AI Agents Need Computers: 74% MoM Growth, 850K/Day Runs, & New Agent Cloud — Ivan Burazin, Daytona

I've never experienced this, that people literally call you if you do not give them access. Like, they want access- ... right now. And so it's like, okay, they don't want this. The thing that they want doesn't seem to exist, or they have not found it, and they really, really want wh- what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for every single agent that will exist ever in the future is just like, what is that market?

How big is that?

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Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.

Thanks for having me, man.

Ivan, you and I go back.

Way back.

I, I don't even know how, um, you found-- like we... Did you reach out or, uh, for Shift or?

I reached out to you. The reason was you-- we were just-- we were thinking about-- I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of like, local hosts should die.

Oh, yeah, yeah.

And you had this article. Um-

End of local hosts.

And then I reached out to you because of that, um, and then we talked. And I was actually at a different job and learning about-- I was the head of like developer experience, and you were quite well-versed in that and actually reached out to you among other people. Like how do we, how do we go about that?

What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.

I don't remember.

I remember because I was with my then, I was thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great. And I was late 'cause we were, um, in, you know, Italy on, uh, vacation, and then I was late for something. I felt so bad, and you were so nice to be, uh, good about that.

The, the reason I'm nice is because I'm also late to other people, so it's like, you know, who's, who's without sin here? Yeah, so I have to... You know, for, for those who don't know, uh, info bit Shift, there's this whole thing that, uh, you did in the past and y- that was basically one of the inspirations for me starting AI Engineer, which is like, you know, I have to thank you for giving me that push to be like, "Oh, you can, you can build and sell conferences?"

Yeah. And I remember you asked- You asked me at the beginning to give me advisory shares, and I was so focused on what we're doing, I said no, and I should have took the advisory shares-

Yeah, yeah

... so I'm sorry, dude. But anyway, um-

We're not, we're not venture backed, you know. It's-

This is it.

Yeah. A- anyway, so I, I think what's interest- impressive about you is that CodeAnywhere is the thing that you've been trying to build and, uh, you know, you, you kind of put it on hold and then came back after, after, after Infobip. Just... Can I just give us the story, do you-- sto- the story and the origin story going into Daytona?

Sure. Like really way back, me and my co-founder have been together-- I've said this multiple times. It's like we were married and divorced and married. Some people actually ask me is, is my co-founder my partner. Like they thought it literally. It's not literally. But we have done multiple companies together and w- to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift and then back to, uh, Daytona.

We originally started stacking, stacking servers, doing like virtualization in the early two thousands and, you know, routers and doing basically all these things, um, at a foundational level. And that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right? Um, I say the first before us was actually Heroku.

They did it for a very, very short time until they became Heroku. But outside of them, we were the only one, and it was called-

There was Cloud9.

There was Cloud9 that came out slightly after us. There was, um, Replit which came out-

Yeah

... w- when we stopped doing it, Replit came out and they have been successful since then, which is great. There was Nitrous IO. There was quite a few that existed in time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, for those that still remember VS Code, um, for, for, um, there was no Kubernetes and Docker had just started when we-- or I'm not sure if it was even public at that point in time.

And so we had to build everything to the whole stack ourselves. And that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about three million people used CodeAnywhere. It was slightly, it was angel backed more than venture backed. We ended up paying everyone back because it didn't have that sort of scale.

But you know, three years ago we started something similar with Daytona, which is not what we were, what we are today, but it was automating dev environments for human engineers, the basically the underlining stack of CodeAnywhere. And then we-- we did a hard pivot last January to sandboxes.

Yeah.

And so here we are.

Historic pivot and you know, it's, it's one of those things where like I had independently invested in, in CodeAnywhere, but also in E2B and then both of you pivoted into the same thing and I'm like, "Fuck me."

You invested, you invested in Daytona, you invested in Daytona. But-- and you were the first. If we had not got your check, we wouldn't have done it.

No way.

No, it was like we have to get him on board first, and you were that kicker that we-- that, that got us on the ship.

No, because you, you were putting me on your pitch deck, man. I was like, "Man, this is like a good trip if I don't invest," like .

Well, that's because it was your quote. It's like we-

Yeah, it's the end of local hosts

... we did a bunch of research about end of local hosts and who was interested in that, so.

Yeah, yeah. No, that's like, um, I put-- I wrote that blog post and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and like talk, talk it, talk through it with me. Uh-

It's finally happening

... so yeah, it's super interesting.

It's finally happening.

It's finally happening.

It's finally happening.

With maybe sort of non-human users.

Yeah.

Yeah, yeah. Um, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.

You're wearing the shirt. Yes, that-

Uh, it, it says-- I think your branding is very good. Like it's very consistent. It, it runs AI code. Like it cannot be simpler.

Exactly, but we're gonna probably have to change that because-

Oh, it's

... it's also a subset of what we do. Unfortunately, we really love this. Uh, Run That Code is super simple. People interpret it different ways. I think we've given out five, six thousand of these shirts. People wear them with pride because it doesn't really market to About us-

Yeah, they say this on the back

... on the back, and markets to the person about the person itself. So I think we did a really good job on that one. But it is also a subset of what we do because people, when they think about Run:ai code, they just think about these small, let's call it isolates, code execution boxes that, you know, you send some code, you get an output.

Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes-

Yeah

... which can be misleading.

All these things, all these things on the-

Yeah, exactly. 'Cause it-

Yeah

... it can be misleading 'cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, you know, my wife is an architect, so she has like a Windows with 3D graph- graphics card inside to do 3D rendering, like computers or different compositions of computers.

And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks, and so we offer that basically through an API.

Yeah. To give people-- I, I'm trying to sort of front load all the aha moments or the wow moments so that people can, uh, stay engaged and click like and subscribe. Uh-

Click.

The market is, is exploding, right? Like, uh, you have been reporting 74% month-on-month growth, and it also, it's just been going for a while. Like, it's been going like this. And every single-- It's not just you guys, it's, it's every single-

Everyone, yeah

... uh, sort of, um, compute provider. I don't know if you agree with me saying compute provider or not.

Sure.

But yeah. So it's, so it's like organically PLG-driven growth, but also, also enterprise is, is doing super well. Um, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you, you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?

The insight that made us do a pivot is the, the quarter before that, so end of 2024, when we had-- Basically, we did a demo with-- I don't... I think we discussed this as well. Um, Devin was not public. You actually gave me access to Devin at that time. So Devin-

I did?

Yeah, you gave me-

I don't think I was supposed to.

Yeah, exactly.

Yeah, I-

So it doesn't matter.

Yeah.

You're...

I, I gave like three, three friends access.

Yeah. Um, or it was a cold call and you showed it to me. It doesn't matter.

Yeah.

Uh, but Open Devin was available, which is now called Open Hands. And so we're like, "Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, uh, Open Devin and launch that as a SaaS." And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, "Hey, my agent needs a compute sandbox runtime," whatever you wanna call it.

I, I forgot what it was called at that point. And then we were like, "Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go." And what we found really, really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, you know, the sandbox we're building for agents.

People were like, "Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things." But we saw that everyone we gave it to, it was like 20, 30 people, they all said, "No." Like, "This is not what we need. This, this sort of breaks." And basically, me and my co-founder not knowing a lot about-- 'cause we're infra people, we're not AI people.

So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get... understand what's going on.

Do, do you wanna shout out who, who else was useful? Uh, just, just in case people are also looking.

