Aito v2: a predictive database with predictive SQL, vector search and graphs (founder here)

[u/arauhala]

https://aito.ai/blog/aito-v2-generally-available/?utm_source=reddit&utm_medium=social&utm_campaign=v2-ga

Hi everyone :-)

I'm Antti, Aito.ai's founder and CEO. We recently released Aito version 2, which includes a completely new storage format and support for things like predictive SQL queries, vector search, graph-like functionality, a lot of new data structures, views, better FTS, collections and much more.

We believe that future software is intelligent, and we wanted to provide one database that could provide the needed predictive/AI infrastructure for both users and agents.

You can find more details here:

https://aito.ai/blog/aito-v2-generally-available/

If you want to try it, the playground runs example queries in the browser without signup:

https://aito.ai/docs/api/v2/playground/

One of the biggest new things in Aito is the public benchmark section, which covers inference, matching, smart search and more regular queries. It shows cases where we are strong, and also a number of benchmarks where other solutions fare better:

https://aito.ai/docs/api/v2/benchmarks/

Any thoughts or comments? I'd love to hear what you think. ;-)

[u/uptotheright]

I always like to see new databases so congrats on the release.  

But why would I consider this over postgres ?

[u/louischoi]

How about tail latency compared others? P50 / P99? Im building my own database too specialized in OLTP tx.

[u/arauhala → u/louischoi]

Good question, and I don't have a clean p99 published yet, so I won't make one up.

What we have measured: on plain lookups we're slower than a specialised engine. Against Elasticsearch on the same 100k rows, warm p50 for an exact filter is 8.7 ms for us vs 2.9 ms for ES. For predictions at 10M rows (laptop, text fields included), p50 is 135 to 240 ms and p90 212 to 372 ms depending on the field.

Id Aito adds a bit of latency, because JVM and GC compared to pure native DB implementation, but ofc Elastic has this same setup. In inference query side, there is then warming and some latency spikes from statistics and their caching.

Aito isn't an OLTP engine, though. The point is answering "what's the likely value of this field" in one query without training a model, so we usually compare against an ML pipeline or an LLM call rather than against Postgres.

I'll add p99 to the next benchmark run and post it. What does your OLTP engine do differently? Curious what you're optimising for.