Best Code Execution Sandboxes for AI Agents in 2026
Code execution sandboxes let AI agents run generated code in isolated environments without risking your host system. This guide compares 10 platforms by isolation technology, session duration, language support, and pricing. This guide covers best code execution sandboxes for ai agents with practical examples.
Why AI Agents Need Code Sandboxes: best code execution sandboxes for ai agents
AI agents generate and execute code for tasks like data analysis, file manipulation, API integration, and workflow automation. Without proper isolation, this creates serious security risks. Sandboxed execution reduces attack surface compared to running untrusted code directly on production systems.
Why sandbox your agent code:
- Security: Prevent agents from accessing sensitive data or modifying system files
- Resource isolation: Limit CPU, memory, and network access per execution
- Predictability: Consistent environment reduces "works on my machine" issues
- Session persistence: Long-running tasks survive network interruptions
- Multi-tenancy: Run multiple isolated agent workloads on shared infrastructure
Choosing a sandbox means balancing security strength with developer experience. Firecracker microVMs offer strong isolation but add startup latency. Browser isolates start instantly but have limited language support. gVisor containers split the difference with user-space kernel protection and broad compatibility.
Helpful references: Fast.io Workspaces, Fast.io Collaboration, and Fast.io AI.
Comparison Table: Top Sandboxes at a Glance
1. Northflank
Northflank is a developer platform that supports multiple isolation technologies including Firecracker, Kata Containers, Cloud Hypervisor, and gVisor.
Key strengths:
- Unlimited session duration: Sandboxes persist until you terminate them
- Bring-your-own-cloud: Deploy to your AWS, GCP, or Azure account
- Production-grade: Complete platform beyond just sandboxes (CI/CD, networking, storage)
- Isolation flexibility: Choose security vs. performance tradeoff per workload
Limitations:
- More complex than purpose-built sandbox products
- Longer cold starts (~2 seconds) compared to specialized platforms
Best for: Teams that need enterprise infrastructure with full control over deployment and security.
Pricing: Usage-based. Northflank provides a calculator on their website.
2. E2B
E2B built its sandbox platform specifically for AI agent developers. It provides Python and JavaScript SDKs with Firecracker microVM isolation and 150ms startup times.
Key strengths:
- Built for agents: SDK designed for agent workflows
- Fast cold starts: 150ms to running code
- Strong security: Firecracker microVMs provide kernel-level isolation
- Developer experience: Clean API, good documentation
Limitations:
- 24-hour session limit: Long-running tasks require checkpointing
- No GPU support: Not suitable for ML inference workloads
- No BYOC option: Runs only on E2B infrastructure
Best for: AI agent developers who want a polished sandbox product without infrastructure management.
Pricing: Free tier available. Paid plans start at usage-based billing.
3. Modal
Modal provides a serverless platform built for machine learning and data workloads. Recently added Python sandbox support for AI code execution.
Key strengths:
- GPU support: A100 and H100 access for ML/AI workloads
- Autoscaling: Handle burst traffic automatically
- Python-first design: Built for data science and ML workflows
- Unlimited sessions: No time limits
Limitations:
- gVisor only: Weaker isolation than Firecracker (though Kata available as opt-in)
- No BYOC: Locked into Modal's infrastructure
- Python-centric: Limited Node.js support
Best for: ML teams running GPU-intensive agent workloads with Python.
Pricing: Free tier includes published pricing credits. Usage-based after that. Consider how this fits into your broader workflow and what matters most for your team. The right choice depends on your specific requirements: file types, team size, security needs, and how you collaborate with external partners. Testing with a free account is the fast way to know if a tool works for you.
4. Daytona
Daytona focuses on fast sandbox creation with sub-90ms cold starts from "code to execution." Supports image-based sandboxing with optional Kata Containers for enhanced isolation.
Key strengths:
- Fast: Sub-90ms cold start times
- Flexible isolation: Docker default, Kata or Sysbox for stronger security
- Developer environments: Built for development workflows
- Stateful sandboxes: Supports elastic, long-running sessions
Limitations:
- Docker by default: Weaker isolation unless explicitly configured
- Enterprise pricing: Not ideal for small teams or individual developers
Best for: Developer teams that prioritize speed and need fast iteration cycles.
Pricing: Contact sales for enterprise pricing. Consider how this fits into your broader workflow and what matters most for your team. The right choice depends on your specific requirements: file types, team size, security needs, and how you collaborate with external partners. Testing with a free account is the fast way to know if a tool works for you.
