Open-source sandbox infrastructure for running AI-generated code safely. Sub-90ms startup, per-second billing, and stateful environments for AI agents and code interpreters.
Secure sandboxes for running AI-generated code. Spin up isolated environments in under 90ms so AI agents can safely execute code.
Daytona spins up isolated sandboxes in under 90 milliseconds. That number matters because AI coding agents generate code constantly, and each snippet needs a safe place to run. Daytona's entire pitch is speed plus isolation: your AI agent writes code, Daytona executes it in a throwaway environment, and nothing touches your production infrastructure.
The closest competitor is E2B, which also offers cloud sandboxes for AI code execution. Daytona differentiates on two fronts: stateful environments that persist between sessions (E2B sandboxes are ephemeral by default) and an open-source core you can self-host. If your agents need to install packages, modify files, and pick up where they left off, Daytona handles that without workaround code.
Daytona uses per-second billing. A single vCPU costs $0.0504/hour, memory runs $0.0162/hour per GiB, and storage is $0.000108/hour per GiB after the first 5 GB free. New accounts get $200 in free compute credits with no credit card required. That $200 goes a long way for testing: running a 2-vCPU sandbox with 4 GiB RAM for 8 hours costs roughly $0.92.
Startups can apply for up to $50,000 in free credits through their startup program. Enterprise pricing requires a sales conversation.
For comparison, E2B charges $0.000145/s per CPU core. At that rate, a single core for one hour costs $0.522 versus Daytona's $0.0504. Daytona is meaningfully cheaper per compute hour, though E2B includes some features (like built-in file upload APIs) that Daytona handles differently.
Each sandbox is a full Linux environment with filesystem access, networking, and package installation. Your agent can run pip install, write to disk, open network connections, and execute arbitrary shell commands inside the boundary. The MCP (Model Context Protocol) server support means AI agents using MCP-compatible frameworks can provision sandboxes through standardized API calls.
GPU support is available for workloads that need it: 12GB GDDR6 GPUs at $0.014/second. That is expensive for sustained use but reasonable for burst ML inference tasks inside agent workflows.
Daytona fits best when your AI system generates code that needs execution in a safe, isolated environment. Code interpreters in chatbots, automated testing pipelines, AI coding agents running generated functions, and data analysis workflows that run user-submitted scripts are all solid use cases.
Skip Daytona if you just need a simple REPL environment. For basic code execution without persistence or networking needs, a Docker container or serverless function is simpler and cheaper. Daytona's value shows up when you need many sandboxes running concurrently with fast startup times and state that survives between sessions.
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Daytona is the cost-effective choice for teams that need fast, stateful sandboxes for AI code execution. Cheaper than E2B per compute hour with the added benefit of session persistence and an open-source core. The platform is newer and less polished in documentation and ecosystem, but the core functionality is solid for AI agent and code interpreter workloads.
Sandboxes boot in under 90 milliseconds, enabling AI agents to request execution environments on demand without noticeable latency. Each sandbox is a fully isolated Linux environment.
Use Case:
An AI coding assistant generates a Python function, spins up a Daytona sandbox, executes the code, captures the output, and tears down the environment in under 2 seconds total.
Unlike ephemeral sandbox providers, Daytona environments persist between sessions. Installed packages, written files, and configured state survive across multiple agent interactions.
Use Case:
A multi-step data analysis agent installs pandas and matplotlib in session one, then returns hours later to generate visualizations without reinstalling dependencies.
Native Model Context Protocol server support allows MCP-compatible AI agents and frameworks to provision, manage, and tear down sandboxes through standardized protocol calls.
Use Case:
A Claude-based coding agent uses MCP to request a sandbox with specific Python packages pre-installed, execute generated code, and retrieve results through the standard MCP interface.
Optional GPU attachment (12GB GDDR6) for sandboxes that need ML inference, model fine-tuning, or compute-heavy data processing within the isolated environment.
Use Case:
An AI agent fine-tunes a small language model on user-provided data inside an isolated GPU sandbox, preventing any access to the host system.
Free
one-time credits
$0.0504/hr per vCPU
Up to $50,000 credits
Contact sales
Ready to get started with Daytona?
View Pricing Options →AI agents that generate and run code need isolated environments. Daytona's sub-90ms startup means your agent doesn't wait, and sandbox isolation means generated code can't damage anything.
Chatbots and AI assistants that execute user-requested code (data analysis, visualizations, calculations) use Daytona as the execution layer with full Python/Node environments.
CI/CD systems that need to run untrusted or generated test suites in isolated environments, with per-second billing keeping costs proportional to actual test duration.
Stateful sandboxes let analysis agents install packages once and return across sessions, avoiding the reinstall overhead of ephemeral sandbox providers.
Daytona works with these platforms and services:
We believe in transparent reviews. Here's what Daytona doesn't handle well:
Both provide cloud sandboxes for AI-generated code. Daytona is cheaper per compute hour ($0.0504/hr vs E2B's ~$0.52/hr per core), offers stateful environments that persist between sessions, and has an open-source core. E2B has a more mature ecosystem, built-in file upload APIs, and broader framework integrations. Choose Daytona for cost efficiency and state persistence; E2B for ecosystem maturity.
The $200 covers compute (vCPU and memory) costs. At standard rates, that's roughly 3,968 hours of single-vCPU usage or about 165 days of continuous light use. For typical AI agent workloads with intermittent sandbox creation, the free tier lasts weeks to months.
Yes. Daytona's core is open-source on GitHub (65k+ stars). You can deploy it on your own infrastructure for full control over data residency and to eliminate per-usage costs. Self-hosting requires managing the infrastructure yourself.
Yes. Daytona provides an MCP server that lets MCP-compatible AI agents provision sandboxes, execute code, and manage environments through the standardized protocol. This simplifies integration with frameworks like Claude, OpenAI Agents, and other MCP clients.
Daytona sandboxes are full Linux environments. Any language that runs on Linux works: Python, Node.js, Go, Rust, Java, and more. You can install packages via apt, pip, npm, or any standard package manager within the sandbox.
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Daytona now offers $200 in free compute credits (up from $30), a startup program with up to $50,000 in credits, GPU-enabled sandboxes, and has grown to 65,000+ GitHub stars. Per-second billing and MCP server support were added for tighter AI agent integration.
Deployment & Hosting
Secure cloud sandboxes for AI code execution using Firecracker microVMs. Purpose-built for AI agents, coding assistants, and data analysis workflows with hardware-level isolation and sub-second startup times.
Deployment & Hosting
Modal: Serverless compute for model inference, jobs, and agent tools.
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