Daytona is secure, elastic infrastructure for running AI-generated code, repositioned as an agent runtime. It targets a specific pain point: AI agents generate code that must run somewhere safe, and slow sandbox startup breaks the flow of an interactive agent. Daytona reports very fast cold starts, about 27 milliseconds using pre-warmed pools of sandboxes, and aims at regulated enterprises that need strong isolation with production performance. The company raised a 24 million dollar Series A led by FirstMark Capital, announced on 5 February 2026.
Where it sits in the stack
Daytona sits between your agent and the compute that runs untrusted code. Your agent asks for a sandbox; Daytona hands over one from a warm pool almost instantly, runs the code, and returns the result.
Installation
Daytona provides SDKs for Python and TypeScript. Install the Python SDK from PyPI.
pip install daytonaFor a TypeScript or JavaScript project, install the SDK with npm.
npm install @daytona/sdkCreate an API key in the Daytona dashboard and export it so the SDK can authenticate.
export DAYTONA_API_KEY="your_api_key_here"Running code in a sandbox
The core pattern configures a client, creates a sandbox, and runs code inside it. This Python example follows the official quickstart.
from daytona import Daytona, DaytonaConfig
config = DaytonaConfig(api_key="YOUR_API_KEY")
daytona = Daytona(config)
sandbox = daytona.create()
response = sandbox.process.code_run('print("Hello World")')
print(response.result)Because each sandbox is a full environment, an agent can run multi-line programs and read structured output. This example computes a result and prints it.
from daytona import Daytona, DaytonaConfig
config = DaytonaConfig(api_key="YOUR_API_KEY")
daytona = Daytona(config)
sandbox = daytona.create()
code = """
numbers = [4, 9, 16, 25]
roots = [n ** 0.5 for n in numbers]
print(roots)
"""
response = sandbox.process.code_run(code)
print(response.result)The SDK also runs raw shell commands through sandbox.process.exec, which suits Git operations, package installs, and file inspection.
from daytona import Daytona, DaytonaConfig
config = DaytonaConfig(api_key="YOUR_API_KEY")
daytona = Daytona(config)
sandbox = daytona.create()
response = sandbox.process.exec("echo 'Hello, World!'")
print(response.result)How a sandbox request flows
The lifecycle of a Daytona sandbox is short. The warm pool is what makes the create step fast enough for interactive agents.
How it compares
The agent-sandbox market has a handful of serious players. Daytona competes on cold-start speed and its enterprise, regulated-industry positioning. The table compares it with E2B, Modal, and self-managed containers.
| Daytona | E2B | Modal | Self-managed containers | |
|---|---|---|---|---|
| Primary use | Agent code execution | Agent code execution | Serverless functions | General workloads |
| Cold start | About 27 ms | Sub-second | Sub-second | Varies widely |
| Positioning | Regulated enterprise | Open-source runtime | GPU and batch compute | Full control, more work |
| Open source | Yes | Yes | No | Yes |
| Best for | Speed-sensitive agents | Code interpreters | Heavy compute jobs | Custom infrastructure |
When not to use it
Daytona is not the right choice in every situation.
- You run trusted code you wrote yourself. Sandbox isolation protects against untrusted, model-generated code. For your own controlled code, standard infrastructure is simpler.
- You need long-lived services. Sandboxes suit short, disposable tasks. A persistent API or database belongs on a platform built for always-on services.
- You want the largest open-source community. E2B is an established open-source sandbox runtime with wide adoption. If community size drives your choice, weigh that directly.
- Your workload is GPU-heavy batch compute. For large-scale training or inference, a compute platform like Modal designed around GPUs fits better than a code sandbox.
Further reading
- Daytona documentation : official docs, SDK reference, and getting-started guide.
- Daytona official site : product overview and the case for a fast agent runtime.
- Daytona on GitHub : the open-source infrastructure for running AI-generated code.
- E2B : a competing open-source agent sandbox to compare against.
- Modal : serverless compute for functions, batch jobs, and GPU workloads.
- What is an AI agent? : the systems that generate the code Daytona runs.