11 Best Sandbox Runners in 2026

Running code in production can be risky if something goes wrong. Sandbox runners solve this by giving you a safe, isolated place to run and test code. If you're building AI agents, running user-generated code, or testing applications, a good sandbox runner keeps things secure without slowing everything down.

But with so many tools out there, choosing the right one can be hard. This guide walks through some of the best sandbox runners to help you pick the right fit for your development workflow.

What is a sandbox runner?

A sandbox runner is a separate, secure environment where you can run code without affecting your main system or other processes. It creates a protective barrier around the code so it can't access sensitive data or interfere with other apps.

Sandbox runners are especially useful when you need to:

• Run code submitted by users
• Test scripts you don't fully trust
• Build or run AI coding agents
• Try experimental features without risking production systems

Factors to consider when choosing a sandbox runner

Before looking at specific tools, it helps to know what actually matters when picking a sandbox runner for your use case.

Isolation and security

The main job of any sandbox is to keep things safe. Your sandbox runner should clearly separate the code it runs from the main system. Look for things like containers, virtual machines, or microVMs that make it hard for code to "escape" its sandbox. It should also limit access to important resources like the file system, network, and system calls.

Performance and speed

Sandboxes always add some overhead, but good ones keep it small. Check how fast environments start, how quickly they run code, and whether they support tricks like snapshots to speed up startup time. For production apps, performance can be the difference between a smooth experience and annoying slowdowns.

Language and runtime support

Make sure the sandbox runner works with the languages and runtimes you actually use. Some tools only support a few languages like JavaScript or Python, while others are more flexible. Think about whether you need custom runtimes, certain language versions, or specific libraries and dependencies.

Scalability and resource management

As your app grows, your sandbox setup needs to grow with it. Look for tools that can run many sandboxes at once, manage resources efficiently, and offer pricing that makes sense as you scale. You should be able to set limits on CPU, memory, and execution time to avoid one job using up everything.

Developer experience and integration

A sandbox runner should be easy for developers to work with. Pay attention to the quality of the API, SDKs, and documentation, and how simple it is to plug into your existing systems. The easier it is to integrate and update, the faster your team can ship.

Observability and debugging

Because sandboxes are isolated, it's important to have good visibility into what's happening inside them. Choose a tool that gives you logs, metrics, and debugging options. This really helps when you're fixing bugs, tracking down errors, or tuning performance.

With these points in mind, here are some of the best sandbox runners available.

1. E2B

E2B is an open-source sandbox platform made for AI agents and LLM apps. It runs secure, isolated cloud environments using Firecracker microVMs (the tech behind AWS Lambda), which start in under 200ms with no cold starts. It is used by roughly half of the Fortune 500 and generates millions of sandboxes every week.

🌟 Key features

• Firecracker microVM isolation for security
• Sub-200ms startup with no cold starts
• Support for Python, JavaScript, Ruby, C++, and any Linux-compatible language
• Long-running sandboxes (up to 24 hours on Pro plan)
• Code Interpreter with Python and JavaScript kernels
• File system persistence and file uploads
• Terminal access and browser support
• Custom sandbox templates
• Package installation (pip, npm, apt, etc.)
• WebSocket streaming for real-time output
• Desktop Sandbox for computer use agents

➕ Pros

• Purpose-built for AI agents and LLM workflows
• Used by roughly half of the Fortune 500 at scale
• Open-source with active community (~11k GitHub stars)
• Extremely fast startup times with no cold starts
• Strong security through Firecracker microVM isolation
• Supports any language or framework that runs on Linux
• Can customize sandboxes with templates
• Real-time output streaming for better UX
• Integrates with all major LLM providers
• Available as BYOC, on-premise, or self-hosted for enterprises
• Free hobby tier with $100 in usage credits

➖ Cons

• Usage costs can accumulate for high-volume applications
• Free tier limited to 1-hour sessions and 20 concurrent sandboxes
• Self-hosting is not production-ready for most teams

💲 Pricing

E2B has three main pricing tiers: a Hobby plan that is free and includes a one-time $100 usage credit, community support, up to 1-hour sessions, and 20 concurrent sandboxes with no credit card required; a Pro plan at $150/month with longer 24-hour sessions, more concurrent sandboxes, and customizable CPU and RAM; and an Enterprise plan with custom pricing, BYOC, on-prem, or self-hosted options.

