Cloud development environment powered by Firecracker microVMs with 2-second startup, environment branching, real-time collaboration, and Sandbox SDK for programmatic AI agent integration.
Instant cloud coding environments — preview and test web apps in seconds without setting up anything on your computer. AI agents can create and manage sandboxes programmatically.
CodeSandbox is a cloud development platform that provides instant, shareable development environments running in microVMs. Originally known as a browser-based playground for React applications, CodeSandbox has evolved into a full-featured cloud IDE and development infrastructure platform, with particular relevance to AI agent builders who need programmable, isolated environments for code execution.
The platform's technical foundation is its microVM infrastructure, branded as "Pitcher." Each development environment (called a "Sandbox" or "Devbox") runs in its own Firecracker microVM with dedicated CPU, memory, and storage. Unlike container-based solutions, these microVMs provide strong isolation while maintaining fast startup times (typically 2-5 seconds). Every environment gets a unique URL for previewing web applications, making it easy to share running code with others.
For AI agent use cases, CodeSandbox offers its Sandbox SDK — a programmatic API for creating, modifying, and executing code in sandboxed environments. Agents can spin up a sandbox, write files, install dependencies, run commands, and observe output through the API. This is similar to E2B's offering but with the added benefit of CodeSandbox's mature web IDE, making it easy for humans to inspect and modify what AI agents have built. The platform supports Node.js, Python, Go, Rust, Java, Docker Compose, and virtually any stack that runs on Linux.
CodeSandbox's branching model is a unique feature. Similar to Git branches but for entire development environments, you can create a "branch" of a running environment that forks the entire VM state — filesystem, running processes, databases, everything. This is particularly useful for AI agents that want to try multiple approaches in parallel or provide a safe way to experiment without affecting the main environment.
The collaboration features are best-in-class: real-time multiplayer editing, comments on code, shared terminals, and environment sharing via URL. This makes CodeSandbox particularly suitable for teams building AI-assisted development workflows where both humans and AI agents need to work in the same codebase.
Pricing includes a free tier with limited sandboxes and compute time, Pro plans for individual developers, and Scale plans with more resources, private sandboxes, and admin controls. The platform's primary limitation for AI agent use is that its SDK and API are less purpose-built for programmatic AI interaction compared to E2B, and the pricing can become significant for high-volume automated sandbox creation.
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CodeSandbox has matured from a frontend playground into a capable cloud development platform with unique strengths in environment branching and collaborative editing. The Sandbox SDK for AI integration positions it as a bridge between pure code execution platforms (E2B) and traditional cloud IDEs (Gitpod, GitHub Codespaces). Users praise the instant environments and Docker support but note that pricing can escalate for heavy automated use and that performance occasionally trails local development.
Each development environment runs in its own Firecracker microVM with dedicated CPU (up to 16 vCPU), memory (up to 32GB RAM), and 20GB+ storage — providing VM-level isolation with 2-5 second cold start times.
Use Case:
A developer spins up a full-stack environment with Node.js backend, PostgreSQL database, and React frontend in under 5 seconds, getting a unique preview URL to share with their team immediately.
Fork an entire running development environment — including filesystem, running processes, databases, and environment state — in seconds. Like Git branches but for whole VM states.
Use Case:
An AI agent tries three different approaches to solving a coding problem by branching the environment, running each solution in isolation, comparing results, and keeping the best one — without affecting the original environment.
Programmatic API that lets AI agents create sandboxes, write files, install dependencies, run commands, and read output — enabling automated code execution in isolated environments with human-inspectable results via the web IDE.
Use Case:
A coding assistant creates a sandbox, writes a web application, installs npm packages, runs tests, and gives the user a live preview URL — all through API calls, with the user able to open the IDE to inspect or modify the code.
Multiplayer IDE with real-time cursor tracking, shared terminals, code comments, and instant environment sharing via URL — enabling simultaneous human and AI agent collaboration in the same codebase.
Use Case:
A pair programming session where a developer and an AI agent both edit files in real-time, with the developer reviewing and guiding the agent's changes through the shared IDE interface.
