Top AI sandbox platforms in 2026, ranked

An AI sandbox platform is infrastructure designed to safely execute code produced by AI systems, whether from LLM-powered coding assistants, autonomous agents, or code generation APIs. These sandbox products isolate untrusted code execution from your production environment, preventing AI-generated code from accessing secrets, consuming excessive resources, or compromising your infrastructure.

The core function of any AI sandbox runner is containment. When an AI agent generates Python to analyze data, JavaScript to render a visualization, or shell commands to install dependencies, that code runs inside an isolated environment with strict boundaries. If the code behaves maliciously or unexpectedly, the blast radius is limited to that single sandbox.

Traditional sandboxing has existed for decades, but AI sandbox products address challenges specific to LLM-generated code. AI outputs can contain bugs, hallucinations, or prompt-injected instructions, and unlike human-written code, they often execute immediately without review. An AI code sandbox platform assumes all code is potentially dangerous.

Scale compounds the challenge. AI applications spawn thousands of concurrent sessions, and sandbox runners must provision and tear down environments in milliseconds. When building AI products for multiple users, each execution must be completely isolated, a core requirement for any AI sandbox platform.

When comparing AI sandbox products and code execution platforms, evaluate these factors:

The security of an AI sandbox runner depends on its isolation method:

• Standard containers (Docker) share the host kernel. Fast but weaker isolation, kernel exploits can escape the sandbox.
• gVisor intercepts system calls in user space, reducing kernel attack surface. Used by Modal and available on Northflank.
• MicroVMs (Firecracker, Kata Containers, Cloud Hypervisor) provide dedicated kernels per workload. Strongest isolation for AI code execution platforms. Used by Northflank, E2B, and Vercel.

For truly untrusted AI-generated code, sandbox platforms with microVM isolation provide the strongest security guarantees.

How fast can the AI sandbox product provision new environments? Cold start times range from sub-90ms (Daytona) to several seconds. For responsive AI agents, faster sandbox runners keep interactions fluid.

Many AI sandbox platforms impose strict time limits:

• Vercel Sandbox: 45 minutes to 5 hours
• E2B: 24 hours maximum
• Northflank: Unlimited

For AI agents maintaining state across extended user interactions, session limits force complex workarounds. The best AI code sandbox platforms offer flexible or unlimited durations.

Can you run any container image, or must you use SDK-defined environments? Some sandbox products lock you into specific languages or image formats. Platforms accepting any OCI image provide maximum flexibility for diverse AI workloads.

Where can the AI sandbox platform deploy?

• Managed only: The vendor controls all infrastructure (Modal, Daytona, Vercel, Together)
• BYOC (Bring Your Own Cloud): Run in your AWS, GCP, or Azure account while the vendor manages orchestration (Northflank)
• Self-hosted: You operate everything, including the control plane (E2B experimental)

For regulated industries or data-sensitive AI applications, BYOC capability in an AI sandbox product is often mandatory.

Is the sandbox runner a standalone tool, or part of a broader platform? Sandbox-only products require you to stitch together separate solutions for databases, APIs, GPU workloads, and CI/CD. Complete AI sandbox platforms provide unified infrastructure.

Northflank ranks as the top AI sandbox platform for teams requiring production-grade isolation, infrastructure flexibility, and capabilities beyond ephemeral code execution.

Operating since 2019, Northflank processes over 2 million isolated workloads monthly. The engineering team actively contributes to open-source projects powering the platform: Kata Containers, QEMU, containerd, and Cloud Hypervisor.

Any OCI container image: Unlike AI sandbox runners requiring proprietary formats or SDK-defined images, Northflank accepts any container from Docker Hub, GitHub Container Registry, or private registries. Existing images work without modification.

Unlimited sessions: While competing sandbox platforms cap sessions at 24 hours or less, Northflank environments persist indefinitely. Essential for AI agents maintaining state across days or weeks of user interactions.

Production-ready BYOC: Deploy the AI sandbox platform in your AWS, GCP, Azure, or bare-metal infrastructure. Northflank handles orchestration while your data stays in your VPC. No other major AI code execution platform offers mature bring-your-own-cloud.

Complete infrastructure: Beyond sandboxed code execution, Northflank runs databases, backend APIs, scheduled jobs, and GPU workloads, all with consistent security. As AI applications grow beyond simple sandbox runners, infrastructure scales accordingly.

Enterprise proven: Companies including Sentry and governments run multi-tenant AI deployments on Northflank. When cto.new launched to 30,000+ users, Northflank's sandbox platform handled thousands of daily code executions without issues.