Usage is billed per second, with RAM included in the CPU price, so a 1 vCPU sandbox costs about $0.05 per hour and you only pay for the time your code is actually running.

2. Daytona

Daytona is a secure platform built for running AI-generated and untrusted code. After pivoting from development environments to AI agent infrastructure in early 2025, it now focuses on sub-90ms sandbox creation for AI agents, code execution workflows, and safe testing. In February 2026 Daytona raised a $24M Series A to expand its agentic infrastructure platform.

It uses Docker/OCI containers to isolate each sandbox (with optional Kata Containers for enhanced isolation), with configurable CPU, memory, and disk, plus controls for how long a sandbox stays active. Daytona also supports computer use sandboxes for desktop automation across Linux, macOS, and Windows.

🌟 Key features

• Sub-90ms sandbox creation (some configurations reach 27ms)
• Docker/OCI container-based isolation with optional Kata Containers
• Customizable resources (vCPU, RAM, disk)
• Automated lifecycle management (auto-stop, auto-archive, auto-delete)
• File system persistence across stop/start cycles
• Snapshot support for environment templates
• Preview URLs for running applications
• Built-in toolbox (file system, Git, process execution, PTY)
• Computer use sandboxes (Linux, macOS, Windows desktops)
• Built-in language server support

➕ Pros

• Fastest cold starts in the market at sub-90ms
• Flexible resource configuration
• Intelligent cost optimization with automatic state management
• Supports any language or framework via Docker images
• Can run indefinitely or auto-stop after inactivity
• Archive sandboxes to object storage for cost-effective long-term retention
• $200 in free compute credits included
• Startup program offering up to $50,000 in credits
• Enterprise on-premise deployment available
• Expanding into computer use and reinforcement learning use cases

➖ Cons

• Docker containers by default (weaker isolation than microVMs without Kata configuration)
• Relatively new pivot with growing ecosystem
• Default resource limits may require support contact for higher allocations

💲 Pricing

Daytona uses simple usage-based pricing and does not require a credit card for the free trial. You get $200 in free compute credits to start. A small sandbox with 1 vCPU and 1 GiB of RAM costs about $0.067 per hour while it is running, and when it is stopped you only pay for storage, with archived sandboxes stored even more cheaply. Pricing scales with usage, and startups can apply for up to $50,000 in additional credits.

3. Modal

Modal is an AI infrastructure platform designed for data and machine learning workloads. It provides containerized execution environments that scale from zero to 50,000+ concurrent instances in seconds, making it ideal for inference, training, batch processing, and any workload that needs to run at scale. Companies like Lovable and Quora run millions of untrusted code executions through Modal every day.

🌟 Key features

• Container-based execution with sub-second cold starts
• Elastic GPU scaling (H200, H100, A100, L40S, A10, L4, T4)
• Automatic scaling from 0 to 50,000+ concurrent containers
• SDKs for Python, JavaScript, and Go
• Scheduled jobs and cron support
• HTTP endpoints and web servers
• Built-in distributed storage layer
• Programmatic sandboxes for untrusted code
• Shareable collaborative notebooks
• Secrets management

➕ Pros

• Exceptional for AI/ML and data workloads
• Multi-language SDK including Python, JavaScript, and Go
• Easy access to GPUs for inference and training
• Scales automatically based on workload
• Sub-second cold starts
• Proven at massive scale — Lovable and Quora run millions of executions daily
• Pay only for actual compute time (CPU cycles)
• Great for batch processing and parallel execution
• Built-in scheduling for periodic tasks
• Unified observability with integrated logging
• $30/month free compute credits
• SOC 2 and HIPAA compliant

➖ Cons

• gVisor isolation only (no microVM option)
• No BYOC deployment option
• Python-first SDK; JavaScript and Go SDKs are available but less mature
• Sandbox pricing uses non-preemptible compute, which carries a cost premium

💲 Pricing

Modal uses per-second billing where you pay only for actual compute time. The Starter plan includes $30/month free compute credits with up to 3 workspace seats, 100 containers, 10 GPU concurrency, and community support. The Team plan at $250/month adds $100 in credits, unlimited seats, 1,000 containers, 50 GPU concurrency, and priority support. Enterprise plans offer volume discounts, higher limits, and dedicated support. Sandbox workloads use non-preemptible pricing at $0.00003942 per CPU core per second.

