Daytona raises $24M Series A to provide secure sandbox infrastructure for AI agents

[applause] understanding. Our next guest is in the reream waiting room. We have Ivan from Daytona.

Great name.

Great name. We love Daytona.

All right, we got to we got to talk about this cuz you had one of my favorite interactions on X. This was like a month ago. you remember you were you said something like I'm working late on a Sunday and somebody commented like that why are you even doing that nobody knows who you are and Ivan was like that's exactly why [laughter]

that's so good

amazing

that's a great response that's a great response

yeah so it it was basically um the reason was like uh it was just before the holidays and everyone was talking about oh no one no one took a break on the during the summer so we're all taking breaks on you winter like

founders in investors or whatever and I'm thinking like are we serious? This is like a once in a generation opportunity uh and like if you go rest like you won't probably make it, right? And I just tweeted that and then to your point someone was like I've never heard of you like exactly that's why we have to work. So yeah.

Well hopefully people hear about you now. Why don't you introduce yourself and the company?

Sure. Uh so I'm one of the co-founders and CEO of Daytona. Daytona is on a mission basically to give every single agent a computer or a sandbox as it's popularly called, right? And so the the team has been working together, the founders for almost 20 years. So we're quite old and we've always been in for people. Everything from screwing together servers to creating orchestrators and whatever.

Interesting orchestrators. Tell me more about where you're seeing the orchestration market going. I'm a big fan of Gas Town and the metaphors and it feels like it could be like the hot new trend, but I don't know if it's uh too early or or where we'll see all that go.

I mean trends keep changing as you've seen like every you know two months things change but one thing is for sure and this is what I mean I love what GSON is doing and all these others which is basically the ability to spin up multiple of these agents. Each of these agents needs a computer. Um, and I don't think we stop here with like two or four or 10 or 50 like this will go exponential. Um, and so that's why we're doing what we're doing. So we really love um what Steve is doing at Gastown and all these other people that are creating this.

Talk about talk more about where the product is, uh, the evolution, where the team is, kind of the the current state of the company and where you want to go.

Sure. So the company's almost 3 years old, but we actually like hard pivoted and like fired all our customers and everything and we decided so yeah that was a hard one and we saw what was coming. We weren't actually a first but we did see that agents would need these computers and so we actually rebuilt the product from zero launched it 8 months ago. Um and since then we've got the number of customers has been insane. The growth has been insane. We have everything from YC companies, growth companies, Fortune 100 companies, everyone building agents either internally or as you know as products uh are using us or someone like us. So yeah,

talk about why an agent needs a computer. Um, you know, there's been this debate over open claw, formerly Claude book, um, around like, oh, should you host it virtually or should you run it on a MacBook Mini? And if you do run it locally, uh, that's a security risk. But then you talk to the people and they're like, okay, yeah, you're running in the cloud, but you still gave it access to your email. And so like you kind of let the fox in the hen house. What does it mean to actually provision a computer versus just APIs to other compute resources?

Yeah, exactly. So, I think that's the the most interesting part where originally agents were like these use like rag based systems. They could connect to APIs and they can usually like consume information, analyze it, give you some feedback and fire up some tasks. But if you actually think about that, if that is a way a digital knowledge worker would work, the two of you wouldn't have laptops in front of you, right? like there's it's it's not that ideal. It's you know it's messy. You have to download things. You have to install things. You have to analyze things. And if we think of AI agents as digital knowledge workers, it actually makes like super sense that they do need these computers. The open claw um or whatever it's called right now. Um and cloud code and whatnot. I can't there's like three names.

Open.

No deal.

Open claw. Open claw. Yeah. So this just shows and everyone buying Mac minis. I think this just made people understand why it needs computers. But if you think of if you look at our use cases and what people um use to build for we basically have three which is code and command execution computer and browser use and RL and so the first one uh like the canonical these are not our customers I'll just say the highest brands just so people know it's like if you think of like lovable and you think of lovable you as a human type with lovable and then it spins up a sandbox to write the code preview the code and run the code and so lovable has how many users and so it needs like hundreds of thousands of these sandboxes to be run so their agents can get there, right? Uh the second one is like if you need your agent to, you know, do QA testing of Booking.com's website, like it needs a computer to be able to run the browser to be able to do these things. Um and the most the newest sort of use case for us is just like the RL environments and I think Dylan Patel was a guest yesterday on your show and also I'm going to also we're doing a conference next week and so he's one of our guests.

He's going.

Yeah, he's going. So at the Chase Center, we rented out the Chase Center to do the conference called compute. That's amazing. Um so you're very you're very invited if you guys want to join.

And so he has this article about um RL environments and how most of the models have now over the last 18 months become better because of post training and spinning up these RL environments. And these companies to spin up these RL environments, they need not only fast computers or sandboxes, they need them they need them in concurrency. So they need a 100,000 or 200,000 to be spun up in an order of like 3 minutes, right? This is not a trivial thing and not something that you can really take off the shelf. And so when you think about why agents need computers, like when you really think about the market of AI agents and softwares, that's sort of where that fits in.

I want Dylan Patel doing the Lily Yachty walk out that he's uh he's made fake [laughter] AI versions of it so many times and now he's at the Chase Center,500 1500 builders there. You got to get the [clears throat] little yachty playing so he can do the walk out. I saw first mark in the chat. Give us the news. What happened?

Break down.

Yes, we raised a $24 million series A.

Boom.

Congratulations.

Uh,

thank you so much. Thank you so much.

Yeah, very impressive progress and uh I'm sure you'll be back on very soon

and uh have a great time at the at the conference pulling together 1500 people. Not

easy. We're excited.

Thanks so much.

We'll talk so much. Great to meet you, Evan.

Have a good rest of your day.

Goodbye.

And without further ado, we've been keeping our next guest waiting. He's live in the TBP Ultra Dome. It's Scott. You

tell him he's suited up.

Thank you so much for coming.