So, uh, generally we-- Uh, I looked at- There, there's a few of podcasts, d- different segments and different types. So there's you guys, No Priors. Bill Gurley's was great while-

VC, yeah

... yeah, um, while, while it was around. So there's a few. 20VC is interesting from a different dynamic, um, and some are different dynamics. But there was, uh, also Redpoint's-

But, but we're not really about the compute market.

It was also already-- Sorry?

I guess you're, you want-- You're looking at the agent infra market.

I was looking at the agent market in gen-- the AI market in general, and sort of understanding who are the players, what's the perception and how that goes. And like obviously, you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening.

And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MC, uh, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 a.m. or something like that. I was doing-- I just put my, like, baby daughter and wife to sleep and, you know, Happy New Year's and go back to just, um, doing this.

And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, "This is absolute garbage." "Do not show this to anybody at all, but the idea is good." And so he took two weeks and he rebuilt it.

Well, did it like look like that? Listen, like it was rough, rough again.

Oh, not, not even cl- not even close.

Oh, okay.

Like it was mi-- it was way worse.

Okay.

But it was like a very... It was a simplistic view of what it should be like. It worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, um, to go and he ki- came back with this version. We then called all the people that had said like, "This is garbage," you know, a quarter ago.

And we set up these calls and we gave it to-- uh, we just sent it to everyone. And all the calls went long. Every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, "We need... We want access." There was no login, just an API key 'cause it was just a beta or an alpha.

And they said, "Oh, we want access." And we're like, "Sure, yeah. Okay, thank you very much." But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, "Where is my API key?" Like, everyone wanted it. We're like, "Shit, like this is it." Like, I've never felt...

So one, the, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never experienced-- I've done multiple companies in my life.

I've never experienced this, that people literally call you if you do not give them access. Like they want access- ... right now. And so it's like, okay, they don't want this. The thing that they want doesn't seem to exist, or they have not found it, and they really, really want what, what we want. And then when we understood That we're onto something.

And then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or, or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, "We are all in on this."

And so that is where we made sort of the cut between the old product and the new one.

Yeah. But it wasn't composable at the time.

So it was very-- It was basically just a Linux box that you could change, that, that you could define number of CPUs, disk, and RAM. Like that, that is what you could do. But you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things.

Yeah.

It was just a, just a first sort of variation of that, yeah.

And was it bare metal from the start?

It was bare metal from the start. And so the interesting thing that we thought about right away, so our-

Which, you know, give people the background, what is the norm-normal path?

Yeah, so, uh, basically, most, most providers run this on top of V-VMs.

Yeah.

And also-

Firecracker and...

Yeah, yeah. Um, they run a Firecracker on VM. And so we also fire-- We can get-- We have multiple isolation layers and we can do that. But the, the common way to do it is that they, one, that the state of the machine or the, the, the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever.

So most of them are preemptible, like they can... There's a time that they can live. And so our thought was, when we were going into this, is agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state.

So you-- agents would want that, like to pause and come back. They want those two things. But also agents really, really want speed, right? Can they get it? So when we thought about it, it's like we need something insanely fast, how to make it fast, how to make it long running and stateful. And so those two things, it's like combining a Lambda and EC2, right?

Those two things together. And so we didn't have an idea how others did it, because we didn't know too mu- that, that there was a market around this. It was more like, "Okay, this is what we need, what they need." And we looked at Kubernetes, it wasn't sp- it wasn't good enough for that. We looked at Nomad, it didn't enable that.

And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, "Oh, the learnings from there," and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, "Dude, what is this? This is like two thousand and eight. Like, we went back in time."

And he's like, "Exactly." And so the reason why Daytona is like super, super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler. Um, we use the underlying, uh, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, you know, there's no network between an EBS or something like that.

But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, um, you, you're es-essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine and it's local.

There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with and that's what we created.

Yeah. I should maybe-- I don't know if you endorse this, but there's someone does compute SDK, you guys do very well on there, uh, with like the TTI, right? I, I guess. Um, i-is this a, is this a rele- is this a relevant metric for you guys? I, I don't know.

I, I don't know, and it changes every day. So today-

Yeah

... Arkill is like-

Well, I don't know what Arkill is, never heard of it.

Yeah, Arc-- Um, yeah, so it is there-

But, but you are, uh, you know, uh, at least a third of the, the next tier of performance and then, uh, you know, there's, there's a lot of other better-known names that are very slow to start.

Yeah. We've been the number one by far for a long time, and now there's difference. There's different definitions also of sandboxes, different isolation patterns, different other things. So Arkill d- runs it literally on the S3. Um, the data, so it's very different, and they spin up a sandbox, uh, spin up a container for that, so it's a different type of thing.

Yeah.

So the definition of a sandbox is something that we can all-

Yeah

... we all need to get along with. But yeah, we're insanely fast on getting these things, uh, up and running. And so you can see even there that it's a zero point ten to zero point eleven, so like-

Close enough.

Yeah.

Yeah, yeah. I mean, what else do you need, right? Like-

Yeah. So the benchmarks itself, so, um, in the s- in-- I don't think the benchmarks equate to market ownership or revenue or anything like that. And I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.

It's table stakes. It's just like the-

Exactly

... roughly-

But it doesn't hurt. Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely-

Yeah

... one of the top-- Because this is only one dimension of what customers look for. There's other things of like how many can you spin up consecutively, there's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, um, on the benchmarks.

How, how many people do people spin up consecutively?

So we have-

Or concurrently, I guess, is the concurrency, right?

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 sixty milliseconds with network latency. So request, spin up, reply, sixty, the whole thing, sixty milliseconds. That is one. But if you wanna spin up fifty thousand at once, we are now at about seventy-five seconds.

So it takes about seventy-five seconds to spin up concurrently fifty thousand. Some others, there's public data around this, like take two thousand seconds, which is thirty minutes. Like there's different variations of that. And then there is the th-- 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 eight hundred and fifty thousand every single day is sort of where they're, where they're 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.

Yeah.

So that's an interesting-

And they pay by like vCPU seconds and whatever.

Per seconds, exactly.

Yeah. Yeah.

Yeah.

The other thing is, I guess the sleeping and the resuming, 'cause it's, it's all the stateful resumption of, of all these things. What kind of workload are people putting through this, right? Like, do we measure by gigabytes in memory, gigabytes in storage? I, I don't... In, like, n- you know, network attached storage. I, I... You know, what, what, what are the costly ones of, out, out of all these features?

The, the mo- the, the most expensive thing are CPU.

Okay.

The second one-

Yeah, of course. Yeah

... then it's RAM, then it's disk. We actually don't charge for-

Which is snapshotting, right?

So no, you know, it's actually the, uh, snapshotting is part of it, but basically the size of your hard disk of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the, the transference of that. Right now, currently we don't charge for, um, network at all at, at Bullshit.

Oh, you gotta, yeah, you gotta fix that.

Yeah. It is very much a lar- it's a larger and larger part of our bill, so we're working around that part there. Obviously, that is the, the least, um, expensive, um-

Yes

... so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, 'cause we don't charge the customer, and then hard disk, um, is how it's spun up. But there's also different types of workloads. So we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents.

Okay.

And the other is basically RLs and evals, which I put sort of together. And so they have very different patterns of usage. And if you look at the usage of a background, and I'll just name names of companies, um, not specifically, so-

Yeah, Open, Ohands.

Yeah. So like a background agent is a Cognition, a Lovable, like all these things are-

Yeah

... Harvey, these are all long-running, uh, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest and the midnight is the lowest, and then weekends are lower, you know, weekdays are higher.

