00:05:43 · Ron Gabrisko, Guillaume Moubeche
[00:05:43] S0: Yeah. I mean, originally, we had, you know, community edition, which is kinda like our free edition. Right? And people would sign up for that. We mostly would just reach out and say, hey, just, you know, saw you signed up, like, you know, is there anything we can help with? Are there any technical challenges? And so if they ran into, hey, can you help me configure this or set up this or, you know, I have a memory issue with this. I mean, you just add a little bit of value, you you gain trust. You know what I mean? Versus like, hey, can I come pitch you on why you should buy Databricks? And so very different approach is, you know, you gotta give to get back. And so that's what we started with, just, you know, very small, like, reach out to try to help folks, and, you know, that worked really, really well for us. [00:06:28] S1: So... And to build up on what you're saying, I think, like, what you did very differently, it's like in 2017, I think, like, obviously, every SaaS company in the world were selling per seat and selling seats. You built basically a consumption model instead. Can you walk us through, like, how exactly did you build, like, that's like, consumption model, and how exactly did you choose where to price, what to keep free?
00:07:01 · Ron Gabrisko
[00:07:01] S0: Yeah. No. That's a great question, because I think one of the early... I mean, Databricks is a innovation machine, you know, in terms of product, but we innovated quite a bit on GTM too. Right? Like, I think we're the first pioneer to build a cloud service around Open Source, and that became, like, how you monetize Open Source, and so we kinda created that playbook, for the market. I think, you know, a lot of startups, and early stage companies, what you wanna do is tie pricing to your most basic unit of value, and for us that was consumption. You know, seats has limitations, especially, you know, there's so many people in an organization, but, you know, tying ourselves ourselves to consumption when data's growing, queries are growing, you know, AI is... And agents are gonna do even more queries, like like that was kind of a genius move that we did as a team, And the market, especially around cloud technologies, was kinda going that way, and so that's where we started, you know, our first kind of proprietary product was our data science notebooks. So we kinda created this entire category of data science, so, you know, we would enter in through kind of the machine learning and data science teams that were building these models to do predictions, and, you know, so we grew a lot from kind of bottoms up with that specific product, and so we would call it open source core, right, where we continue to put more and more in open source, but maybe keep some of the proprietary features so that we can continue to build a business, and then we keep putting those in open source, and so, you know, people have, you know, listen, the great part about open source is you're not locked in, you can move away if you need to, and so we kind of embrace that community and that interest, but also built a business around it with other things we thought had value. So...
00:09:00 · Guillaume Moubeche, Ron Gabrisko
[00:09:00] S1: At the same time, you know, like, what's interesting in what you're saying, you know, about open source is the fact that, as you said, like, people can move if they wanna move. But at the same time, like, if you look at your retention number, I don't have, like, the the exact one, but I'm assuming it's, like, north of a 130, 40, something like this. [00:09:22] S0: Yeah. We're north [00:09:23] S1: of that. Okay. So Yeah. That's, like, super impressive, which means that revenue keeps expanding, your churn is extremely low. So how how do you think the the two are are connected or maybe more specifically, like, how do you make sure that you give enough value to people so they actually, like, stay longer?
00:09:23 · Guillaume Moubeche, Ron Gabrisko
[00:09:23] S1: of that. Okay. So Yeah. That's, like, super impressive, which means that revenue keeps expanding, your churn is extremely low. So how how do you think the the two are are connected or maybe more specifically, like, how do you make sure that you give enough value to people so they actually, like, stay longer? [00:09:43] S0: Yeah. I mean, part of it's just, you know, customer obsession, making sure customers are successful and they love the company, and, you know, I think the entire company, including engineering and sales, are trying to make sure that, you know, they love Databricks. But the bigger part is we didn't just do project. So a lot of open source companies, they build one product, and then they stop. And it's hard to be sticky because then they can probably use that, you know, elsewhere. What we did was we built, you know, Spark, and then we built Delta, and then we built MLflow, and then we combined Delta with Iceberg when we bought Tabular, which was also open source. We bought Mosaic AI, which was open source. Like we continue to do these open source projects and so now it's a fully integrated data and AI platform that's all open source. Right? And so I think that's the key to retention is you can't just have one trick. You gotta, you know, keep layering on these new innovation engines, and that's what we've done really well at Databricks, right, is, you know, created this innovation factory where we're constantly, you know, reinventing ourselves and releasing new technology. So...
00:09:43 · Ron Gabrisko, Guillaume Moubeche
[00:09:43] S0: Yeah. I mean, part of it's just, you know, customer obsession, making sure customers are successful and they love the company, and, you know, I think the entire company, including engineering and sales, are trying to make sure that, you know, they love Databricks. But the bigger part is we didn't just do project. So a lot of open source companies, they build one product, and then they stop. And it's hard to be sticky because then they can probably use that, you know, elsewhere. What we did was we built, you know, Spark, and then we built Delta, and then we built MLflow, and then we combined Delta with Iceberg when we bought Tabular, which was also open source. We bought Mosaic AI, which was open source. Like we continue to do these open source projects and so now it's a fully integrated data and AI platform that's all open source. Right? And so I think that's the key to retention is you can't just have one trick. You gotta, you know, keep layering on these new innovation engines, and that's what we've done really well at Databricks, right, is, you know, created this innovation factory where we're constantly, you know, reinventing ourselves and releasing new technology. So... [00:11:02] S1: And as a CRO, like, how much involved are you in the decisions of launching, like, a new open source project or even acquiring an open source project?
00:11:02 · Guillaume Moubeche, Ron Gabrisko
[00:11:02] S1: And as a CRO, like, how much involved are you in the decisions of launching, like, a new open source project or even acquiring an open source project? [00:11:15] S0: I mean, a lot of that's driven by Ali and the founders, right, like, they have specific cultural things they're looking for in acquisitions, as well as technology fit, and, you know, I would say our seven founders are visionaries for this space. I mean, they teach at, Berkeley and MIT and Stanford, and most of the, you know, other innovations come out of those schools, and they've probably taught most of those students. But, you know, they're... So they have their own vision on where they wanna go with the data and AI platform. My my, role in that, I mean, I do get involved in the acquisitions to understand the sales teams, customers, you know, even try to sell the, you know, the the companies on why Databricks versus going alone. But they're really making the decisions on the technology and the acquisitions. The part GTM solves is like, you know, recently we released Lakebase and Genie and Lake Watch, and so how do I get, you know, all the enablement done? How do I make sure we know how those products add value, how they differentiate with other products in the market, those are massive innovations, for us in the market, and so, you know, we wanna market share and grab customers and get customers using those as fast as possible, and so a lot of the new product introduction materials and enablement and all those things, we go pretty fast on that stuff, so.