4. Fly.io

Fly.io is a global platform for running full-stack apps and, since January 2026, home to Sprites — its purpose-built stateful sandbox product for AI agents. Sprites are persistent Linux VMs that create in 1–2 seconds, checkpoint and restore in around 300ms, and automatically idle when inactive so you only pay for what you use. Each Sprite gets a 100GB NVMe filesystem that survives indefinitely between sessions.

Fly.io CEO Kurt Mackey's pitch for Sprites is direct: "Ephemeral sandboxes are obsolete. Claude doesn't want a stateless container. Claude wants a computer." The result is a sandbox that installs packages once, persists state across sessions, and picks up exactly where it left off.

🌟 Key features

• Firecracker microVM isolation
• Persistent 100GB NVMe root filesystem per Sprite
• Checkpoint/restore in ~300ms
• Auto-idle with no billing when inactive
• Global deployment across 35+ regions
• Any language or framework via full Linux environment
• Built-in managed PostgreSQL and Redis (via Fly Machines)
• Private networking (WireGuard mesh)
• GPU support (A10, L40S, A100) via Fly Machines
• REST API and TypeScript/Go SDKs for Sprites
• Pre-installed Skills for Claude Code integration

➕ Pros

• Persistent state across sessions — agents don't rebuild environments every run
• Firecracker microVM isolation for strong security
• Checkpoint/restore means instant rollback from a broken state
• Auto-idle keeps costs near zero when a Sprite is not in use
• Full Linux environment with no language or runtime restrictions
• Global edge regions with automatic traffic routing
• Transparent per-second billing
• No charges when idle — only actual CPU, memory, and storage

➖ Cons

• Sprites launched January 2026 — newer product with smaller ecosystem than E2B or Daytona
• CPU-only for Sprites (GPU workloads require separate Fly Machines setup with Docker images)
• Creation time of 1–2 seconds is slower than Daytona's sub-90ms or E2B's sub-200ms
• More complex for teams not already familiar with Fly.io

💲 Pricing

Fly.io has usage-based pricing with per-second billing. Sprites bill at $0.07/CPU-hour and $0.04375/GB-hour of memory, with no charges when idle. A 4-hour Claude Code session costs roughly $0.44. The general Fly.io free tier includes a few shared-CPU VMs, storage, and bandwidth. Annual compute credit purchases save up to 40%.

5. Freestyle

Freestyle is a VM platform built for AI agents and developer sandboxes, offering full Linux environments that provision in under 800ms. Unlike microVM-based runners, Freestyle gives you real root access, nested virtualization support, and a first-class TypeScript SDK — making it a strong fit for teams building coding agents, user sandboxes, or browser automation workflows that need more than a constrained execution environment.

Each VM runs with full KVM support, meaning you can run Docker, other VMs, or any virtualization stack inside a Freestyle VM. Agents can fork a running VM without pausing it, and VMs can be hibernated to disk so billing stops while state is fully preserved.

🌟 Key features

• Full Linux VMs with root access and real networking stack
• Live forking — clone a running VM without pausing it
• Hibernate (suspend) and resume with full memory state intact, billed only for storage while paused
• Nested virtualization with full KVM support (run Docker, VMs-in-VMs)
• Declarative VmSpec for reproducible environments and snapshot layering
• Automatic idle timeout with configurable suspension
• Pre-built language integrations: Node.js, Python, Bun, Ruby, uv, Java
• SSH access and interactive web terminal
• First-class TypeScript SDK (freestyle-sandboxes)