That's, that's a fun question. How global is it, you know? Is, is it very US-centric or?

So, well, US is a large part, but we have currently, we have Asia, Europe and Eur- and the US regions.

Quite, quite global.

Yeah, it's quite global. We have it all. It's interesting that our num- I talked to you a bit about this. Our number one city by user-

Hmm

... is Singapore.

Oh, wow. Amazing.

Which is an interesting one, right?

Yeah, yeah.

Not by revenue, just by-

Yeah, yeah

... just, like, by individual head count.

Yeah.

Just kind of interesting, interesting thing.

Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, you know, 7, 8 million population.

Yeah.

And it's it's like sh- keeps showing up.

No, it's quite interesting. We were quite shocked and I was like, "Oh, this is interesting."

Yeah.

And also one that's not there-

There, there's a reason I'm doing AI Singapore. I mean-

Exactly

... it's because I'm from there

So we're there. We're gonna, we're gonna be there as well.

Yeah, yeah.

Um, and it's interesting that Japan is in the top or like-

Yes

... Tokyo's in the top, which is in all the tech cycles, it has never been.

Yeah.

It has never been. So it's quite interesting that-

I think the Japanese just love AI.

Yeah.

Yeah. It's, it's that, and then it's Brazil.

Yeah.

Right.

But, but Brazil has always been in the-

I-

... but even when I look, if you look at like GitHub's data and us historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in the sort of the top-

Yeah, yeah

... the top.

Weird, weird pockets.

Yeah, so it's very global.

Okay, so, so actually that, but that's helps you to distribute your load through, uh, all time, you know?

Yeah. So the interesting thing is- ... like we have the, those kind of loads, but if you look at the researcher loads, they're quite different.

Yeah.

So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs, whatever it may be, when they fire off a, a run, it's just 100%.

Mm.

And then it just runs, runs, runs, and then it stops.

Mm.

So it's very... The, 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 that... 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 follow 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, 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 gonna come in, it's like, "We're gonna use nothing, then can we have 100,000?" Right? And then go back down, and then 100,000, go back down. So it's very, very different, right? And so-

So do you want to lock them into commits so that-

Yeah, yeah, we do

... yeah, okay.

We li- so we have to lock them into some sort of commits to have that capacity because we have to have, basically we have to have the capacity for peak.

Yeah, yeah.

Right? And so right now, Daytona's mean utilization is 15%, 1-5.

Oh my God.

So it's very low. But-

Because it's very spiky

... but it's very spiky, but we get up to 90%.

Yeah, yeah.

Us- so we have these things. And so what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agents where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, um, as a compute provider in the agent space.

Yeah.

And when we were doing the conference recently, and so we talked to like Nikita from Neon and the, um-

I should bring it up

... Parag from Parallel and whatnot. Everyone has the same problem, whereas the usage is super spiky. And this is something that has not happened before, that you have these types of sp- Like it was always, it w- the amplitudes were not this high, right? So it's quite interesting use case and problem solve.

Yeah. I don't know if we're gonna bring this up again, but let's just pr- talk about the conference. Uh, you had like 1,000 something people at the Warriors game, uh, at the... Sorry, where is it? What's the-

Chase Center.

Chase Center.

Chase Center.

I went. It was, it was very impressive. Obviously, you can, you know how to throw a conference. What did you learn? You know, you, you put, you pulled together all these impressive names.

Yeah. When I-

And, uh, what were you looking for?

My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents and so can do that. And what I found, this was a, a theory, it wasn't proven, is that we all have these problems, as I touched onto.

And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, um, in human ... compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking to, um-

Lin, I guess, Nikita

... uh, Lin, Nikita, all those. Lin especially, I was talking to her the other day as well. Like the, the... it is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a l- bunch of geos and basically as users work in different places and depending on your tier, they can move you around the geos.

Yep.

And so that how, that's how they get to the higher utilization. But you can sort of predict these and it's... if it's something and you'll rarely get a spike that is 10 orders of magnitude. Like you'll get a spi- like let's say one of your customers has some ex- like an exponential curve, what is that to s- to...

I make, I'm using Cloudflare as an example, 10%, 20%? Whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10X, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So you have-

So she also has the same thing.

So yeah, yeah. I, I know specifically that like Neon had that issue as well, like how are we solving these spiky loads and things like that.

Yeah.

'Cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, um, which is like-

Let me, let me double-click on this. Okay, so for example, Neon, I happen to know that they're very sort of S3 oriented, right?

Yep.

Like, so they're j- they're just like fully bet on S3.

Yeah.

And you get to benefit from S3's distribution and, and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lin maybe has to care a bit more because obviously she's doing GPU inference.

Yeah.

And, uh, for listeners, we did an episode with her, uh, one and a half years ago. And, and you have to care.

Yeah.

But like, right?

So Parag cares for sure, um, and Nikita does.

And Parag is CEO of, uh, Parallel.

Parallel, yeah.

Former CTO of Twitter.

Twitter, yeah.

Uh, they are the search-

Yeah, they're search, yeah

... I just, uh, you and I know.

Yeah.

The l- the listeners don't know.

Yeah. Um, we can put it down in the screen. Um, and so because when we were talking-

I mean, I put it up on the, on the screen-

Yeah, right

... so, so people can look it up if they need to.

Can look it up. And, uh, y- yes, but they still have CPU and RAM, uh, allocation that you have to have-

Oh, yeah

... up and running. And so CPU, RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either overprovision and you can handle the bursts or two, you basically have, I don't know if this is a term, just-in-time compute, which is like, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them-

Yeah

... up and running.

So this is if you go above 100%, right?

Yeah, this is the-

Like your, your overflow

If your overflow, like spillage or whatever, you do that.

Yeah, you, you probably lose money on it, but it doesn't matter, right? Like-

It... Not... Well, you might, you might notl- that is a more cost-effective way to do it-

Yeah

... but it's a slower way to do it.

Yeah.

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, 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, 100% the entire time. And so when you're running runs on CPUs, when the CP- 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, right?

Yeah.

And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.

Yeah. Let's talk about the different workload, right? You said that, um, what was it? A few months ago you, you had zero RL workload.

Yep.

And now it's 50%.

It will be this one 50%, yeah.

Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here it's probably all the same code, you're just doing parallel runs or something, I don't know.

Yeah, so you'll have multiple, depends on the like for each run you'll have a snapshot. And for the most part they actually do use our declarative image builder, which is like, oh, the agent wants these dependencies, these env vars.

This one, yeah.

Yeah, the declarative image builder. It essentially-

Which is a very modal like thing it is.

Yeah, and so we build it on the fly-

Yeah

... and then we propagate that snapshot, and then you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also, also be automated. It's like, oh, now for the next run we need to install these things or remove these things or whatever to get, uh, a task done, and then it goes off and, and runs that.

So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes.

Yep.

So EKS, GKS, whatever, that is what the vast majority run on. And anyone that has tried Daytona versus GKS, EKS is like, "I'm never going back."

Mm.

That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running.

Um, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much, much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM or out of memory our sandboxes because we can dynamically on the fly-

Resize

... resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, um, the Terminal Revenge team is, uh, brought us actually, so thank you Alex and the team that brought us into this whole space. Um-

It is just very, very, very rare that, uh, you know, a, a framework would just say, "Guys, just use Daytona."

Yeah, I think it says it somewhere, yeah.

Yeah, I was like, "What is this?"

There's all-

Yeah

... there's multiple there, but they also mention a few other places.

