangurp2• 2 years ago

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

lsaferite• 2 years ago

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?

manishsharan3• 2 years ago

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.

epistasis2• 2 years ago

It's fascinating to see the diversity of vector databases! I've chosen to prototype with two, ChromaDB, and LanceDB, based on the ease of using embeddings with them, and had not even heard of these others here. I'm also very excited to go through VectorHub's table of databases:

https://vdbs.superlinked.com

(discovered from sibling comment here: https://news.ycombinator.com/item?id=39103322)

daveed• 2 years ago

I think Activeloop(YC) is too: https://github.com/activeloopai/deeplake/

rgbrgb• 2 years ago

I've been looking at this one to embed in desktop apps https://github.com/unum-cloud/usearch

alfalfasprout• 2 years ago

Sure, but frankly it's historically very hard to build a business around a specialized database, especially if you have competitors that are even 80% as good but free.

The cases where I've seen this work are when the DB offers something way ahead of what their competitors offer. For example, KDB+ was historically unrivaled when it came to ultra high performance time series storage and Aerospike is very hard to beat for extremely high performance multi-node K/V.

Otherwise there's little to stop a larger company from offering the OSS competitor to your DB as a service for a lower cost and invest eng resources to close the gap.

shenli3514• 2 years ago

Chroma looks good. https://github.com/chroma-core/chroma 10k+ stars, very easy to use, and can be used as an embedding database

morgango• 2 years ago

Elasticsearch: https://www.elastic.co/platform

bumberg• 2 years ago

Marqo.ai (https://github.com/marqo-ai/marqo) is doing some interesting stuff and is oss. We handle embedding generation as well as retrieval (full disclosure, I work for Marqo.ai)

tw198414• 2 years ago

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.

manishsharan3• 2 years ago

There are huge segments e.g. banking, insurance,legal, which are wary of using OpenAI and they would much rather host their own LLMs. I think these vector databases will find a ready market in this segment

tw198414• 2 years ago

Tell me what makes you believe that those big techs are not going suit those "banking, insurance,legal" orgs by providing them their own LLMs? For example, ever heard about github enterprise? you pay a stupid amount, github setup everything almost identical to the public github, just on your servers for your employees. Why big techs won't do the same here?

Those high profit margin part of the LLM business is for big players only, they don't burn hundreds of billions to offer you opportunties to cut their profit by capitalizing on their core business.

Communism doesn't exist in high tech. People don't work their xxx off to pave ways for your free lunch for life.

mritchie7123• 2 years ago

OpenAI uses Qdrant for chatgpt and a few other products.

https://news.ycombinator.com/context?id=38611608

guappa4• 2 years ago

And has enough money to fork it at any time.

nemothekid4• 2 years ago

Is the idea that big players will get tired of paying a license to a company like Qdrant and write their own database? I just don't really see why they would do that - if Qdrant is similar in complexity to any standard DB, it's like asking why doesn't Apple just rip out MySQL and write their own Apple DB.

I can see them replacing it if Qdrant isn't able to scale to their needs - thats why we ended up with Dynamo, Spanner, MyRocks. However its likely that its probably easier to just acquire the team - like Apple did with Foundation - and become project stewards than trying to invent a new datastore to save pennies.

spullara2• 2 years ago

qdrant is trivial compared to a normal database.

CamperBob2• 2 years ago

There is not much technical stuff there.

I know that, and you know that, but tell it to a jury of East Texas hayseeds.

tw198414• 2 years ago

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?

coffeebeqn• 2 years ago

Are in-house databases that common? I thought generally we’ve found as an industry that to be a great thing to purchase. I do wonder how many will need anything other than the vector support in their already existing Postgres instances though

tw198414• 2 years ago

I do wonder how many will need anything other than the vector support in their already existing Postgres instances though

exactly! if there is a real & strong demand, we'd be seeing open source ones get upgraded & ready in months. it is more like just one of those "I want to build something easy in the core but fancy in its name to get some quick VC $"

Prosammer• 2 years ago

My understanding is that these vector search databases are generally used by people who want to use an existing AI and extend it with RAG etc. Was anyone ever expecting major AI players to use a tool like this as you are suggesting?

tw198414• 2 years ago

use an existing AI and extend it with RAG etc

companies like openai will fill the gap by offering such features out of the box. there is no logical reason why and how a big AI tech giant is going to take all hard work and letting someone else to take the profit by ignore the last mile issue. in fact, openai has already released such APIs in their last devday event.