➕ Pros

• Full Linux power — not a constrained sandbox; supports any OCI image and real system-level operations
• Live forking enables parallel agent exploration without duplicating setup work
• Hibernate/resume preserves exact VM state across sessions at no compute cost
• Nested virtualization covers use cases other runners block entirely (Docker-in-VM, browser automation with full stack)
• Declarative specs with snapshot caching mean repeated VM creation is instant after the first build
• Language integrations are composable — mix Node.js, Python, and Bun in a single VM
• No credit card required to get started
• Default VM specs are generous (4 vCPUs, 8GB RAM)

➖ Cons

• Newer platform with a smaller ecosystem and community than E2B or Modal
• No BYOC option currently available

💲 Pricing

Freestyle uses subscription tiers plus usage-based overage. The Free plan costs nothing and includes up to 10 concurrent VMs, 500 repositories, and 500 runs per month. The Hobby plan is $50/month (plus usage over $50) with up to 40 concurrent VMs and 5,000 runs. The Pro plan is $500/month (plus usage) with up to 400 concurrent VMs and 500,000 runs. VM usage is billed per resource: vCPU at $0.04032/hour, memory at $0.01294/GiB-hour, and storage at $0.000086/GiB-hour — with 20 free vCPU-hours and 40 free GiB-hours per day included on all plans.

6. Cloudflare Sandboxes

Cloudflare Sandboxes provides secure, isolated environments for running AI agents and untrusted code. Built on Cloudflare's global network, each sandbox runs in its own isolated Linux container — a full Ubuntu environment with Python, Node.js, Git, and common developer tools pre-installed. Because it is built on top of Cloudflare Containers and Durable Objects, sandboxes maintain persistent state while the container is active and integrate directly with Workers, R2, and the rest of Cloudflare's developer platform.

🌟 Key features

• Per-VM isolated Linux containers (Ubuntu)
• Supports Python, Node.js, and any Linux-compatible language or runtime
• Global deployment across Cloudflare's network
• Integrated with Cloudflare's developer platform (Workers, R2, KV, AI)
• Built-in code interpreter with persistent Python and JavaScript kernels
• WebSocket transport for high-throughput SDK calls
• Git operations, file system access, and background process control
• Preview URLs via automatic subdomain routing
• Real-time streaming output
• Web terminal support

➕ Pros

• Strong isolation through per-VM Linux containers
• Benefits from Cloudflare's global infrastructure and low-latency edge network
• Integrates natively with Cloudflare Workers, R2, KV, and Workers AI
• Supports any Linux-compatible language, not just Python and Node.js
• Built-in security and DDoS protection at the network layer
• Automatic scaling without configuration
• Purpose-built for AI agents and untrusted code
• Free tier available for experimentation
• Battle-tested infrastructure powering millions of sites

➖ Cons

• Still in beta with an evolving feature set
• State is lost when a container idles (10-minute default timeout resets the environment)
• Requires a Workers paid plan ($5/month) for production use beyond the free tier
• No BYOC option
• No GPU support

💲 Pricing

Cloudflare Sandboxes pricing is based on the underlying Containers platform. CPU time is billed on active usage only (not provisioned resources), at $0.00002 per vCPU-second. Memory and disk are billed on provisioned resources. A free tier is available for experimentation. Production use requires a Workers paid plan at $5/month, after which usage scales with consumption.

7. Vercel Sandbox

Vercel Sandbox is an ephemeral compute primitive built for safely running untrusted or user-generated code. It is designed for AI agents, code generation, and developer experimentation, giving you isolated environments to execute third-party code without exposing production systems. Each sandbox runs inside a Firecracker microVM powered by Vercel's Fluid compute model, which means you are only billed for active CPU time — not I/O wait — resulting in up to 95% lower cost for bursty or I/O-bound workloads.

