Vector database that can index 1B vectors in 48M

I guess for 2D vectors that would work?

For 1024 dimensions even with 8 bit quantization you are looking at a terrabyte of data. Lets make it binary vectors, it is still 128GB of VRAM.

WAT?

1B x 4096 = 4T scalars.

That doesn't fit in anyone's video ram.

Show your math lol

Hey! It's a great question. Co-founder of Vectroid here.

Today, the differences are going to be performance, price, accuracy, flexibility, and some intangible UI elegance.

Performance: We actually INITIALLY built Vectroid for the use-case of billions of vectors and near single digit millisecond latency. During the process of building and talking to users, we found that there are just not that many use-cases (yet!) that are at that scale and require that latency. We still believe the market will get there, but it's not there today. So we re-focused on building a general purpose vector search platform, but we stayed close to our high performance roots, and we're seeing better query performance than the other serverless, object storage backed vector DBs. We think we can get way faster too.

Price: We optimized the heck out of this thing with object storage, pre-emptible virtual machines, etc. We've driven our cost down, and we're passing this to the user, starting with a free tier of 100GB. Actual pricing beyond that coming soon.

Accuracy: With our initial testing, we see recall greater or equal to competitors out there, all while being faster.

Flexibility: We are going to have a self managed version for users who want to run on their own infra, but admittedly, we don't have that today. Still working on it.

Other Product Elegance: My co-founder, Talip, made Hazelcast, and I've always been impressed by how easy it is to use and how the end to end experience is so elegant. As we continue to develop Vectroid, that same level of polish and focus on the UX will be there. As an example, one neat thing we rolled out is direct import of data from Hugging Face. We have lots of other cool ideas.

Apologies for the long winded answer. Feel free to ping us with any additional questions.

I was starting to think this was impressive, if not impossible. 1B vectors in 48 MB of storage => < 1 bit per vector.

Maybe not impossible using shared/lossy storage if they were sparsely scattered over a large space ?

But anyways - minutes. Thanks.

Edit: Gemini suggested that this sort of (lossy) storage size could be achieved using "Product Quantization" (sub vectors, clustering, cluster indices), giving an example of 256 dimensional vectors being stored at an average of 6 bits per vector, with ANN being one application that might use this.

Yeah, the SI symbol for minutes is min, if you're going to abbreviate it in a technical context. Super funky using M.

Thank you, title needs edited.

Legend

Thankfully not months.

No special hardware. Google Cloud vms. We use multiple of them during index building.

Seriously. The amount of lift a SaaS product needs to give me is insane for me to even bother evaluating it, and there's a near zero percent chance I'll use it in my core.

What do you think an alternative is for someone who:

1. Has a technical system they think could be worth a fortune to large enterprises, containing at least a few novel insights to the industry.

2. Knows that competitors and open source alternatives could copy/implement these in a year or so if the product starts off open source.

3. Has to put food on the table and doesn’t want to give massive corporations extremely valuable software for free.

Open source has its place, but it is IMO one of the ways to give monopolies massive value for free. There are plenty of open source alternatives around for vector DBs. Do we (developers) need to give everything away to the rich

Not that locked in - you can just move your vectors to another platform, no?

Vectroid co-founder here. We're huge fans of open source. My co-founder, Talip, made Hazelcast, which is open source.

It might make sense to open source all or part of Vectroid at some point in the future, but at the moment, we feel that would slow us down.

I hate vendor lock-in just as much as the next person. I believe data portability is the ACTUAL counter to vendor lock-in. If we have clean APIs to get your data in, get your data out, and the ability to bulk export your data (which we need to implement soon!), then there's less of a concern, in my opinion.

I also totally understand and respect that some people only want open source software. I'm certainly like that w/ my homelab setup! Except for Plex... Love Plex... Usually.

Nothing for you to see here. Surely you just aren't their target customer.