The Exchanges

Q How many people do people spit up consecutively? So we have currently, I guess is the concurrency.

A So there's, there's three metrics that we look at. And so one is like time to spin up one. And so our time to spin up one is 60 milliseconds with network agency. So requests, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you want to spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2000 seconds, which is 30 minutes. Like there's different variations of that. And then there is that. So it is speed of one, speed of like multiple, and then how many can you consistently have up and running? And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they were there just shy of a million every single day that they're running. Um, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.

AI assessment note: “takes about 75 seconds to spin up concurrently 50,000”

Q Yeah, weird pockets. So actually, but that helps you to distribute your load through all time, you know?

A Yeah, so the interesting thing is that we have those kinds of loads, but if you look at the researcher loads, they're quite different. So what they are is like, if you give them concurrency of 10,000, or 50,000, or a 100,000 CPUs, whatever it may be, when they fire off a, a run, it's just a hundred percent, and then just runs, runs, runs, and then it stops. So it's very, The usage pattern is squares basically, right? And it's also not follow the sun because people will fire it off at midnight before they go to sleep, but then wake up and so it's very unpredictable. So you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a fall of the sun, even if you have a high growth company, you can sort of predict your usage patterns and, and have enough Capacity for that because it's sort of, it grows in a, in a way you can project when you have companies doing sort of like evals and RL, they're super spiky. So they're going to come in. It's like, we're going to use nothing. Then can we have a 100,000, right? And then go back down and then have a thousand and get back down. So it's very, very different. Right.

AI assessment note: “we have those kinds of loads, but if you look at the researcher loads”

Q So this is if you go above a hundred percent, right? Like your overflow. If your overflow like spillage or whatever, you probably lose money on it, but it doesn't matter, right?

A Well, you might, you might not, that is a more cost effective way to do it, but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just in time compute, um, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine. And you can do that, but if your customer, and especially for, let's say the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at what? A hundred percent the entire time. And so when you're running runs on CPUs, when the, when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down.

AI assessment note: “Well, you might, you might not, that is a more cost effective way”

Q But they're kind of dropping the bucket, right?

A I guess, I think it's like sort of all the things come together, and so there's so many things that, that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, uh, from Anthropic, so, or Claude, Claude code was very interesting. The reason why it was interesting is that a lot of Let's call them app. I don't know what to call them app layer agent companies. Essentially. They are like, oh, I can create this new app, uh, this new agent. All I need, I just use cloud code and I throw it into a sandbox and then I have my interface to the human to that. And so that enabled so many more companies to actually offer this. And then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP versus the CLI, I mean, the MCP is an interface against an API, whereas the CLI is like, you can actually go do things like, yeah. This is the difference between integrations and actually running scripts or data or analysis against the thing. So being able to use CLI very, very well enables the agent to do more things. And it's because people will invoke a sandbox, they'll run in the CLI and, but it'll do analysis on that data and then give you an actual result versus just, you know, pulling data from an APS source.

AI assessment note: “To your point, like OpenClaw wasn't huge for us, but like having the agent SDK”

Q And yeah, you have differentiation versus many of these, but like what sells them?

A The thing that we found that sells people the most, this is more maybe a day two thing instead of a day one thing. And we've seen this again and again. So we have a bunch of case studies and we have a bunch of them still coming out. They're all done by third party. So we don't do the case studies. And it's actually interesting to watch those cases. I watched the recorded and because it's a third party, people are actually more open and they will tell you, oh, we use this competitor or we like this competitor more or this thing or whatever. And the, the number one thing that people come back to us for is that our, we have an insane responsiveness.

AI assessment note: “the number one thing that people come back to us for is that our, we have an insane responsiveness.”

Q have any other topics. I, I, I think this is like as best and Comprehensive, like, if you have, like, any questions about the compute market, uh, and sandboxing in Daytona, like, this is the best place to start. Where does this go, man? Like, you know, we're, we're here in April. Things are going 75% month to month. Like, where are we going to be by end of year?

A It's an insane number. I'm sort of scared to say it out loud. So it is, it's very big. Um, just the sandbox market on, and we, there, we talked about this in general. The entire infrastructure market is wearing 40% plus or minus month over month. Everyone is wearing 40% a month. And that's also a hot take is like, if you're not growing 40% ish, it's not that it's just the market. You might as well, you don't have to come to work. You'll grow that amount. Basically. I'm half kidding, but you know, that that's where it's going. And so where does it end? We will see the thing that I think about from, from at least a CPU perspective, GPU is even crazier, but a CPU perspective is like, there's a high probability that actually owning the CPUs beforehand will be a A go to market tactic. Um, and it will probably cause I, you, as you do probably talk to a lot of GPU providers, their growth is hindered by the amount of GPUs that you have, right?

AI assessment note: “It's an insane number. I'm sort of scared to say it out loud.”