Yeah, yeah.

Um, and so Daytona specifically, we have, uh, just jumping on themes here.

Uh.

We... I don't know where it says Daytona, so-

I, I...

Doesn't matter

... I, I, there's a very, very strong recommendation which is like very unusual-

Yeah

... which is, it's...

We do not pay them for this, just so you know.

No, yeah, they just like you.

Yeah, they like us. Um, yeah, and also a thing, so, uh, Daytona has multiple isolation Sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, um, that's hardened with Sysbox. So it's Docker's isolation that is a security equivalent to a VM, but it's still a container, and that is the default.

And they, especially in these training workloads, really like that as an interface to be able to use just a, a basic Docker container and reenable Docker in Docker, which for these RL runs, if you need to do a Docker Compose or Kubernetes, you can spin up a K3s inside of these things, which unlocks a huge amount of workloads they can do that you cannot do on other providers.

So just on, on that part is much more interesting. And so we went that through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the, uh, the bench people-

Yeah, and Harbor, yeah, and Harbor people.

Right.

The Harbor people. Do you know, are they, are they a company yet? Are they-

As far... I do not know.

Okay. All right. Yeah. It's, it's, like, super obvious that, like, you know, there's a lot of excitement and success around, around these things. Tell us more, right? Like, uh, this is an exploding workload. Harbor adopted you, which helped speed this thing along. But what are you learning as this new workload comes online?

Sure.

And-

There's a couple things that we learned, which we ch-chat about in the beginning. We-- And this has led our story, as we mentioned, we, like, talk to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And I think it's, it's that the ecosystem is so small and/or the, the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week.

Mm.

It's, like, very bizarre. It, it happens so many times, which is like-

Because they're all friends. They all-

Sorry?

They all, they're all friends. They're all in the same group chat.

Yeah, they... Probably. Yeah.

Yeah, yeah.

'Cause then they're like, "Oh, can you do this?" And I'm like, "Okay, this is interesting. We'll put it on a feature request." And then the next one's like, "Oh, can you do this? Okay." It's all the same, right?

Yeah, yeah, yeah.

It's always the same. And so what we try to do, and I personally try to do, I try to be on as many, quote-unquote, "sales calls" I can. I'm in every Slack channel. We literally have about 1,000 Slack ch- Connect channels, something like that. It's an interest-- There's so many interesting things you find out when you have all the Slack channels.

You can also see where people transfer between companies. You see "Leave Slack channel," "Enter Slack channel."

Oh, yeah. Yeah.

It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be.

Yeah.

You have a list and-

LinkedIn charges you to, to, you know, use your own connections, but Slack doesn't, right?

Yeah.

Slack is like, "Do it for free. It's more lock-in. It's great."

Yeah. It's, it's amazing, right? It's one of those-

You're gonna pay Slack for life.

Exactly. You're there for life. So that's interesting. And so one of the things, the, the newer things we were talking about earlier is we made a big bet and, and put a lot of investment on computer use.

Mm.

That is not seen publicly the light of day. We haven't GA'd that yet. Uh, but we have-

Is there a thing I can pull up?

There is computer use there. It's right up a bit.

Oh, yeah. Okay.

Yeah.

Cool.

And w-what we have, what we talked about and what we've seen publicly is there's this theme now about, like, the human emulator where... And Elon from, XAI, has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools.

Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, you know, terminal or, or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US-

Mm

... 50 trillion worldwide-

Wow

... something like that. And if we look at the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US, it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very, very long time.

Like, people just won't in-invest in that. How much of it? Our assumption is the following: if, like, in the RPA market, which is similar market, but not the same-

Yeah

... 25% of, like, these white collar workers, um, work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, like, 40%, right? And so if you take 40% of that, you get to essentially, like, $10 trillion-

Yeah

... a year.

That's a 10.

That is a 10. That, that, that is a 10. So that's the 10 of the models, right?

Yes.

That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, um, either Mac or Windows or Linux. Linux, we also obviously have and others have, but Windows specifically is something very, very new, and the only option right now is an EC2 with Windows or on, on, on Azure.

Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, like, point-in-time snapshots. You have, like, forking. You have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's, that's been our big push and bet, but we've sort of, like, kept our ear to the ground.

What is sort of the next things in the market?

Yeah. Knowledge work and building a-and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing.

Mm-hmm.

And it was, like, a very hard... Isn't it, uh, Eastern European?

It is, um, Romanian.

Romanian? Yeah, it might be the only Romanian, uh, big unicorn.

Yeah.

Okay. Yeah. These... I, I don't I don't, I don't have, like, a... I think there's a stage being set for the resurgence of RPA because everyone understands that, yeah, no one wants to deal with these shitty apps, and no one's gonna rewrite them. Like, you just have to do, like, a remote operation and programmatic operation of them.

But, but if you wanna unlock it, like, my own setup was, was basically the following. So I was doing a board deck-

Yeah

... recently, um, last month, whatever, and I'm like: Okay, let's just, let's just do automated. So, like, all our data's in, like, ClickHouse and PostHog and QuickBooks, like, where everyone else's is. And I'm basically, like, connected that all to, like, um, my cloud code, like go off and go cloud network-

Yeah

... whatever. Go off and, like, "Here's integrations. Go do that." It pulled out the first report, which was great. It connected to Brex and all these things. Pulled out, which was great, and then I say, "Okay, now pull out this, this, this and this." And I kept getting, like- Really well McKinsey-style design reports, but the data said partial data-

Mm.

Like all the missing data, partial data, like it can't access all the things, and I got so frustrated. And so I got, I got, you know, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company.

And so I would say, "Now go and do this report," and it'll get the same or like, "I can't via the MCP or the API or whatever. I can't get all the information." I'm like, "Go log in."

Yeah, yeah.

And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I s- to get value, like I get immense value right now, but it, it has to be a computer usage, unfortunately.

And so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools still needs a computer agent, what hope does, you know, Goldman have? To have a headless, right?

Yeah, yeah. Why isn't Microsoft doing this?

Well, I'm pretty sure like, uh, Satya had a post yesterday-

Oh, okay

... which was like-

I didn't see

... every agent needs a computer.

I see, I see.

So they have launched something.

Yeah, they have Microsoft Power Automate. Uh, I'm sure, I'm sure like, you know, they, they're gonna have their version.

Version of that. Yeah, yeah.

And, and you're gonna try to do yours. And it-- I, I always know there's always demand for Mac, but I know it's like tricky to host Mac OS sandboxes.

So we will have macOS sandboxes fairly soon. The problem with macOS, uh, OS sandboxes is, I'm deep in this, I don't know how much interesting is this.

It is-

macOS has this problem.

It's a licensing thing.

Licensing thing.

Yeah.

So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every twenty-four hours. So if you come in, and theoretically, if I wanna charge you per second, and I charge you one second, I have to have it idle for the rest of the day.

Yeah.

Like I can't have anyone else doing that. So the pricing will be different in the sense that we would have to charge for twenty-four hours, and that's not even, that's not even the most difficult thing. But the, uh, thing above that is from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine.

And so what you can do in like Windows world or Linux world is that I can move in the background your snapshot from one to the other and, and manage load, right? Here, if you wanna do that, you essentially have to have your-

Yeah, snapshots

... your, your-

It's like a-

... physical machine

... you, you can't break it up.

You can't move-

Yeah

... things around that, and all of that is, that, that, that part is like from a security standpoint, if it is written. They're like, I understand the security aspect of that, but it en- disables you from doing these agentic, like really-

Mm, mm, mm, mm, mm

... scalable agentic workloads.