Devboxes support Docker and Docker Compose, allowing databases (PostgreSQL, MySQL, Redis), message queues, and any containerized services to run alongside application code within the microVM.
Use Case:
A developer sets up a complete microservices environment with a Node.js API, Redis cache, PostgreSQL database, and Nginx reverse proxy — all running in a single Devbox with Docker Compose.
Free
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$9.00/month per user
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View Pricing Options →Provide secure, isolated environments for AI agents to write, test, and debug code with full Linux capabilities and human-inspectable results through the web IDE
Real-time multiplayer coding sessions with instant environment sharing via URLs — ideal for pair programming, code reviews, and team onboarding
Instantly spin up full-stack development environments with Docker support for testing ideas without local setup or environment configuration
Automatically create live, running environments for each pull request so reviewers can test changes interactively rather than reading diffs
CodeSandbox works with these platforms and services:
We believe in transparent reviews. Here's what CodeSandbox doesn't handle well:
E2B is purpose-built for programmatic AI code execution with ~150ms startup, a lightweight SDK, and security-focused microVMs. CodeSandbox is a full cloud IDE with development environment management that also offers programmatic access via its SDK. CodeSandbox is better when humans need to inspect and collaborate on what AI agents build (thanks to its IDE). E2B is better for pure backend code execution where speed and API simplicity matter most. CodeSandbox's startup time (2-5 seconds) is slower but still acceptable for most agent workflows.
CodeSandbox Branching creates a full copy of a running development environment — including all files, running processes, databases, and environment state — in seconds. It's like Git branches but for entire VM states. This is powerful for AI agents that want to try multiple approaches in parallel: fork the environment, try different solutions in each branch, and keep the best result.
Yes, Devboxes support Docker and Docker Compose, allowing you to run PostgreSQL, MySQL, Redis, message queues, and any containerized services alongside your application code. Each Devbox runs in a microVM with sufficient resources (up to 16 vCPU, 32GB RAM on Pro) for development-grade databases and full-stack environments.
The free tier includes 20 SDK sandboxes/hour with 4 vCPU VMs. Pro ($9/month per user) increases to 1,000 sandboxes/hour with 16 vCPU VMs, plus VM credits at $0.015 each for compute time. For high-volume agent workloads, the credit-based pricing can add up — budget $0.015 per VM-hour of active sandbox time. Scale plans offer custom pricing for enterprises needing 250+ concurrent sandboxes.
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Daytona is a development environment management platform that creates instant, standardized dev environments for teams and AI coding agents. It provisions fully configured workspaces in seconds from Git repositories, ensuring every developer and AI agent works in an identical environment with the right dependencies, tools, and configurations. Daytona supports devcontainer standards, integrates with popular IDEs, and can run on local machines, cloud providers, or self-hosted infrastructure. It's particularly valuable for teams using AI coding agents that need consistent, reproducible environments to write and test code.
E2B (short for 'edge to browser') provides secure, sandboxed cloud environments where AI agents can write and execute code safely. Each sandbox is an isolated micro-VM that spins up in milliseconds, letting AI models run code, install packages, access the filesystem, and use the internet without risking your infrastructure. E2B is designed specifically for AI agent use cases — coding assistants, data analysis agents, and autonomous AI that needs to execute generated code. The platform offers SDKs for Python and JavaScript, supports custom sandbox templates, and handles the infrastructure complexity of running untrusted AI-generated code at scale.
See how CodeSandbox compares to E2B and other alternatives
View Full Comparison →Deployment & Hosting
E2B (short for 'edge to browser') provides secure, sandboxed cloud environments where AI agents can write and execute code safely. Each sandbox is an isolated micro-VM that spins up in milliseconds, letting AI models run code, install packages, access the filesystem, and use the internet without risking your infrastructure. E2B is designed specifically for AI agent use cases — coding assistants, data analysis agents, and autonomous AI that needs to execute generated code. The platform offers SDKs for Python and JavaScript, supports custom sandbox templates, and handles the infrastructure complexity of running untrusted AI-generated code at scale.
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