Run Code Execution Sandboxes For AI Agents workflows on Fast.io
Fast.io gives AI agents 50GB free storage with 251 MCP tools for file operations, built-in RAG, and ownership transfer. Complement your code sandbox with persistent workspace organization.
5. Vercel Sandbox
Vercel Sandbox (still in beta) provides ephemeral Firecracker microVMs with up to 45-minute execution times. Tightly integrated with the Vercel platform.
Key strengths:
- Vercel integration: Works smoothly with Vercel's edge network
- Strong isolation: Firecracker microVMs
- Node.js and Python support: Run both languages
- Free during beta: No cost while testing
Limitations:
- 45-minute limit: Short sessions compared to competitors
- Beta status: API may change, limited production support
- 8 vCPUs max: 2GB memory per vCPU
Best for: Teams using Vercel who want integrated sandbox execution for agent playgrounds and code demos.
Pricing: Beta (free). Production pricing not yet announced.
6. Cloudflare Sandboxes
Built on Cloudflare's global network, Cloudflare Sandboxes uses browser isolates to safely execute Python and Node.js code with strong security guarantees.
Key strengths:
- Global edge network: Run sandboxes close to users worldwide
- Browser isolates: Security model from Chrome isolation
- Sub-50ms starts: fast cold starts in this comparison
- Cloudflare integration: Works with Workers, R2, KV
Limitations:
- 30-minute limit: Shorter than most competitors
- Limited compute: Designed for lightweight tasks
- Cloudflare ecosystem: Best when using other Cloudflare products
Best for: Edge-first applications that need global distribution and instant starts.
Pricing: Part of Cloudflare Workers pricing. published pricing + usage.
7. Blaxel
Blaxel provides perpetual sandbox environments with sub-25ms resume times from standby mode. Zero compute cost during standby with 1-second auto-shutdown.
Key strengths:
- Perpetual sandboxes: No session time limits
- Instant resume: 25ms from standby to execution
- Cost optimization: Zero cost during standby periods
- Fast auto-shutdown: 1-second idle detection
Limitations:
- Less transparent pricing: Contact sales model
- Smaller ecosystem: Fewer integrations than larger platforms
Best for: Applications with intermittent execution patterns that benefit from instant resume.
Pricing: Contact sales for custom pricing. Consider how this fits into your broader workflow and what matters most for your team. The right choice depends on your specific requirements: file types, team size, security needs, and how you collaborate with external partners. Testing with a free account is the fast way to know if a tool works for you.
8. Depot
Depot offers Docker-based remote agent sandboxes with an 8-hour execution limit. Designed for CI/CD workflows with persistent cache support.
Key strengths:
- CI/CD optimized: Build caching, layer sharing
- 8-hour sessions: Long enough for most build workflows
- Simple pricing: $10/seat/month flat rate
- Docker compatibility: Use standard Dockerfiles
Limitations:
- Docker only: Weaker isolation than microVMs
- 8-hour limit: Not suitable for perpetual workloads
- No GPU support: CPU-only execution
Best for: Teams running CI/CD pipelines with AI-generated code.
Pricing: $10/seat/month with usage pooling. Consider how this fits into your broader workflow and what matters most for your team. The right choice depends on your specific requirements: file types, team size, security needs, and how you collaborate with external partners. Testing with a free account is the fast way to know if a tool works for you.
9. Beam Cloud
Beam Cloud provides open-source alternatives to E2B with Docker-based sandboxing for code execution. Focuses on GPU workloads and ML applications.
Key strengths:
- Open source: Self-hostable option available
- GPU support: Good for ML inference and training
- Unlimited sessions: No artificial time limits
- Python and Node.js: Both languages supported
Limitations:
- Docker isolation: Weaker than microVMs
- Smaller community: Less mature than E2B or Modal
Best for: Teams that want open-source flexibility with GPU access.
Pricing: Usage-based. Free tier available. Cloud storage architecture matters more than most people realize. Sync-based platforms require local copies of every file, consuming disk space and creating version conflicts. Cloud-native platforms stream files on demand, so your team accesses what they need without downloading entire folder trees.
10. Fast.io
Fast.io provides file storage and collaboration for AI agents with 251 MCP tools for file operations. While not a traditional code execution sandbox, it's designed to give agents persistent storage and workspace organization.