You need to do a vibe-coded clean room implementation on macOS-

Mm-hmm

... that you can then-- There's like Clean OS or something, I don't know.

I guess so. We have to-

You know, 'cause like Linux was originally like a clean room rewrite of Unix.

Okay, yeah.

Or something like that, right? Like, like same, same thing to macOS. Someone needs to do it.

Someone, someone will do that. Some- we'll have some long-running agents for a few days to figure this stuff out. But yeah, so definitely we-- we're really close to offering something 'cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.

Yeah, nobody's gonna use this.

Uh, um-

I mean, like the, the labs, the labs will because they want to automate-

They have to do RL

... macOS.

They have to do RL again. But the point is with the RL part, i- if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, uh-

Yeah

... later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get-

Mm

... if it would just enable a s- concurrency model similar to what you can get on a Windows and a, and, and Linux.

Yeah.

Yeah.

Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's -

Yeah

... and maybe, maybe they'll change their mind with the new CEO.

Yeah. We'll see.

We'll see.

High hopes.

High hopes.

High hopes.

Okay. But I, I mean, I mean, it's very clear the market opportunity is huge in Windows, and you can go for a long time on, on just Windows. Um, but your customers are gonna want both.

Yep.

It is interesting to me that this is the, the s- the sort of God application of, of, of, um, of agents, right? Like I, I don't-- It was-- How big was OpenClaw for you guys? Like was it, was there like a significant bump or-

Not for us-

Because you already-

Okay. So we're-

Yeah

... kind of positioned differently.

Yeah.

Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C.

Mm.

Sort of it's either B2B or B2B2C.

Mm.

So like in the researcher world, it's B2B, so you're selling to, um, labs and neo labs and things like that. But on the long-running agents, it's mostly from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you-

Like a Manus

... at scale.

Yeah.

Yeah, yeah.

Yeah.

Like a Manus level type, type of thing.

Yeah, yeah.

Yeah, yeah.

Yeah. Yeah, B2B2C is basically to me what I've been calling an agent lab. Uh, w- which is kind of like you're not an, a model lab, but you're making a very, very good wrapper that is a platform that other people can sign up, so they don't have to, to code those things. Yeah, I, I-- It sound, it sounds like a much better market than the direct OpenClaw market.

So I've like-- We-- I've done multiple things. So the CodeAnywhere's part of our career path-

Yeah

... R on the calendar, was very much an end user developer product.

Yeah.

And so that is great. It, you can get a lot of developer love, and I feel that we do as a company have a bunch of developer love, but it's a different type, where it's, it's people building these things. Again, it's more akin to a Twilio because you don't really run-- As a person, you wouldn't run Twilio.

I don't know how many people remember it was like ask your developer billboard-

Yes

... um, and whatnot, and people really loved Twilio, but they only used it inside of like, "Oh, I'm building this app or service for thing." And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, I guess, in-

Yeah

... my DNA.

People don't know Infobip is that big.

Yeah, it's hu-- It's like-

Because they're all American. They're like, "Whatever is in Europe doesn't matter to me."

Yeah, yeah.

But like it's the, it's the same size or bigger?

No, no

Same size?

It's, it's, it's about half the size

Half the size?

Yeah, half the size

It's like, yeah

But still huge.

Yeah.

Multiple billions a year. Yes.

Yeah, crazy.

Exactly. These are, like, really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the-- when your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very, you know-

Yeah

... a-around that, and there's very har- it's very hard to scale. Your cap is the number of people that are willing to spin up-- first of all, wanna spin that up and then spin up multiple of these. Whereas if you're in the enterprise, one, like, we know everyone's talking about, like, how many tokens they're spending, I'm spending.

Like, like, m- a lot of companies today are like, "This is our company. Spend as much as you can." Like, basically that is where we're going. And so if you think about that paradigm where you're selling to companies that say, "Spend as much as you can to generate, you know, productivity," versus, "Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever."

Like it is a different, it's a different go-to-market, I think, strategy.

Yeah. There's a lot of discussion. I'm just kinda going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI . It's been very good for you.

Yeah.

I feel like it's maybe a drop in the bucket or maybe it's huge. I, I'm just checking whether it's like these are big trends.

I mean, those things work w-well in our favor to your point-

Yeah

... just because every-

But they kinda drop in the bucket right now.

Yeah, I guess, I think it's like sort of all the things come together and-

Yeah.

So there's so many things that, that impact that. To your point, like OpenCloud wasn't huge for us, but like having the agent SDK, uh, from Anthropic, so or Cloud Code. Cloud Code was very interesting. The reason why it was interesting is that a lot of, let's call them app layer, 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 it. The, the difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very, very well enables the agent to do more things and because that people will invoke a sandbox, they'll run it 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 API source.

Yeah, it's a layer of indirection-

Exactly

... basically. Uh, it's the same thing as agentic search versus RAG which-

Exactly. Exactly, yeah

Just like you just win whenever people put more agents into their workflow .

Exactly.

And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, "Oh yeah, this is another sandbox use case. Oh yeah, that's another one."

Yeah.

Am I missing any big ones?

So the thing, the thing that people, which is the, the computer stuff which I think is probably the most interesting one is, a-and to your point, uh, we've talked to so many people over the last year. It's like, "Oh, like why do you need a sandbox? Why do you need this? Why this?" And to your point it's like, "Oh, I need sandbox for this, I need sandbox for that, I need sandbox-" "And so, oh, I need it for every single thing."

And so basically what I, what I, and it sounds like a broken record, is like you use a laptop every single day, right? And you are n of one, it's just you. But now imagine how m- And by the way, the, the laptop, the computer PC market, the PC market is about equal to the cloud market.

Mm.

And so it's about 150, 180 billion a year-

Okay

... something like that.

Yeah.

It's about roughly the, the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, Alt- It's a little bit less, but it's sort of like that. And now imagine, and that's just like, so how big is the addressable market? Well, how many people are there in the world now? What's the last data?

Let's call it eight billion.

Eight billion.

Yeah.

And so let's say you can have two computer, like you have one personal and one business whatever like-

Yeah

... so it's, it's double that. And so that's 16 billion, right?

Right, right.

How many agents are gonna be running-

Yeah

... in two years, in 10 years, in 100 years? And for every single task they will need one of these. And so how big is that? That market is essentially quote unquote "infinite". You, you will get to the point, and Dylan Patel was at the conference talking about from SemiAnalysis that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint.

You won't be able to grow or we won't be able to have enough of these because there won't be enough CPUs to basically do that.

Yeah. Well, I actually had a really good podcast with Doug Olofson which was his president at SemiAnalysis where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to some memory-

Yeah

... and now to CPUs.

CPU, yeah.

And, uh, I mean it, what's next? Sorry, networking .

Yeah, yeah .

Networking actually has been in shortage for a while if, if you're looking at like just GPU networking. But yeah, I mean it's, it's, uh, it's really crazy the amount of computer use that's going on. Um, yeah, cool. I, I guess other questions are, uh, just the, the one very big part is the open sourceness-

Mm-hmm

... uh, which you didn't have to do, your competitors don't do. I guess a lot of people are worried about keeping their projects open source because some competitor can just slot fork it.

Yeah.

I don't know if there's any reflections on just being an open source company.

Uh, yeah, there's a bunch. So we s- the, the original product that we did was open source.

Yeah.