🌟 Key features

• Ephemeral Firecracker microVMs for untrusted code execution
• Active CPU billing via Fluid compute (I/O wait excluded)
• Node.js (node22) and Python (python3.13) runtimes
• Up to 8 vCPUs with 2 GB RAM per vCPU
• Git repository cloning and package installation
• Up to 4 open ports per sandbox
• Real-time log streaming
• Sudo access for package management
• TypeScript and Python SDKs
• CLI for sandbox management

➕ Pros

• Active CPU billing only — not charged during I/O wait, up to 95% cheaper for bursty workloads
• Purpose-built for AI agents and code generation workflows
• Can clone private repositories and install packages
• Real-time development server support with live previews
• Strong isolation for running untrusted code safely
• Integrates with Vercel's existing authentication (OIDC)
• Generous free tier for hobby projects (5 CPU hours/month)
• Available on all Vercel plans, including standalone SDK use on non-Vercel platforms

➖ Cons

• Limited to Node.js and Python runtimes
• Hobby plan capped at 45-minute max runtime
• Concurrent sandbox limits (10 for Hobby, 2,000 for Pro/Enterprise)
• Still in beta

💲 Pricing

Vercel Sandbox is available on all plans with usage-based pricing. The Hobby plan includes 5 CPU hours, 420 GB-hr provisioned memory, 20 GB network bandwidth, and 5,000 sandbox creations per month for free. Pro and Enterprise plans pay $0.128 per active CPU hour, $0.0106 per GB-hr of provisioned memory, $0.15 per GB network, and $0.60 per million sandbox creations. Maximum runtime is 45 minutes for Hobby and 5 hours for Pro/Enterprise, with a default of 5 minutes that you can configure.

8. Val Town

Val Town lets you write and run TypeScript or JavaScript in the browser with instant execution. Each function you create (a "val") gets its own API endpoint, so you can quickly build webhooks, cron jobs, and small utilities without managing servers.

It runs on Deno, uses V8 isolates for safe sandboxing, and has a social layer where you can browse, fork, and remix other people's code. With the built-in Townie AI assistant, Val Town is especially useful for fast automations, simple APIs, and custom workflows.

🌟 Key features

• Browser-based code editor with instant deployment
• Automatic API endpoints for every function
• Scheduled execution (cron jobs as fast as 1-minute intervals)
• Built-in blob storage for file uploads and data
• Social features (browse, fork, remix code)
• NPM package support and Deno compatibility
• Built-in secrets management
• Townie AI coding assistant (pay-per-use credits)
• Custom domain support
• Version control and code intelligence

➕ Pros

• Incredibly fast to go from idea to deployed function
• No infrastructure management required
• Built on Deno runtime with web standards
• Social features make learning and collaboration easy
• Generous free tier for experimentation
• Automatic HTTPS endpoints for all functions
• Can integrate with thousands of APIs
• Great for automation, bots, and glue code
• Active community with shared templates

➖ Cons

• Execution time limits can be restrictive for complex tasks
• TypeScript/JavaScript only (no other language support)
• Free tier limited to 1-minute wall clock time

💲 Pricing

Val Town has four pricing tiers: Free, Pro, Teams, and Enterprise. The Free plan is generous enough for small projects. Pro suits most individual developers and includes $5/month in Townie AI credits. Teams adds $100/month in Townie credits, unlimited custom domains, and team accounts. Townie is also available as pay-per-use credits separately from the plan tier, at a 50% markup over raw LLM costs. Yearly billing saves two months.

9. CodeSandbox

CodeSandbox is a browser-based development environment built for web development. It uses microVMs to provide full Node.js environments in the cloud, letting you work on complete projects with backend services, databases, and frontend frameworks all running together.

🌟 Key features

• Full-stack development environments
• Real-time collaboration
• MicroVM-based sandboxes and devboxes
• GitHub integration
• Framework templates (React, Vue, Next.js, etc.)
• Built-in terminal and debugging
• Hot reload and instant preview
• CodeSandbox SDK for programmatic management
• Private NPM registry support
• VS Code extension

➕ Pros

• Excellent for web development workflows
• Real-time collaboration features are superb
• Fast environment setup with templates
• Integrates seamlessly with GitHub
• Can run backend services alongside frontend
• Great for teaching and code demonstrations
• No local setup required for contributors
• Built-in browser preview for instant feedback
• SDK enables programmatic environment creation
• Unlimited sandboxes and devboxes on all plans

➖ Cons

• VM credit system can be complex to understand
• Build plan limited to 40 hours of VM credits monthly
• Higher-tier VMs require Pro or Enterprise plans

💲 Pricing

CodeSandbox uses a VM credit system where 1 credit = $0.015. The Build plan is free with 40 hours of monthly VM credits (using Pico VMs), unlimited sandboxes, 5 workspace members, and up to 4 vCPUs + 8GB RAM. The Pro plan at $9/month adds 60 hours of credits, 20 members, and VMs up to 16 vCPUs + 32GB RAM.