Key strengths:
- Free 50GB storage: Generous free tier for AI agents (no credit card)
- 251 MCP tools: Most comprehensive MCP server for file operations
- Persistent workspaces: Organize agent outputs by project
- Built-in RAG: Intelligence Mode auto-indexes files for semantic search
- Ownership transfer: Build for a client, transfer ownership, keep admin access
- Works with any LLM: Claude, GPT-4, Gemini, LLaMA, local models
Limitations:
- Not a code sandbox: Provides file storage, not execution isolation
- Requires external compute: Agents run code locally or in other sandboxes
Best for: AI agents that need persistent file storage to work with code outputs, data pipelines, or document processing workflows. Complements traditional sandboxes by providing organized storage for inputs and outputs. For example, an agent might execute Python code in E2B or Modal, then save the results to Fast.io workspaces for long-term organization, client delivery, or RAG indexing.
Pricing: Free tier with 50GB storage and 5,000 monthly credits (no credit card). Paid plans start at usage-based billing.
How We Evaluated These Sandboxes
We tested each platform across six key factors for AI agent code execution:
1. Isolation Strength
Security matters when running untrusted code. We ranked platforms by isolation technology:
- Firecracker/Kata microVMs: Kernel-level isolation (strongest)
- gVisor: User-space kernel protection (strong)
- Browser isolates: Chrome-based sandboxing (strong for specific workloads)
- Docker: Container isolation (weaker, but fast)
2. Session Duration
Long-running agent tasks need sandboxes that don't limit execution time. We noted maximum session lengths and whether platforms support unlimited sessions.
3. Cold Start Performance
Agent responsiveness matters. We measured time from API call to code execution across all platforms.
4. Language Support
Python dominates AI agent code, but Node.js is common for API integrations. We verified which platforms support both languages natively.
5. GPU Access
ML inference and training workloads require GPU support. We identified platforms with A100/H100 access for compute-intensive tasks.
6. Pricing Model
Cost structure impacts production viability. We compared free tiers, usage-based pricing, and seat-based models to help you estimate costs at scale.
Which Sandbox Should You Choose?
Match your sandbox to your specific use case:
For maximum security: Choose Northflank or E2B with Firecracker microVMs. Both provide kernel-level isolation that prevents malicious code from escaping the sandbox.
For GPU workloads: Use Modal or Beam Cloud. Both offer A100/H100 access optimized for ML inference and training.
For fast starts: Pick Cloudflare Sandboxes (sub-50ms) or Blaxel (25ms resume). Best when agent responsiveness matters.
For long-running sessions: Choose Northflank, Modal, or Daytona. All support unlimited session duration without time limits.
For edge distribution: Use Cloudflare Sandboxes to run code close to users globally with minimal latency.
For development workflows: Consider Depot if your agents generate code for CI/CD pipelines with build caching needs.
For agent file storage: Add Fast.io to organize sandbox outputs, enable RAG search, and transfer ownership to humans. The 50GB free tier and 251 MCP tools make it ideal for persistent agent workspaces. Most production systems combine multiple platforms. For example, execute code in E2B or Modal, store results in Fast.io workspaces, and use Cloudflare for global edge distribution.
State Persistence and Multi-Agent Coordination
Basic code execution isn't enough. Consider how agents maintain state and coordinate work:
Session checkpointing matters when sandboxes have time limits. E2B caps sessions at 24 hours, requiring intermediate state saves. Vercel Sandbox limits sessions to 45 minutes, so longer tasks need checkpointing. Platforms like Northflank and Modal with unlimited sessions reduce this burden.
File system persistence varies by platform. Some sandboxes provide ephemeral filesystems that reset on restart. Others maintain state between invocations. Check whether your agent's workflow requires persistent storage or can rebuild state from external sources.
Multi-agent coordination gets complex when multiple agents share sandboxes. File locks prevent conflicts when agents edit the same files at the same time. Fast.io provides file locks via its MCP server for safe concurrent access across multiple agent sessions.
Output management gets harder at scale. Where do sandbox results go? Many teams pipe outputs to object storage (S3, R2) or structured storage like Fast.io workspaces where humans can review agent work and trigger follow-up tasks.
Security Considerations for Production Deployments
Running agent-generated code in production requires defense in depth:
Network isolation prevents agents from making unauthorized external requests. Configure outbound firewall rules to whitelist only approved APIs and services. Cloudflare Sandboxes has built-in egress controls.
Resource limits stop runaway code from consuming all CPU or memory. Set per-sandbox caps for CPU cores, RAM, disk space, and execution time. Modal and Northflank give you detailed resource controls.
Audit logging tracks what code executed, when, and what resources it accessed. For compliance-sensitive industries, detailed logs are mandatory. Northflank and Fast.io both provide audit trails.
Secret management keeps API keys and credentials out of agent-generated code. Use environment variables or secret managers instead of hardcoding credentials. Never let agents read or write secrets directly.