Doing that was actually very good for us. There's basically a saying of, um, what's the saying? Like companies that are doing really well, um, measure themselves against, you know, free cash flow that are kinda okay, it's EBITDA, then you know it's, it goes all the way down-

The worst is like GitHub stars

... GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars.

Yeah, yeah.

That's what we talked about. We're, we're at the point we're talking about revenue, so we're, we're-

Yeah

... we've gone up the stack on that. And so we started-

No, no, profit, profit.

Yeah. We haven't, we're-

Thank you

... we'll get there . But basically at that point we did stars and GitHub and whatnot, and was useful and the original variation that we did, it w- we split the, the core into its own repo and it was Apache 2.0, so very, uh, permissive. And then we basically would bundle that on the enterprise side with a proprietary repo.

So it was like open core, but it didn't fill out the reposi- the repository was very clean. When we did the pivot, we didn't have time to rethink this, and we wanted to... We had this open source community. It felt a shame not to do that, and so-- but we still did want to add some restrictions, so in the new sandbox product, we did add a AGPL 3, which is, you know, it's a kind of a shortcut way to do that, where you are open source.

And it is true open source in the sense of an enterprise can use it if it, if it wants, but you essentially can't make a competitor without open sourcing-

Yeah

... your stuff much.

It, it's one of, like, three approaches. Like, there's, like, BSL-

Yeah

... and some o- some of the other sort of, uh, elastic license.

Yeah. There, there's some others there. So pure open source believers agree that this is not full open source-

Yeah

... and I totally respect that.

Yeah.

That is absolutely true, but we did leave that. And Daytona, in its essence, everything outside of what's under a feature flag today, which is like the Windows stuff, GPU stuff, and whatever, it is in this open source. It is there, so everything is there, like our own scheduler, everything's there. So we are-- I've had some competitors say, like, "You guys are actually open source, open source.

Like, you, you're real."

Yeah.

"Like, you can actually see that." And I mean, people do like that, and it has helped a bit, but it's actually more helped in the consumption of our cloud product than actually transferring people over. The reason is you can actually, you send the repository to your agent-

Mm

... when you're integrating Daytona, and it just has more context.

Yeah.

It's like, "Oh, okay, this is why this is happening. This is why that's something."

Uh, you could equivalently just have docs that you can... Yeah. So, okay.

I, I agree. I agree, but, uh, to, to, to be fair, and so it actually doesn't really help the growth significantly today.

Yeah.

We've had this con- conversation with, like, investors and other people is like, "How do you convert people-

Dude, the open-

... from the open source?"

... the open source business conversation is so all over the place, right? Okay, on, on... I, I was just like, for listeners who maybe they haven't thought this through, a lot of people say like, "Oh, it's our free tier," right? Like, "Oh, if you run it yourself, but if, when you get serious, call us."

Yeah.

Right? And then other, uh, and then f- uh, me personally, 'cause of my Temporal experience, it actually is the way that it's the, it's GTM into some of the largest companies where we wouldn't pass their review process, maybe 'cause we're too young of a company or, like, there's, like, parts of the stack that we haven't, like, that just doesn't work with them.

But because it's open source, then they, then they adopt it, and then fig- later on we figure it out. That's the low end and the high end.

Yeah.

I, I don't know if it-

No, no, no, absolutely. Um, and that has been historically. The thing that we have found in this AI transition is, and so we haven't talked about this, Daytona's customers are everything from, you know, the single developer, the YC startup, to people say Fortune 500. I'll say Fortune 5, like the biggest companies in the world.

Yeah.

Like-

And, and big Neo labs. You, you told me about-

Yeah. Oh, like the-

... you know, we're gonna keep them anonymous

... e-enormous companies, right?

Yeah.

And because the market pull is so strong, we're able to circumvent these processes. I'm not saying we go, we pass security audits, we pass all these things, but as you know, as you're mentioning, like Temporal way back in the way, uh, day, in our old version of Daytona, like it took us months, and usually at the end they would churn off because just like, "Oh, you're too small of a company," like, "We don't trust you"-

Yeah

... "enough." Whereas today, we've had these large companies push us, like they would push us through. Like, usually when you would go through procurement to become a vendor of large companies, it would take you, like, two, three months. We get it done in five days now. And this is not saying that maybe we're great, but it's more, I think, a sign of the market where it is today.

And so when you think about that, the open source is something that we, from a go-to-market perspective, don't think about that much-

Right

... because everything that we've created right now has been PLG through the cloud product, people signing up and just pulling us inwards.

This is a personal interest, and I don't know if you have an answer, but, um, do you have problems with GitHub?

I do. A little bit. A little bit.

Yeah. Tell me.

Yes.

'Cause, uh, you know, I'm, I'm thinking about like, okay, what would it take to replace GitHub?

So there's a lot of things. I, I, I've thought about this, and I, I've talked, I've tweeted about this, and I looked at some. I've actually invested personally in some.

Is it, uh, Entire?

No, I haven't done that.

No? Okay.

Yeah, so I, and I've, I've met Thomas or virtually-

Yeah

... um, and we've talked. So I really think that... And th-this was my reason for that. Because we have a bunch of background long-running agents, and for our time, most of them are coding agents. Like, everyone was bui-building up a competitor to Lovable or, or, um, Cargo or Devon or whatnot. What we saw from our customers was that they were all trying to figure out how to do, uh, versioning.

Everyone is doing it in different ways. There were some, some really weird ways where people were doing that, and the reason was that GitHub as is was an overhead. Like, it wasn't fast enough what they needed. It didn't solve the problem that they needed. And to be fair, like GitHub is for post your, the inner loop, right?

Yeah.

It is, it's post your laptop, right?

Yeah. GitHub is the, the point at which the outer loop starts.

Exactly.

Yeah.

People started using that for sandboxes, which is inner loop, which is usually, you know, it's, it's on your laptop, right? And so that is not what it's made for, and then we had everything from people... Actually, the, the most interesting one is we had one customer that would literally take the entire code base inside the sandbox and every-- I forgot what the time sequence was.

They would just dump it all into a JSON and then push that to S3.

Yeah.

And that's it.

You make your own Git.

And it's, it... But it's not, there's not even diffs. It's just a whole- ... thing every single time. It's just every... Because it was super fast. And then they would go back and search and find, you know, sort of what the file was and writing it and, and whatnot. Because there's text file, there's JSON. Like, they're very small, so the, the network cost is very low and they didn't care, and they just did it that way.

And I'm like, if people are doing this, that means there needs to be a new solution-

Yeah

... to this problem, right? And so for me, it's quite interesting to look at who, who is building these types of new things. Agent first, I think Git- As is, still exists in the future, maybe even GitHub exists, but there will be a whole new sort of-

Yeah, exactly. Git is like the deploy artifact to kick off CI/CD.

Yeah.

But then there's a layer before that, that is like the agent collaboration layer.

Yeah. And so I think something needs to be said there, but on the other side, like there's issues with-- Another interesting thing is just like CI right now. So the amount of PRs being created is insane right now, right?

Mm-hmm.

In general.

Even for you guys, right?

Everyone's creating a bunch of PR.

Yeah.

Like everyone. And then all that has to go through CI, and then that's the bottleneck. Like, everyone is bottleneck. Like, not just acti- like, not just actions, but like go to any CI provider, you will not be able to... If you have a high throughput of, of PRs, there's one company we're talking to, they do a thousand PRs a day.

Mm.

Which means like, and they're just waiting. They have just a queue on that, right?

