Short answer: Ben Lerner says FinOps means doing work to review budgets and dashboards, while Espresso plugs into your data warehouse and optimizes in the background, aiming to cut a Snowflake bill in half without customers touching anything.
Watch this answer from Episode 27 (starts at 2:44) · Episode 27 notes and transcript
From Episode 27: We Let AI Control Our Data Warehouse — The Results Were SHOCKING, starting at 2:44.
Ben Lerner [2:43]: I don't know if the clapping comes through at all on the mic. This is phenomenal. This is really, really well. Yeah. This is great. This is a super fun quiz. It's the, like, sort of thing that people should be thinking about. I think pretty ironically, like, this is a something that points to, like, a really common set of products in the space, but the solutions are, like, very different from what we actually do. So, like, this starts touching on, like, FinOps and accounting best practices and all the kind of stuff that, like, comes to mind with really, like, any expensive bill in a business or even, like, at home. Right? You're like, well, you should look at your budget. You should figure out what you're doing and why. You should figure out if you can be doing it better. Maybe we really don't need that dashboard to refresh, like, 10 times a day if it's checked once a week. You know? Oh, like, honey, like, we haven't watched Netflix in six months. We should just shut it off. Right? So it's it's those sorts of things that go into, like, financial discipline and FinOps best practices. In a lot of ways, this is, like, exactly the opposite of what we actually do.
Ben Lerner [3:49]: So we plug into your data warehouse, and we shift everything around. Like, we're not opinionated on, like, what you're running or why. We try to do everything in the background to make things as cheap and efficient as possible. And I think that, like, you're paying for convenience is, like, pretty accurate. But the flip side of that is always, like, how much are you paying? Right? Like, everyone we talk to is, like, overworked. Like, this, like, this is a great quiz. And, like, if this bill number is right, like, this is probably a team with two data engineers. They have way too much on their plate. And what we do is we come in and we give you a button, and we're like, okay. Well, you're you know, can we get your CFO's BPM down by just cutting your bill in half without you having to think about anything and without you having to touch anything? And we kind of, like one of the things I say to people is, like, yeah. You can do all this work to save money, but, like, man, like, if your snowflake bill were 50% lower, like, would you care, or would you do other stuff? So a, this is, like, awesome.
Ben Lerner [4:53]: I'm, like, very impressed that you guys put this together. Thank you. But, b, I think it's, like, a nice call out of, like, you can go do a bunch of work to lower your bills. And to some extent, what we try to tell people is, like, you can and you should do that, and at scale, you will need to. But you can also not do a bunch of work, and we can lower your bills for you using AI magic.
Jordan Metzner [5:14]: No. I think that's that's a great way to look at it. I know, like, we just had a GPU cluster that stayed on, and we had some AWS overage, and, it almost feels like, man, I wish I would have, like, I wish I would have had some protection against that and put some, better guardrails in place, so that wouldn't happen next time type of thing.
Ben Lerner [5:35]: Yeah. So, like, the analogy there is, like, we can't help you if, like, your ML engineers are running up their GPU clusters, but we can definitely help you if they forgot to shut them off because we could tell there's you know, they're underutilized and we'll help with that. So, yeah, I think it's different ways to get to the same goal. But, like, not doing work is usually better than doing work.
