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monthly or annual
monthly or annual
per-VM-second or per-credit
Pricing sourced from CodeSandbox · Last verified March 2026
Detailed feature comparison coming soon. Visit CodeSandbox's website for complete plan details.
View Full Features →CodeSandbox runs each project inside a Firecracker microVM and snapshots the full VM state — memory, running processes, open ports, and installed dependencies — to disk. When you reopen a sandbox, the platform restores from the snapshot instead of cold-booting and reinstalling, so your dev server, database, and build tools resume in roughly two seconds.
The Sandbox SDK is a Node.js and Python library that lets developers programmatically create, fork, and destroy CodeSandbox microVMs from their own applications. It's primarily aimed at AI product teams that need to execute LLM-generated code in isolated environments — for example, coding agents, data-analysis copilots, or interactive tutorials — and want kernel-level VM isolation rather than shared-container sandboxing.
Codespaces and Gitpod use container-based dev environments with cold starts measured in tens of seconds to minutes. CodeSandbox uses snapshotted Firecracker microVMs that resume in seconds and supports environment branching (forking a running VM). It also offers a programmatic SDK for agent use cases, which Codespaces and Gitpod do not natively expose.
Yes. CodeSandbox provides a VS Code extension and JetBrains plugin (Cloud Containers) that connect your local IDE to a remote microVM. You get the same microVM infrastructure and real-time collaboration features while keeping your local extensions, keybindings, and editor configuration.
CodeSandbox isolates each sandbox in its own Firecracker microVM with a separate kernel, which is a stronger boundary than the shared-kernel containers used by many code-execution services. This makes it a common choice for AI products that need to execute model-generated code on behalf of end users without exposing the host environment.
AI builders and operators use CodeSandbox to streamline their workflow.
Try CodeSandbox Now →Open-source secure code-execution sandbox for AI agents — spin up a fresh Linux VM in under 200ms, hand your agent a Python/Node/Bash environment, and get back files, plots, and results without ever exposing your infrastructure.
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