The explosion of AI agents capable of writing code, running experiments, and executing complex workflows has exposed a fundamental gap in how cloud infrastructure was built: today’s cloud was designed for stateless production workloads, not for the dynamic, exploratory computing that agents actually require. Existing cloud primitives optimize for serving software at scale, but agents need something different – environments that launch in milliseconds, branch into parallel execution paths, persist state across failures, and scale to millions of concurrent instances simultaneously. Daytona addresses this mismatch directly by providing programmatic, composable sandboxes that function as full computer agents can start, pause, fork, snapshot, and destroy on demand, giving each agent its own dedicated execution environment rather than squeezing agentic workloads into infrastructure designed for a different era. Daytona’s sandboxes support the full range of capabilities agents need to do real work: process execution, file system operations, native Git integration, built-in language server support, and Computer Use sandboxes for desktop automation across Linux, macOS, and Windows. The company reached a $1M forward revenue run rate in under three months and doubled it six weeks later, with customers spanning early-stage Y Combinator companies and Fortune 100 enterprises across use cases in code execution, computer use, and reinforcement learning.
AlleyWatch sat down with Daytona CEO and Cofounder Ivan Burazin to learn more about the business, its future plans, recent $24M round that brings total funding to $31M, and much, much more…
Who were your investors and how much did you raise?
We raised a $24M Series A led by FirstMark Capital, with participation from Pace Capital, Upfront Ventures, and E2VC. The round also included strategic investments from Datadog and Figma Ventures.
Tell us about the product or service that Daytona offers.
Daytona builds infrastructure for AI agents. We provide programmatic, stateful sandboxes that agents can start, pause, fork, snapshot, resume, and destroy on demand. While traditional cloud infrastructure was built for stateless production workloads, AI agents need long-lived environments that behave more like computers than containers. Daytona gives every agent its own composable computer.
What inspired the start of Daytona?
We saw a clear mismatch between how AI agents operate and how cloud infrastructure was designed. Agents write code, run experiments, recover from errors, and branch execution paths. Existing primitives were built for serving software, not creating it. Daytona was started to build infrastructure purpose-built for agentic workloads.
How is Daytona different?
Most infrastructure today is optimized for stateless services. Daytona focuses on stateful, programmable environments that can be manipulated in real time. Agents can fork execution paths, snapshot environments mid-run, resume from failure, and run at massive parallel scale. We are not optimizing containers; we are building a new primitive: composable computers for agents.
What market does Daytona target and how big is it?
We operate at the intersection of cloud infrastructure and AI. As agents move from prototypes into production, every agent will require its own execution environment. We believe the market opportunity spans the entire cloud infrastructure stack as it shifts to support agent-native workloads. Given the size of global cloud spend and the rapid growth of AI, this represents a multi-billion-dollar market opportunity.
What’s your business model?
We operate as a usage-based infrastructure platform. Customers pay for the compute, storage, and orchestration resources consumed by their agents running on Daytona.
How are you preparing for a potential economic slowdown?
We’ve built the company with capital efficiency in mind from day one. Our focus is on strong unit economics, disciplined hiring, and delivering clear ROI to customers. When companies evaluate infrastructure spend, solutions that directly improve reliability and reduce operational complexity tend to remain mission-critical.
What was the funding process like?
The funding process was rigorous but ultimately rewarding. We focused on building a compelling narrative around our product’s value and potential. Engaging with investors who share our vision was crucial. This involved multiple rounds of meetings, presentations, and due diligence. The support from existing investors and the momentum from our open-source launch played a significant role in attracting new interest and closing the seed round successfully.
What are the biggest challenges that you faced while raising capital?
One challenge was articulating why agents require a fundamentally different infrastructure primitive. This is not an incremental improvement to existing cloud services, it’s a structural shift. Helping investors fully grasp the scale of that shift required clarity and precision.
One challenge was articulating why agents require a fundamentally different infrastructure primitive. This is not an incremental improvement to existing cloud services, it’s a structural shift. Helping investors fully grasp the scale of that shift required clarity and precision.
What factors about your business led your investors to write the check?
Strong early adoption, rapid growth, and clear market pull were key. Customers are already running large volumes of agent workloads on Daytona. The combination of technical depth, real usage, and a clear long-term infrastructure vision gave investors conviction.
What are the milestones you plan to achieve in the next six months?
We plan to expand our enterprise footprint, deepen integrations across the agent ecosystem, continue scaling reliability and performance, and ship additional capabilities that make agent orchestration more programmable and composable.
What advice can you offer companies in New York that do not have a fresh injection of capital in the bank?
Focus on building something customers truly need. Revenue and strong customer pull create optionality. In tighter markets, capital flows toward companies with clear traction, disciplined execution, and products that solve real pain points.
Where do you see the company going now over the near term?
In the near term, we’re focused on becoming the default execution layer for AI agents. As agents take on more responsibility across industries, we want Daytona to power the infrastructure behind that shift.
What’s your favorite winter destination in and around the city?
A winter walk through Central Park after fresh snowfall is hard to beat. It’s one of those uniquely New York moments that never gets old.
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