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On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.
“The end of localhost” has been Ivan Burazin’s obsession for more than a decade.
Something that is all too familiar…
Infobip Shift 2022Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.
The thesis was directionally right, but the market wasn’t ready yet.

However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.

Daytona isn’t just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan’s original localhost thesis.
In this episode, Daytona’s CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.
We go deep on the new agent compute market: Daytona’s hard pivot from human dev environments to AI sandboxes, the New Year’s Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.
We discuss:
How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis
Why Daytona pivoted from human dev environments to AI sandboxes
Why agents need composable computers instead of disposable code execution boxes
The New Year’s Eve MVP that customers chased API keys for
Why Daytona chose bare metal, stateful snapshots, and its own scheduler
How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds
Why Daytona’s biggest customer runs ~850,000 sandboxes a day
How RL/eval workloads create zero-to-100,000 CPU spikes
Why RL workloads went from 0% to roughly 50% of Daytona usage
Why customers compare Daytona against EKS/GKS and say they’re “never going back”
Why every AI agent may need a computer, including Windows and macOS environments
The Apple licensing constraints that make macOS sandboxes hard
Why CLI gives agents more power than MCP
How open source helps agents integrate Daytona
Why agent-generated PRs may break today’s CI/CD assumptions
Why AI SaaS companies reselling tokens may face a cold shower
Why the AI cloud may look more like Stripe than AWS
Ivan Burazin
LinkedIn: https://www.linkedin.com/in/ivanburazin
X: https://x.com/ivanburazin
Daytona
Website: https://www.daytona.io
X: https://x.com/daytonaio
Timestamps00:00:00 Hook
00:01:12 Introduction
00:03:15 CodeAnywhere, Shift, and the end of localhost
00:05:58 What Daytona is: composable computers for AI agents
00:08:07 The pivot from dev environments to AI sandboxes
00:10:17 The New Year’s Eve MVP and customers begging for API keys
00:12:56 Bare metal, stateful sandboxes, and Daytona’s scheduler
00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs
00:21:53 Spiky RL/eval workloads and the new agent infra problem
00:28:12 RL workloads, Kubernetes pain, and dynamic resizing
00:33:31 Why every AI agent needs a computer
00:38:48 macOS sandboxes and Apple’s licensing problem
00:44:28 Why CLI may matter more than MCP
00:48:11 Open source, GitHub stars, and agent integration
00:53:11 Git, CI/CD, and agent collaboration bottlenecks
00:58:15 Founder life and building a 25-person infra company
01:02:44 AI SaaS, token resale, and API-first business models
01:06:10 GPU sandboxes, data centers, and compute growth
01:09:48 Why the AI cloud may look more like Stripe than AWS
01:11:26 Closing thoughts
TranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we’re in the studio with Ivan Burazin, CEO of Daytona. Welcome.
Ivan [00:00:07]: Thanks for having me, man.
Swyx [00:00:08]: Ivan, you and I go back.
Ivan [00:00:10]: Way back.
Swyx [00:00:11]: How I don’t even know how, you found, did you reach out or, for Shift.
Ivan [00:00:17]: 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, localhost should die. And you had this article.
Swyx [00:00:29]: End of localhost.
Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, 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.
Swyx [00:00:51]: I don’t remember.
Ivan [00:00:52]: I remember because I was with my then I’m 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, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.
Swyx [00:01:10]: The reason I’m nice is because I’m also late to other people, so it’s like, who’s, who’s without sin here, yeah, so I have to, for those who don’t know, InfoBip Shift, there’s this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”
Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should’ve took the advisory shares. So I’m sorry, dude. But anyway.
Swyx [00:01:43]: We’re not, we’re not venture backed.
Ivan [00:01:44]: No, it doesn’t matter.
Swyx [00:01:45]: It’s Yeah, anyway, so I think what’s impressive about you is that CodeAnywhere is the thing that you’ve been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.
From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I’ve said this multiple times, it’s like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It’s not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, 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, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.
Swyx [00:02:55]: There was Cloud9.
Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out 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 at the 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, 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 3 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, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.
Swyx [00:04:01]: Historic pivot, yeah, and, it’s one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I’m like, “Fuck.”
Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn’t have done it.
Swyx [00:04:18]: No way.
Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.
Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don’t invest.”
Ivan [00:04:29]: That’s because it was your quote. It’s like we.
Swyx [00:04:30]: Yeah. It’s the end of localhost.
Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.
Swyx [00:04:34]: No, that’s like, 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, talk it, talk through it with me.
Ivan [00:04:47]: It’s finally happening though.
Swyx [00:04:48]: It was really super interesting.
Ivan [00:04:48]: It’s finally happening.
Swyx [00:04:49]: It’s finally happening.
Ivan [00:04:49]: Yeah, it’s finally.
Swyx [00:04:49]: It’s finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let’s get like a quick description. I’m wearing the shirt.
What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You’re wearing the shirt. Yes,.
Swyx [00:04:59]: It says, I think your branding is very good. Like, it’s very consistent. It runs AI code. Like, it cannot be simpler.
Ivan [00:05:05]: Exactly, but we’re gonna probably have to change that.
Swyx [00:05:07]: Oh, shit.
Ivan [00:05:07]: It’s also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we’ve given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn’t really market about us.
Swyx [00:05:21]: Yeah, Daytona’s on the back.
Ivan [00:05:22]: It markets the back. It markets to 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 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 which can be misleading.
Swyx [00:05:44]: All these things. All these things on.
Ivan [00:05:45]: Yeah, exactly, ‘cause 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, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different 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.
Swyx [00:06:19]: Yeah, to give people - I’m trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it’s just been growing for a while. Like, it’s been going like this. And every single - It’s not just you guys. It’s every single.
Ivan [00:06:41]: Everyone, yeah.
Swyx [00:06:42]: Sort of, compute provider. I don’t know if you agree with me saying compute provider or not.
Ivan [00:06:48]: It’s fine.
Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, 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 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 Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is 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, Devin was not public. You actually gave me access to Devin at that time. So Devin.
Swyx [00:07:25]: I did?
Ivan [00:07:26]: Yeah, you gave me access.
Swyx [00:07:26]: I don’t think I was supposed.
Ivan [00:07:27]: Yeah, exactly.
Swyx [00:07:28]: Yeah, I.
Ivan [00:07:28]: So it doesn’t matter. You.
Swyx [00:07:29]: Yeah. I gave like three friends access.
Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn’t matter. but OpenDevin was available, which is now called OpenHands. 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, OpenDevin 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 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 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, 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 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.
Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.
Ivan [00:08:49]: Generally we -, I looked at There’s a few of podcast, different segments and different types. So there’s you guys, No Priors, Bill Gurley’s was great while.
Swyx [00:09:04]: VG2, yeah.
Ivan [00:09:05]: Yeah, while it was around. So there’s a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.
Swyx [00:09:14]: We’re not really about the compute market.
Ivan [00:09:15]: It was also already - Sorry?
Swyx [00:09:16]: You’re, you want - You’re looking at the agent infra market.
Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what 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 MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year’s, and go back to just, 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.
Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.
Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. 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, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed 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 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 seen, 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 want 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 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.
Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn’t composable at the time?
Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but y