How does this handle bot detection?

We enable stealth mode on Kernel (onkernel.com), which is a great piece of infrastructure.

We're working on precise benchmarks, but we are much faster than surreal is right now. Chroma is a standalone vector DB so harder to compare exactly, but for vectors we're on par with them for insertions and reads.

Again, working on benchmarks so will put them here when we're done :)

Thanks for the kind words:)

I like what GQL has done. We've definitely taken inspiration from their methodologies in building our own language.

It’s definitely on our roadmap, but not a priority because no one using us needs it. Is this something that would be useful to you?

I'll add memgraph to our benchmarking list! Make sure you join our discord. would love to help in any way we can and hear about any issues you run in to

We went with LMDB because it was a lot faster. But will definitely look over this before we work on our own engine

We started as a graph database, so that's definitely the main thing we want to get right and we wan't to prioritise capturing all that functionality.

We have a python SDK already! What do you mean by generated types though?

Looking forward to putting you at ease :) Working on some proper benchmarks over the next few days.

We are open-source, so you can use and self host us for free. Our plan is to create a managed service (so long as all goes well) which shouldn't be priced any differently from other databases in the space.

We chose AGPL to make sure someone can't make a cloud hosted version of our product, think MongoDB on AWS a few years back.

I can use it for personal needs, sure. Bringing AGPL in a closed-source project is a no go for obvious reasons.

We built this to help people make software that was previously harder to make. If people want to build software and share it with everyone, they are welcome to do that for free, and if someone wants to close-source their project to make lots of money, then they can support the community they rely on by paying a license. :)

No, we are in the process of writing up some proper benchmarks. Our first user used us to build MuskMap and TrumpMap, which went viral on twitter. Not sure how it compared to other graph DBs at the time (bear in mind this was v1 and very bear bones), but it got latency of using Postgres >5s down to 50ms with us.

What are MuskMap and TrumpMap (I'm kind of afraid to ask), and can you link to more info about how they used your database?

They were two viral web apps that blew up on twitter. They had approx 25,000 users at their peak.

Originally they were built on Postgres, so we helped move over to us. Their graph had about 50,000 user nodes and 25 million edges (follower connections). This then made it a lot more optimised to handle the highly interconnected users to find shortest paths between one user and Elon Must / Donald Trump.

So to sum it up, they stored clones of all the users and how they were interconnected by follower relationships, and then used our query language to super easily calculate the shortest paths.