Giving Agents Computers — Ivan Burazin, Daytona

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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 2022

Long 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

Timestamps

00:00:00 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

Transcript

Introduction: Daytona, CodeAnywhere, and the End of Localhost

Swyx [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 Daytona

Ivan [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 Agents

Ivan [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

[truncated for AI cost control]