What do they use, like, uh, Buildkite or-

I don't know what they-

... Circle? You know, technically your tech can be used for CI.

That's, that, that was the conversation.

Oh, okay.

That was the conversation.

Is that a serious conversation?

So we'll, we'll see how that goes. We've had quite a few conversations around that. We're, we are not a CI provider by any means, right?

But what, what is... I mean, what's missing?

Essentially, you could use a Da- a San- Daytona sandbox ins- instead of whatever you use for, you know, your GitHub runners, essentially.

Yeah.

Yeah, yeah.

The only thing I would say is like maybe CI machines are supposed to be very cheap, and maybe it's like the low end because it's supposed to be like, you know, non-blocking or something like a, like a background job. Like it's, the, the urgency is not that important for CI.

Performance is, though.

Yeah.

Performance is, yeah.

Yeah. Okay, that is interesting. Um, before we leave Daytona and, and go into like sort of broader like founder takes and what, what have you, when startups evaluate you, like, so you have, you have all these like names and you, you have more that you can't, you can't even name. They see all your wall of competitors-

Yeah

... and yeah, you have differentiation versus, uh, many of these, but like what sells them?

The thing that we found that sells people the most, this is more maybe a day two thing instead of a day one thing.

Sure.

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 a third party, so we don't do the case studies, and it's actually interesting to watch those cases. I watched them, they're 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.

In terms of your team?

In terms of the team, yeah.

Okay.

Insane responsiveness has been by far the... Now, we can talk about like features a-and breadth of product and concurrency and CPUs and like all those things, but I feel that that would probably-- So if all other things are equal, that is very much a differentiator I found.

Yeah.

And I did not-

And is that entirely Slack or Slack plus email?

There's email there as well. There's calls, but o- the vast majority is like on site. So it's Slack. Like we have had customers like, "Hey, we have a problem. Can you get on Huddle?" Like, we will get on that Huddle like in five minutes, literally. I've done this multiple times, so yeah.

Wait, okay, so how big are you? Uh-

Twenty-five today.

How, how do you do this kind of support like this?

We're j- we're insane. We don't sleep. Zero, zero, seven. Have you heard the new thing?

Zero, zero, seven. I mean, like I've met your team.

Yeah.

They're very impressive. They're very dedicated. But like also, how do you get a team to do that? You know, it's-

So there's, um-

I have Slack exhaustion, you know?

Yeah, we all have Slack exhaustion. We're very, very tired.

Yeah.

Uh, the thing that is unique, I don't know unique about us, but unique, I would say unique about any successful, um, serial founder is that you're able to pull in people that you've worked with before, and so you can't do that as a first-time founder. Like, I couldn't have done that or whatnot. But of the twenty-five people in Daytona, I think about thirteen of them, we have worked with seven years plus.

Yeah.

So it's like high trust, high throughput, high, "We know what we're signing off to do." And especially these people worked with us when we were starting and we were actually hustling, you know, hungry for food hustling type level. And so those are the people that work with us. The, now the, the new segment that has come is almost everyone is sort of, you know, one der- one degree of separation.

So it's like someone that someone has known, and so they sort of come into this org. And we've had people that have, like, not fit into org as well. It's just like it's that type of culture where there is a high expectation of, like, being online, replying for these things, and I do that first. You will-- If you ask any engineer, they're like, "You never sleep," like about me.

Yeah.

And so then I do that as an examp-- I don't do it as an example. That's just how I'm wired. My wife doesn't appreciate that, I can tell you. My wife doesn't appreciate that. I told her about nine, nine, six. She said, "I wish."

It's like-

Yeah

... these, these Chinese people are slacking.

Yeah. So like that is something there. And so I, I think every company has their own culture, and that's something very, very deep, um, ours. And it's something that's come up again and again, and every single day we're reminded about that. And I didn't go out thinking that that is how I'm gonna build it. It's just how I built these things right now.

Yeah, yeah. I, I'll transition a little bit on the founder side. Like, I'm very impressed by you in general of, like, your sort of balance. You have a young family.

Two kids, yeah.

No, two kids now.

Yeah, two kids now.

Um, I think a lot of people I meet, they're like, "Well, I'm starting a family. I can't be a founder," and all that. Um, what's your advice to those people?

Every single day-- So my family, they're here right now, but they're usually-- I fly between Croatia and, and here. Like, a lot of our team is in Croatia. A part of our team, and are growing, is here now in San Francisco. And so I spend a lot of time away from my family, and that is hard. Like, that is a sacrifice that you have to.

But going in, like, people say, like, on your deathbed, you're gonna miss some of those things. The thing that, and probably might be true, but the thing that going into this, I already said, like, "I know that this is gonna hurt," and everything has to hurt. By the way, I'm very much of a feeling that everything has to hurt.

Going to the gym hurts. Losing weight hurts. Like, everything has to hurt, right? It does. Like-

No pain, no gain.

It is literally, but you actually have to enjoy the pain and just, like, if you don't enjoy the pain, it's not for you. And so you get accustomed to that pain. And so love the kids, especially I have a daughter and a son. Daughter is the o- eldest, like, love her and do miss her when she's not here, but it's like, that's what I signed up for, and there is a- Plan and target of what I'm trying to achieve.

And now hopefully with my wife, which does support me, we can get ourselves together more, so it, it doesn't there. But she takes a large part portion of that, and so if you have a partner on the other side that is okay with that, then you can do that. But even if they do, you have to be okay with not being there, right?

Yeah. This is my, uh, my vision for you, uh, th- this, this meme.

Yeah, yeah, yeah.

So that's your kids in the future.

Yeah, I think so.

It's like he's like-

But we have to teach them that they're not rich

... because, because Dad, you know, built the compute sandboxes.

Here she comes, sandboxes. Dad made sandboxes. Dad made sandboxes.

Uh, and built the spiritual successor to serverless and Kubernetes-

Yeah

... and for agents. Any other sort of, uh, hot topics, trends? You, you have a lot of hot takes. Uh, actually, you are best known for ... You, you were, you were, you were sort of in sort of hustle culture mode, right? And someone q-mo- quoted you and said, "I haven't even heard of you, bro."

Yeah.

"Just log off and take the-

Yeah

... take the Christmas off." And then your response was?

Oh, my response was like, "That's why I can't."

Yeah.

Yeah.

So I mean, like I, I think that's like very typical of you. I, I don't have it here. I can't, I can't bring it up.

Yeah.

But I think that's, that's very typical of the, the, the culture, but like I, I think you have a lot of like interesting hot takes like that. Any, any other sort of takes on the startup ecosystem?

Oh, the startup ecosystem. And this was the, the recent one, which is I think that, and this is general like business, I, I feel that the... It didn't come off, I think well on Twitter. Some people mis- misread it, which is the market is adding premium to SaaS vendors that are reselling tokens.

Yes.

And I think that's incorrect.

Why?

Why I think that's incorrect is that if you look at, one, your pricing depends on what the price is, if it's a public market or if it's private or whatever. You're saying, the person that's reading that, that the re-acceleration of revenue is equal to the old revenue, which it's not, not even close. Because one, you had on SaaS, you had typical SaaS margins, whatever it was, right?

Yeah, yeah.

Stickiness and all these things. Now what you're doing is you are saying, "Here is my agent, and I have whatever the margin is." It's way worse, right?

Yeah.

And now you're using Anthropic or, you know, or OpenAI or whatever through me, the, the SaaS model, and then we as a community are saying now that is re-acceleration. And so one, I think that's wrong because it, the, first it's not the same. The mar- the makeup is not the same. The other thing is, and go back to like what, what I mentioned earlier is like the, the Kua and how I set up OpenCloud and whatever, I don't want your agent essentially.

