Short answer: Ben Lerner says Espresso uses ML models to rewrite code, shift workloads between clusters and run the right machines at the right time, like Kubernetes powered by ML. Today it works with Snowflake and Databricks SQL.
Watch this answer from Episode 27 (starts at 6:00) · Episode 27 notes and transcript
From Episode 27: We Let AI Control Our Data Warehouse — The Results Were SHOCKING, starting at 6:00.
Jordan Metzner [5:59]: Yeah. I would love to. I'd love to just get, like, the one minute spiel about you and your business.
Ben Lerner [6:04]: Yes. I'm I'm Ben, one of the founders. I'm the CEO of Espresso AI. The goal of Espresso is to use machine learning to optimize compute. So we use ML models to rewrite code and make it faster. We use it to shift workloads around between different clusters to make sure things are running as efficiently as possible. We use it to make sure that you're running the right machines at the right time to get the performance that you want, but without overpaying for unused or underutilized capacity. So for the more technical folks in the audience, you can think of it like Kubernetes but powered by ML. Today, we work specifically for Snowflake and for Databricks SQL, but we're planning to expand to other data warehouses and other compute generally in the future.
Jordan Metzner [6:52]: Got it. And so, essentially, so so if I understand this straight, let's give a case of somebody on Snowflake. The reason they're able to save is that they're buying they're they were previously overpaying for how much, like, compute they need for their data warehouse. And by optimizing that through, like, scalable some type of cluster system, they're able to save significantly and then just buy and I don't know enough about it, but just buy basically on a license basis versus, like, on a hosted basis. Is that, like, how the switchover happens per se?
Ben Lerner [7:20]: No. So we hook up directly to people's Snowflake accounts. And Interesting. Really, everything that we're doing is stuff that our customers could do directly, but we're doing very fine grained piloting of exactly how much compute do you need at any given second, where should this query be running versus, like you know, instead of running all of your BI tooling on one cluster, sometimes you may be able to shift it around. And, like, I wouldn't necessarily say people are overpaying. I think it's more that, like, people have been trying to make computers run faster and more efficiently since, basically, since we've had computers, and this is kind of a next step in that and really leveraging ML to get even more performance out of it. So, like, once you know how to do something better, you can look at the old way and say, oh, yeah. Well, this is more effective. But it's not like people are doing something wrong per se. It's just that the technology's gotten better.
Jordan Metzner [8:15]: Yeah. Cool. Okay. Now I totally get it. That's awesome.