10. StackBlitz

StackBlitz created WebContainers, a WebAssembly-based system that runs Node.js directly in your browser. Instead of using remote servers, your whole dev environment runs inside the tab, so it starts instantly and can even work offline.

🌟 Key features

• WebContainer technology (Node.js in browser via WebAssembly)
• Instant environment startup (milliseconds)
• Offline-capable development
• Full npm package support
• Integrated terminal
• Hot reload and fast refresh
• GitHub repository integration
• Framework templates
• Bolt.new AI code generation
• WebContainer API for embedding
• Zero network latency

➕ Pros

• Runs entirely in your browser for privacy and speed
• Instant cold starts with no waiting for servers
• Can continue working offline
• No remote server costs for basic usage
• Excellent for creating bug reproductions
• Great documentation and examples
• Strong integration with popular frameworks
• Secure by default (code doesn't leave your device)
• AI-powered app creation with Bolt.new
• Trusted by major companies (Shopify, Google, etc.)

➖ Cons

• Limited to JavaScript/TypeScript ecosystem
• Complex projects may hit browser resource limits
• WebContainer technology requires modern browsers

💲 Pricing

StackBlitz offers a generous free tier for public projects with full access to WebContainers technology, making it ideal for learning, open-source work, and demonstrations. Paid plans provide private repositories, increased resources, and priority support, with specific pricing available on their website.

11. Replit

Replit is an online IDE and hosting platform that runs in the browser and supports 50+ languages using isolated containers. It is used by millions of developers, from hobbyists to Fortune 500 teams.

🌟 Key features

• 50+ language support
• Browser-based IDE with AI assistance (Replit Agent)
• Real-time collaboration
• Instant hosting and deployment
• Built-in PostgreSQL database
• Package management for all languages
• Always-on deployments
• Mobile app for coding anywhere
• Visual Editor for design refinement
• Autonomous agent that tests its own code
• Figma import capability
• Built-in Auth and Database services

➕ Pros

• Supports more languages than almost any competitor
• Zero setup required to start coding
• Real-time collaboration for pair programming
• Great for learning, prototyping, and production
• Replit Agent dramatically speeds up development
• Can deploy applications directly from the IDE
• Mobile app lets you code from anywhere
• Strong community with 40 million creators
• SOC 2 compliant for enterprise use
• Private deployments for internal tools
• SSO and RBAC for team management

➖ Cons

• Free tier has significant limitations (1,200 minutes dev time, public apps only)
• Advanced features require paid plans
• Agent autonomy limited on free tier
• Weakest isolation model among AI-focused options in this list

💲 Pricing

Replit offers a Starter plan that is free with a Replit Agent trial, 10 development apps with temporary links, public apps only, 1,200 minutes of development time, and basic AI features. The Replit Core plan at $20/month includes full Agent access, $25 in monthly credits, unlimited private and public apps, 4 vCPUs, 8 GiB RAM, and advanced AI features.

Final thoughts

In this article we explored some of the strongest sandbox runners available and looked at how they handle isolation, performance, language support, and developer experience. If you are still undecided after comparing your options, we recommend using E2B as your default choice. It is purpose-built for AI agents and LLM workflows, powered by Firecracker microVM isolation, supported by an active open-source community, and proven in demanding production environments by roughly half of the Fortune 500.

You can still bring in other platforms later for very specific needs — Modal if your workloads are GPU-heavy and Python-first, Fly.io Sprites if your agents need persistent state across long sessions, or Freestyle if you need full Linux VMs with nested virtualization. But starting with a reliable, AI-focused sandbox runner lets you move faster and ship with more confidence. With that foundation in place, you can spend less time worrying about infrastructure and more time building useful, trustworthy AI systems.

Happy coding!

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