Because what happens, right now we have a problem that, and this has historically been, you have data siloed in, again, ClickHouse, QuickBooks, it's all siloed, and now you're giving me an agent that'll give me the data, but it's still siloed, right? And so now I have to like take that data and then-

Yeah

... get another agent.

Just expose the data to my agent.

Just, just expose the data.

Yes.

Just expose it. And one thing I have to s- and so I'm like, "Just expose everything and charge me for that." So charge me for consumption of API. So you'll have your old seat-based pricing for humans.

Yeah, yeah.

Charge me for this. The number of agents will skyrocket, and essentially you'll have more usage and charge for more if your product has value. So like there's arguments some of them do have value. It's a database, not database. We can get into that. But some of them really do, and I was actually shocked that the first person to do this was Benioff.

Mm. Salesforce, yeah.

Salesforce.

Agentforce?

There was a tweet I think three days ago which he said, "Every product in Salesforce has been exposed via an API."

Wow.

Everything. And I'm like, "Now I understand why this person has built this."

This guy's king.

This i- insane. Kudos to him. Amazing. It's like, thank you. I don't know if he listened to me or someone else, but like thank you for solving. This is the direction of the world, and so if you can get real acceleration against that, against consumption of API, that is actual revenue and that is actual real acceleration, and that is where value will come from.

And I think that there will be a cold shower when people understand like no one's actually gonna use and pay for these agents and tokens, and that wasn't actually really re-acceleration, but it'll drop back down.

Yeah, yeah. Yeah. Uh, I mean, it looked like obviously, I think generally correct, and I agree. I think but people are going to try to become an AI company.

No, no, absolutely. And I have nothing against that. And I, this is no, uh, uh, to be very clear, this is not a downer on anyone that's building this thing. Everyone has to get to like, get to the revenues, get to the multiples, get the valuations, do what you have to get to the next step. The- absolutely agree.

But we as a community are now like saying, "Oh, this is like the magical way to get out." This is not. Like that, that is not what is happening, right?

No, I think, uh, there was like this kitchen appliance company that put out some AI nonsense recently.

But it was also the sneakers. What was it called? Allbirds?

Allbirds.

Yeah.

No, Allbirds is pivoting to GPU. Th- th- that's, that's fine. It's like, you know, I have, I can, I have some money left, so I'm just gonna, uh, do some lottery tickets. Uh, would you go into offering GPUs?

Oh, yeah, we will.

Yeah.

But not for inference. Like essentially what we think about is like the GPU sandbox.

Mm.

So if you think of like if you have a GPU in your computer, that is what you have a GPU in the sandbox. So there are workloads that do need GPUs. Again, I always go back to 3D rendering because it's the easiest one to comprehend. But like if you want to do any type of RL on like CAD or, or something like that, you will need a GPU in the sandbox, and so that's coming now as well, yeah.

How about own data centers?

Own data centers, so we run on co-location providers, bare metal machines. Data centers, we technically can run on that or, or our own data center. Like that's how we architected it. Today from a gross profit margin perspective, doesn't make sense for us to get in that. You have to raise a large amount of capital, a large amount of risk for like single digit percentage points.

So today that doesn't make sense, but we are fundamentally architected so that we can do that if we want to.

Yeah. I mean, you're a large customer of these guys now.

Yeah.

Do you see any opportunity?

We will see.

Yeah.

We will see. Yeah.

Yeah. I see a lot of people like trying to do the bare metal thing. Uh, we talked to Railway, uh, the other day.

Yep.

And they're also doing a, a very similar, uh, strategy.

They think, I think they're building out something or they have their own sort of data centers now.

Yeah, they have majority their own data centers. Um- I, but I, I do think, like, I mean, they still use Equinix and, and all those things.

Yeah.

So I, I think it's just interesting that, you know, this model basically hasn't changed. It's basically a real estate model. They, they manage the facilities-

Yeah

... and then you do everything else. I wonder how it can be changed for the, for the future 'cause, I mean, you know, like, the, the AI wave is the opportunity to reinvent everything. Yeah. Any- anything else? Cool. I, I think that's about it. I didn't have any other topics. I, I, I think this is, like, a...

as best and comprehensive, like, if you have, like, any questions about the compute market, uh, and the 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 growing 75% month to month. Like, where are we, where are we gonna be by end of year?

It's an insane number. I'm sort of scared to say it out loud. So, like, it is, it's very big. Um, just the, the sandbox market on, on, like... And we, there, we talked about this in general. The entire infrastructure market is growing 40% plus or minus month over month. Everyone is growing 40% month over month, and that's also a hot take is, like, if you're not growing 40%-ish, it's not that-- It's just, just the market.

You might as well... You don't have to come to work. It'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 from a CPU perspective, it 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 now, right?

Yeah. It's, this is like, it's whatever NVIDIA decides to bless that day.

Yeah. It's that how much, that's how much they're gonna grow, right? And so where, the, the CPU market in general, be it like something like Railway, um, for example, or Vercel or whatnot, or Deployment, or it's like the sandboxes, they're still CPUs, so, like, each is, is growing at the pace of the market, of their, the market and what their, you know, plus or minus of that market.

But it's still not constrained by that. And so my thought is like for ev- for all of us in this market, and databases fall into that as well 'cause database is also run on CPUs, and it's like we all have to grow as fast as we can so we can get enough of, you know, CPUs tomorrow from-

Yeah

... Intel or from NVIDIA 'cause they have now CPUs and everyone else later on.

Yeah.

So it'll be interesting when we get to that.

Maybe one version I, I'll phrase this is like, are you, you know, is, is the potential new Heroku, new AWS, or new, what's, new Stripe? Like, what's the, what's the analogy that-

Yeah

... is most appropriate?

It, there's interesting. There's, like, analogies of, like, so the, you know, there's new Cloudflare, but new Cloudflare is new Cloudflare.

New Cloudflare.

Like, they're actually doing a really good job about, like-

And Cloudflare owns networking. No one can fight.

Yeah, yeah.

Like, it's like, come on.

They're doing, no, they're doing really well. No, what I said is in the sense of their whole agent portfolio-

Yeah

... is actually really good. And I should say there are some technical limitate, I think personally-

Uh

... around, like, everything's under constrained under Workers. Like, Workers is their thing. But from a go-to-market vision perspective, I think they're actually really, really good. I think they actually get it unlike some other companies. And to your question is, like, what is gonna be... There will be an equivalent. Everyone says, like, an AWS for AI agents, but your answer, like, it might look more like Stripe than AWS in that sense.

So there will be a cloud built out-

Hmm

... specifically for agents. And so that cloud will have sandboxes, and it will have web search, and it'll have databases like SQLite or Neon or, or whatever specifically for agent and other things. We are not at the end of the new infrastructure primitives for agents. There are more coming.

Yeah, yeah.

So people think like, "Oh, there's nothing else. This it." There are more. Like, we have some ideas about the next ones. We don't have time to do them. But there are definitely more primitives that are being built out for AI agents, and there will be, I think, a cloud that runs all that.

Yeah. OpenAI has said AI cloud. Vercel has said AI cloud. Uh, and you are potentially also one of the other-

Yeah

... uh, the prospective AI clouds. I think it's a very big prize-

Yeah

... to win. Uh, well, thanks for coming on.

Thank you for having me. It's been amazing.