Very curious about the hardware setup used for this benchmark!
No special hardware. Google Cloud vms. We use multiple of them during index building.
Vector database that can index 1B vectors in 48M
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Re: Vector database that can index 1B vectors in 48M
#22Earlier quoted context omitted.
Nothing for you to see here. Surely you just aren't their target customer.
So who is? Who really needs to index 1 billion new vectors every 48 minutes, or perhaps equivalently 1 million new vectors every 3 seconds?
If HNSW were accurate enough (and if this DB were much faster) then I'd have a use case. I wound up going down a different route to create a differentiable database for ML shenanigans though.
Re: Vector database that can index 1B vectors in 48M
#23Not trying to be snarky, just curious -- How is this different from TurboPuffer and other serverless, object storage backed vector DBs?
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!) th…
I’m curious, what’s the tech stack behind this?
Re: Vector database that can index 1B vectors in 48M
#241B vectors is nothing. You don’t need to index them. You can hold them in VRAM on a single node and run queries with perfect accuracy in milliseconds
Show your math lol
Re: Vector database that can index 1B vectors in 48M
#25Proprietary closed-source lock-in. Nothing to see here.
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…
Traditionally the most profitable approach is offering enterprise support and consulting.
Re: Vector database that can index 1B vectors in 48M
#26How is this different from running tuned HNSW vector indices on Elasticsearch?
Re: Vector database that can index 1B vectors in 48M
#27Earlier quoted context omitted.
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…
Traditionally the most profitable approach is offering enterprise support and consulting.
Enterprises are so very fond of choosing novel open source technologies, too!
(not)
Re: Vector database that can index 1B vectors in 48M
#28There was recently this paper: https://arxiv.org/abs/2508.21038
They show that with 4096-dimensional vectors, accuracy starts to fail at 250 mln documents (fundamental limits of embedding models). For 512-dim, it's just 500k.
Is 1 bln vectors practical?
Re: Vector database that can index 1B vectors in 48M
#29Re: Vector database that can index 1B vectors in 48M
#30Very curious about the hardware setup used for this benchmark!
No special hardware. Google Cloud vms. We use multiple of them during index building.
The question is how many, and what kind of VMs you use? It greatly affects performance :)
I run a lot of search-related benchmarks (https://github.com/ashvardanian) and curious if you’ve compared to other engines on the same hardware setup, tracing recall, NDCG, indexing, and query speeds.