PheonixPharts• 2 years ago

Can you name me a single 10k-30k people company that has their own internally built relational database? Their own internally developed document database? I've never seen this in my career.

I don't even know any 10-30k people companies that build their own search, most I've known use elastic search or lucene.

It's had to imagine that a few of these vectordb companies don't establish themselves as the standard solution, being the equivalent to MongoDB in their space. The other competent players will very likely get acquired.

Certainly these vectordb companies are in a better long term standing that the bajillion companies rushing to build products that are just calling an API endpoint at the end of the day.

tw198414• 2 years ago

please read my reply again, I was talking about AI companies (e.g. openai) are going to build their own vector database when there have a large enough team.

for your question, google built spanner (that was like 12 years ago) and leveldb when it had about 30k people. Meta/facebook forked leveldb and started building rocksdb when they had less than 30k people.

no one is saying any banking, insurance, retail companies with 30k employees should be building their own databases/search engines. They shouldn't. That is actually my main argument - those companies shouldn't get too involved for infra like that, they should just take the offerings of the big tech companies.

naveen99• 2 years ago

Vector databases are complementary to “today’s AI” as they store and index embeddings which are a key output of large language models. As LLM’s get better at generating text and images, their embeddings also get better.

yujian• 2 years ago

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)

andre-z6• 2 years ago

The open-source benchmarks show different results. Feel free to make a PR to improve. ;) https://qdrant.tech/benchmarks/

softwaredoug2• 2 years ago

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.

inertiatic2• 2 years ago

Solved to the point that squeezing a bit more recall out of vector retrieval isn't the problem anymore.

I think this is a bit of a strawman. I don't think recall is the main point these systems are trying to sell us on, it's more about robustness and ease of use compared to building something inhouse or using a lower level library to build a system on top of it just for this small part of your overall project/product (be it RAG, search, whatever).

I guess Lucene-based solutions, while very mature overall in terms of engineering, lagged behind this functionality (out of caution, trying to build what's going to be long term useful) and are also perceived a bit too cumbersome. So these stores do make sense, I think. The core functionality is nothing too complex (at least HNSW), but hiding it behind a stable black box with just a few inputs and levers, has value for people that are likely to use these stores.

sanp• 2 years ago

Agree but then the same argument applies to RDBMSs and multiple vendors seem to be doing OK in that space. I think it ultimately comes down to "stuff" (sales journey, price, support etc.) other than the technology itself. I am sure any RDBMS can meet most of the requirements of any given customers (in most cases) but we still see customers buying across vendors.

esafak5• 2 years ago

qdrant is open source. Being open source is not in opposition to running a company; it is part of their strategy.

There is still work to be done in vector databases. None of the products have perfected hybrid search yet, for example, and performance varies a lot between products; they are not fungible.

hartator• 2 years ago

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

Small revolution indeed.

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

avereveard2• 2 years ago

I really don't understand that sample the similarity capability is provided by the external embeddings model not by quadrant per se, unless they have some proprietary embeddings.

minimaxir• 2 years ago

Correct, but that one-pager is more aimed toward project managers than engineers. Marketing copy is weird like that.

ancorevard• 2 years ago

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

wahnfrieden• 2 years ago

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

spullara2• 2 years ago

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

braza• 2 years ago

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.

esafak5• 2 years ago

Vector databases enable semantic and similarity search. What company does not need that?

beernet• 2 years ago

Companies that don't want/can build it by themselves, so the majority of enterprises. It's a nice series A by the numbers, at the same time, generating relevant revenue will very likely not happen (given the valuation at this round was probably around 200M€). It's hype all over but can't blame them, would do the same I guess.

yding• 2 years ago

Congrats! Amazing milestone.

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

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