Code review workflows add a human approval step before executing sensitive operations. Some teams require human review for any agent code that touches production databases or calls external APIs.
Frequently Asked Questions
Why do AI agents need code sandboxes?
AI agents generate and execute code for tasks like data analysis, API integration, and file manipulation. Without sandboxing, this creates serious security risks since agents might generate malicious or buggy code that could compromise your system. Sandboxed execution provides kernel-level or container-level isolation that prevents untrusted code from accessing sensitive data, modifying system files, or consuming unlimited resources. As agent-generated code grows more common, sandboxing becomes important for production deployments.
Is E2B free for developers?
E2B offers a free tier with limited usage for developers to test and prototype. The free tier includes a certain number of sandbox hours per month. For production workloads, E2B uses usage-based billing where you pay for compute time, memory, and storage consumed by your sandboxes. Check their pricing page for current free tier limits and paid plan details.
What's the difference between Firecracker and Docker for sandboxing?
Firecracker provides microVM isolation where each sandbox runs its own kernel, giving kernel-level security similar to running separate virtual machines. Docker uses container isolation where sandboxes share the host kernel with namespace separation. Firecracker offers stronger security isolation but adds some startup latency compared to containers. Docker starts faster but provides weaker security boundaries. For AI agent code execution, Firecracker is preferred when running untrusted code, while Docker works for controlled environments where code comes from trusted sources.
Can AI agents run GPU workloads in these sandboxes?
Only some platforms support GPU access in sandboxes. Modal and Beam Cloud offer A100 and H100 GPUs optimized for ML inference and training workloads. Northflank and Daytona also provide GPU support. However, E2B, Vercel Sandbox, Cloudflare Sandboxes, and Blaxel do not currently offer GPU-accelerated sandboxes. If your agents need to run ML models or perform GPU-intensive tasks, choose Modal, Beam Cloud, or Northflank.
How long can AI agent code run in a sandbox?
Session duration varies by platform. Cloudflare Sandboxes limits executions to 30 minutes. Vercel Sandbox caps sessions at 45 minutes. E2B allows up to 24 hours per session. Northflank, Modal, Daytona, and Beam Cloud support unlimited session duration where sandboxes persist until you terminate them. For long-running agent tasks like overnight data processing or continuous monitoring, choose a platform without time limits. For short-lived tasks like API calls or quick data transformations, shorter session limits are fine.
What happens to files created in a sandbox after execution completes?
File persistence depends on the platform. Some sandboxes provide ephemeral filesystems that delete all files when the session ends. Others offer persistent storage that survives across multiple executions. For important outputs, most teams copy files to external storage like S3, R2, or Fast.io workspaces before the sandbox terminates. Fast.io provides persistent workspaces where agents can organize outputs by project, enable RAG search, and transfer ownership to humans for review.
Can multiple AI agents share the same sandbox?
Most platforms allow multiple agents to access the same sandbox, but you need to handle concurrency carefully. Without coordination, agents might overwrite each other's files or conflict on shared resources. Use file locks to prevent concurrent writes to the same file. Fast.io provides file locks via its MCP server for safe multi-agent coordination. Alternatively, give each agent its own isolated sandbox and use external storage for shared state.
How much does it cost to run AI agents in production sandboxes?
Costs vary by platform and usage patterns. E2B, Modal, and Beam Cloud use usage-based pricing where you pay for compute time, memory, and storage consumed. Depot uses seat-based pricing with usage pooling. Cloudflare Sandboxes uses Workers pricing plus usage. Northflank and Daytona require custom enterprise pricing. For typical agent workloads, costs depend on execution frequency, session duration, and resource requirements. Free tiers from E2B and Modal help you estimate costs during development before committing to paid plans.
Consult legal counsel before deploying healthcare AI agents.
What's the best sandbox for agents using the Model Context Protocol (MCP)?
MCP-compatible agents benefit from sandboxes that integrate well with external tool servers. Fast.io provides 251 MCP tools for file operations via Streamable HTTP and SSE transport, making it ideal for agents that need persistent storage and workspace organization. For code execution, pair Fast.io with a compute sandbox like E2B, Modal, or Cloudflare Sandboxes. The agent executes code in the sandbox, stores results in Fast.io workspaces, and uses MCP tools to organize files, trigger workflows, or transfer ownership to humans.
Related Resources
Run Code Execution Sandboxes For AI Agents workflows on Fast.io
Fast.io gives AI agents 50GB free storage with 251 MCP tools for file operations, built-in RAG, and ownership transfer. Complement your code sandbox with persistent workspace organization.