Daytona

25 investors. Amazing partners (shoutout Mercury and Sydecar). Incredible food.

In other words: it’s fair to say Unicorner hosted a great SXSW omakase last night.

Among other conversations: we think we’re certified tokenheads, and so are many other VCs at this point.

We’re in Austin until Thursday night. Want to grab a coffee while we’re here? Reply to this email, and we’ll make it happen. 🤝 

Arek and Ethan 🦄

Daytona is an open-source platform for running AI-generated code inside isolated sandbox environments, secure mini-computers that let AI agents execute code without affecting other systems. It treats each sandbox as a composable computer, meaning an agent can create, configure, save, copy, and resume an environment through an API as it completes a multi-step task. It is built for AI agent workflows that need access to information and long-running execution within a persistent environment. The platform supports snapshotting, recovery, and forking, so teams can save and restore progress or duplicate environments for experimentation. This helps reduce wasted compute when agent tasks require multiple attempts, and it allows teams to run many sandboxes concurrently for large-scale workloads.

Check it out: daytona.io

Daytona uses usage-based pricing. It includes $200 in free compute and 5 GB of free storage, then bills per second for compute and memory, with metered storage beyond the free tier. It also offers enterprise options for larger deployments, including on-premises setups and a startup program with additional credits.

• Raised $24 million in Series A funding in February 2026, led by FirstMark Capital

• Raised $5 million in seed funding in June 2024, led by Upfront Ventures, and $2 million in pre-seed funding in November 2023

• After rebuilding and relaunching for AI agents in late April 2025, the platform crossed $1 million in ARR within two months, demonstrating a rare pace of adoption among infrastructure products

• Case study metrics include 4,000 sandboxes created per month, more than 660 hours of sandbox runtime per month, 10 to 30-minute sessions supported, and under 100 milliseconds of sandbox creation time, according to LangChain, an open-source framework for building LLM agents

• More than 65,200 GitHub stars on the main open-source repo as of March 15, 2026

Open roles:

• Developer Experience Engineer

• Senior Support Engineer

• Senior System Engineer

• Senior Platform Engineer

• Senior AI Engineer

• Partnership Manager

• Digital Community Manager

• Dev Community Specialist

• Product Lead

• Designer Engineer

• Product Designer

• Other open-source bounty opportunities

Daytona is also offering a startup program offering $10,000 upfront and up to $50,000 in Daytona credits.

Daytona was founded by CEO Ivan Burazin, CTO Vedran Jukic, and Chief Architect Goran Draganić. Burazin has worked in developer tools since 2009. His background includes co-founding Codeanywhere, a cloud-based development platform that provides browser-based integrated development environments, and building Shift, a developer conference founded in Croatia that grew into one of the largest developer events in Southeastern Europe and was later acquired by Infobip. 

Daytona started in 2023 as an enterprise development environment for human engineers, aimed at companies that wanted a secure, self-hosted environment for management. Inbound demand from teams building agents exposed a gap where an enterprise dev environment could look like a fit, then fail in production, because agents need fully programmatic control, persistent state, and the ability to recover, fork, and continue autonomously. With AI agents hitting the limits of infrastructure designed for humans and traditional production workloads, the team hit an inflection point. 

According to Burazin, the key insight was realizing that agents should be treated as a different type of user, with different requirements than human engineers: 

That drove a high-conviction reset. The team sunsetted the earlier product, let go of the existing customer base, rebuilt from scratch in early 2025, and launched the new Daytona in late April 2025 as a runtime infrastructure built specifically for agents.

Daytona is going after a bottleneck that becomes obvious the moment agents leave the demo stage, when code execution stops being a small feature and starts looking like infrastructure. This paradigm is fraught with problems. Agents work iteratively, which requires execution that can run code and preserve context across the workflow. Stateless execution resets on every retry, which forces repeated setup, increases cost, and makes long-running workflows fragile. 

What Daytona is really promising is not just safety. It is a way to give agents a real computer that they can start on demand and control through an API, with sessions that persist long enough to finish real work. 

This market also has the kind of usage curve that can explode. Once a team relies on sandboxes as its execution layer, usage tends to expand with more agents, more tasks, longer sessions, and higher concurrency. That fits Daytona’s pricing model and the way these workloads scale, from computer use systems to reinforcement learning jobs that can require thousands of environments running in parallel. 

Scientific workloads are an emerging example. We have genome work and drug discovery, and chemistry and pharma teams running agents inside sandboxes sized to hundreds of CPUs.

The category will be crowded because agent execution is a universal need. McKinsey’s 2025 survey found 23% of respondents say their organizations are already scaling an agentic AI system somewhere in the enterprise, and another 39% report experimenting with one. This pressure is strongest for enterprise teams running agents that execute code in production, often with sensitive data. 

As adoption grows, more teams will try to solve execution in different ways, building on containers and serverless, buying a sandbox layer, or defaulting to whatever cloud providers and agent-infrastructure companies ship. These approaches either create a major security and reliability burden or rely on stateless serverless execution that resets environments and breaks multi-step workflows. The hard part is that execution at scale demands reliability and security controls that hold up under high concurrency. That is where Daytona stands out, because it is designed around the realities of production agent execution, not just the demo version of it. 

The conviction behind that approach is simple: every knowledge worker needs a computer, and agents will need millions. Building the infrastructure that powers those computers safely and reliably is a platform-sized opportunity. Owning that execution layer puts Daytona underneath an expanding share of agent work as adoption grows.

• Daytona wants to be an enterprise-grade GitHub Codespaces [TechCrunch]

• Daytona Raises $24M Series A to Give Every Agent a Computer [Daytona]

• How LangChain Found a Trusted Partner for Their Sandbox Needs [Daytona]

• Daytona Goes Open Source: Embarking on a Bold New Journey [Daytona]

• Daytona’s source code [GitHub]

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