Daytona raises $24M Series A to build agent-native compute infrastructure

Croatian-founded Daytona has raised a $24M Series A to build
infrastructure designed for large-scale agent workloads. The round was led by
FirstMark Capital, with Matt Turck joining the board. Additional participation
came from Pace Capital, Upfront Ventures, Darkmode, and E2VC, along with
strategic investments from Datadog and Figma Ventures. The round also includes a group of angel investors, including Gorkem Yurtseven (Co-founder of Fal), Theo Browne (Founder of T3 Chat), Eno Reyes (Co-founder of Factory.ai), Nikita Shamgunov (Founder of Neon), and others.

Every knowledge worker relies on a computer. As software
agents take on more work, they will require computing resources at a much greater
scale, potentially spanning millions of concurrent environments.

Most cloud infrastructure today is optimised for production
workloads – stateless, immutable, and designed for consistent execution. While
effective for serving software, this model is less suited to development and
experimentation, which depend on flexible, stateful environments. Agents have
similar needs but operate at far higher speed and scale, requiring environments
that can launch in milliseconds, branch into parallel executions, support
snapshots, and scale across large numbers of concurrent instances.

Daytona addresses these needs by introducing sandboxes as a
core infrastructure primitive. A sandbox is a programmatic, composable
computing environment in which CPU, memory, storage, GPU, networking, and the
operating system can be provisioned on demand. These environments can be
started, paused, forked, snapshotted, or terminated at any point during
execution.

Founded in 2023 by Ivan Burazin (CEO), Vedran Jukić (CTO), and Goran Draganić (Chief Architect), Daytona focuses on providing programmable, sandboxed compute that
allows agents to run code, explore alternative execution paths, and persist
state at scale.

Using Daytona, an agent can launch a sandbox, run for
extended periods, reach decision points, and fork into parallel branches to
evaluate alternative approaches. Promising branches can be snapshotted, while
others are discarded. State persists across failures, and execution paths can
be cloned, resumed, or merged. Workloads may run for minutes or for days.

This model reflects a broader shift from cloud primitives
designed around human workflows to infrastructure optimised for agents. Daytona
focuses on making dedicated computing environments for agents practical through
rapid startup, persistent state, and integrated tooling for activities such as
writing code, using version control systems, and executing workloads securely
at scale.

Following the Series A, Daytona plans to expand beyond
sandboxes to support a broader set of agent-native infrastructure. The company
will scale its systems for higher volumes of concurrent agent workloads, deepen
integrations with developer and agent tooling, and continue improving
reliability, security, and performance.

Daytona also plans to grow its team to
support product development and customer adoption.