The Exchanges

Q Okay. Um, we'll jump more into that in a second, but I want to take a step back. Tell us about the product. What does data stacks do and what's the business model? Is it pure play SAS or is it pay as you go based off your usage?

A Yeah. So data stacks is a leader in data management for cloud applications, which we'll talk about, have a whole different kind of scale, um, and, and set of requirements for the database. Uh, the way we make money is we sell our, our flagship product, DataStax Enterprise, uh, on an annual subscription basis, and you can consume that either as a service, where we have a managed service that we'll do for you, or you can consume it where you manage it, and you may decide to run it in the cloud, or you may decide to run it on-prem, but we have about 60 to 70% of our workloads are, are already being run, uh, in the cloud, either with us managing it or our customers managing it.

AI assessment note: “we sell our, our flagship product, DataStax Enterprise, uh, on an annual subscription basis”

Q Okay. Now, I mean, dive into that. I mean, how'd they ask you, was this an email? What was the subject line? Or was it a phone call? I mean, how'd that work?

A They were in Austin and I was living in Dallas. And so it was a nice, easy drive down there and, uh, got to know them and the team. And as we started talking about how we might be able to help them with tooling and such, It just naturally led to conversations about the market and where are you guys going and how are you developing? And, uh, they had a technical founder and a semi-technical founder who was doing the CEO work and it was the, uh, Matt File who was the semi-technical founder who came and said, you know, I think I could learn all this stuff. But I'm not sure I should learn it as fast as I could, which would be best for the company, and so I think you would be a great fit if you were interested, and the rest is history.

AI assessment note: “Matt File who was the semi-technical founder who came and said”

Q Interesting. Um, interesting. So it's a land and expand approach. Okay. So how does that first person inside one of these lines, these teams that represent one of these lines, how do they find you?

A Our primary persona with whom we engage is a role called a data architect. And this is the person who's responsible for fitting together the entire data strategy of a set of applications or, or a given application. And that person, uh, generally knows right now, I think that among sober minded people in the industry, there's a very good agreement that Cassandra, which is our core technology, is the de facto scalable industrial grade performance database. And so they kind of know that to begin with. And then because we are the company who essentially Uh, Bill Cassandra, if you look at the amount of contributions we gave to open source and all the marketing we did, then it's a very natural path to us.

AI assessment note: “because we are the company who essentially Uh, Bill Cassandra... it's a very natural path”

Q Okay. And you obviously just did that kind of all together as a, at the company level at your individual customer, a hundred grand first year ACV level. What do you know you have to drive expansion level to, or what can you accurately predict ACV is going to grow to in year two? Is it You know, net, you know, one, 21, 20% growth or something different.

A It depends again on the nuance of what project they're implementing. So as an example, sometimes customers will start, we have two very large buckets of use cases. Uh, one bucket is around customer experience. So this is a set of use cases like customer three 60 recommendation engines, fraud detection, rec, um, personalization. Um, many times what they'll do is they'll start very granular with one of those projects. And that expansion might take a couple years to really get that first project off the ground, get it solid, and then move on to the next one. Another class of applications we have is around enterprise optimization. These are things like supply chain, inventory management, asset management, security access, all those things internal to the company. Um, those tend to happen much quicker. So that project could be a big project initially with a very rapid expansion. So I see what you're trying to ask me, but unfortunately, it really does kind of blend into the overall company metric because there's too much bespoke activity per account depending on what project they're trying to take on.

AI assessment note: “It depends again on the nuance of what project they're implementing.”

Q and it's a success. But if Lightspeed wants to go raise their next fund, or Sequoia, or Jason, or whatever, it's nice to be able to point to you guys as a case study where they've gotten money back somehow. Have you figured out a way to create liquidity for early shareholders by replacing them by new investors so you can keep the, you know, their story a good one?

A Yeah, we, great question. We, we've been very blessed in this regard. Um, that anxiety that you mentioned is also directly tied to the success of the funds that you're already in. And in our early investors, their funds are performing extraordinarily well. And so we don't have that pressure from them. They are not on my back saying like, geez, when are we going to get this thing going? Because they look at the health of the company. They see that it's on the right trajectory. They see that we're growing well at a material number. They see we have optionality ahead of us. And they don't want to rush it and do something silly now that you've gotten to this point. And that's really nice when you have investors that have had success in their funds. That they don't feel that pressure necessarily. I think as much from maybe their LPs because their funds are performing so well. So we've got a, we've got a really high class, a lot of investors. And, um, the truth is I, I do not feel that pressure from them.

AI assessment note: “we don't have that pressure from them. They are not on my back”

Q What are you paying to acquire these data architects as a customer? What's your CAC on average, would you say?

A Uh, we don't reveal CAC in that manner, but I can tell you that it's a combination of a couple of things. So one is the open source trail, which by the way, you, you can't really draw a direct monetary line to, because the whole point of open source is a lot of times it's all about, um, come and get things without having a direct connection. Um, you know, so in other words, you don't want to be pestered, Nathan, you want to be an open source person. You want to be anonymous. You want to go grab the open source code. And then you find us through many things that we do on our training, like data stacks has a site called data stacks Academy, which provides free world-class training. Many people find us through that as an example. So you can't really measure the cap directly in that way, but we also have a direct sales force who, who also goes after a direct enterprise accounts as well. So it's a combination of the two.

AI assessment note: “we don't reveal CAC in that manner, but I can tell you”

Q Wait, Billy, come on, let me do it. Billy, I have a big question for you. When are you, when are you filing to go public?

A Shocking. I've never heard that one. Uh, and I'm sure you've never heard the answer. We don't talk about those kinds of things. We don't reveal those futures, but what I can tell you is Uh, because our cash is in our control, then that, that creates a ton of optionality for us. We don't have to worry about racing to markets to get money. Um, but what that does mean is we're, we're also very efficient in the way that we look at our unit economics around our accounts. And so for us, this is partly why we focus so much on enterprise. Number one, the use cases lend themselves much better to our technology in big, hairy problems in enterprises. But number two, the unit economics are infinitely better than SMB. So we stay very focused on, uh, getting that land very efficiently, but then Making sure very quickly, how fast can we make that first project successful? What we find is, once that first project is successful, the expansions happen much, much faster. Now, the inverse of that is true as well. If we don't get that first project right, it can really retard the progress on, on expansion. Because this is still new, Nathan. A lot of these technologies are still new. There's a lot of eyeballs in the company, in the customer company, that are waiting to look at that first project to say, should we double down on that, or should we stay away a little longer?

AI assessment note: “We don't talk about those kinds of things. We don't reveal those futures”