Transparent usage-based pricing:

• CPU: $0.01667/vCPU-hour
• RAM: $0.00833/GB-hour
• GPU (H100): $2.74/hour all-inclusive

Northflank's GPU pricing includes CPU and RAM, approximately 62% cheaper than comparable AI sandbox products charging separately.

Plus, of course, you get the whole platform included.

Teams seeking the top AI sandbox platform with enterprise-grade isolation, BYOC deployment, and unified infrastructure for complete AI applications.

E2B built its sandbox platform specifically for AI agent developers, offering polished Python and JavaScript SDKs for programmatic code execution.

• Firecracker microVM isolation: Each sandbox in this AI code execution product runs in a dedicated lightweight VM
• 150ms cold starts: Fast environment provisioning for responsive AI sandbox runners
• Session persistence: Pause and resume sandboxes from saved state

• 24-hour session cap: Even Pro plans limit this sandbox product to day-long sessions
• Self-hosting complexity: Scaling the AI sandbox platform past hundreds of concurrent environments requires operating E2B's control plane
• No network policies: Lacks granular egress controls for AI code execution
• Docker image requirements: Custom environments require building and pushing images

• Hobby: Free with $100 credit, 1-hour sessions, 20 concurrent sandboxes
• Pro: $150/month, 24-hour sessions, configurable resources
• Usage: ~$0.05/hour per 1 vCPU sandbox

Modal provides a serverless compute platform optimized for machine learning, with AI sandbox capabilities integrated into a broader Python-centric infrastructure.

• Massive autoscaling: This sandbox runner scales from zero to 20,000+ concurrent containers with sub-second cold starts
• Python-first experience: Define AI sandbox environments in Python code
• Built-in networking: Tunneling and egress policies for code execution platform connectivity
• Snapshot primitives: Save and restore sandbox state efficiently
• GPU access: Full range of NVIDIA GPUs for ML workloads

• No BYOC: This AI sandbox platform offers managed deployment only, no option to run in your cloud
• SDK-defined images: Cannot bring arbitrary OCI containers to this sandbox product
• Python-centric: JavaScript and Go SDKs exist but the code execution platform optimizes for Python
• gVisor only: No microVM option for stronger isolation in this AI sandbox runner

• CPU: $0.047/vCPU-hour
• RAM: $0.008/GB-hour
• H100 GPU: $3.95/hour (plus CPU and RAM charges)
• $30/month free credits

Daytona pivoted in early 2025 from development environments to AI agent infrastructure, positioning as the fastest sandbox product for code execution.

• Sub-90ms cold starts: The fastest AI sandbox runner available—critical for high-volume agent workflows
• Docker compatibility: Standard container workflows function without proprietary formats on this sandbox platform
• Stateful execution: Filesystem, environment variables, and process memory persist across interactions

• Docker isolation default: This AI sandbox product uses standard containers by default—weaker than microVMs. Kata Containers available but not default.
• Maturing platform: Feature parity with established sandbox platforms still developing
• Limited networking: No first-class tunneling or egress policies in this code execution platform
• Sandbox-only scope: No broader infrastructure for databases, APIs, or GPUs beyond the AI sandbox runner

• $200 free compute credit
• Pay-per-use after credits
• Startup program: up to $50k credits

Together AI extended their GPU cloud with sandbox platform capabilities, providing integrated code execution for teams already using Together's inference infrastructure.

• 500ms snapshot resume: This AI sandbox product resumes VMs from snapshot with memory pre-loaded
• Hot-swappable sizing: Scale from 2 to 64 vCPUs dynamically on this code execution platform
• Together AI integration: Seamless connection between model inference and sandbox execution

• Slower cold starts: 2.7 seconds for fresh sandbox creation versus sub-second competitors
• VM-style pricing: Per vCPU and GB-RAM billing less attractive for bursty AI code execution
• No tunneling: Lacks network tunneling features found in other sandbox platforms
• Dev container format: Must use Docker-based dev container images for this AI sandbox runner

• ~$0.089/vCPU-hour
• Billed per vCPU and GB-RAM per minute

Vercel launched their sandbox platform in beta, offering Firecracker-based isolation tightly coupled with Vercel's deployment infrastructure.

• Firecracker microVMs: True VM-level isolation for this AI code execution product
• Vercel integration: Seamless experience for teams using Vercel's platform
• Active CPU billing: Charges only when code actively executes in the sandbox runner

• Strict time limits: 45 minutes (Hobby) to 5 hours (Pro/Enterprise) maximum for this AI sandbox platform
• Limited runtimes: Only Node.js and Python supported in this sandbox product
• Single region: Only iad1 available for this AI code execution platform
• Vercel dependency: Designed for Vercel ecosystem—limited standalone utility as a sandbox runner
• Beta status: Production readiness timeline unclear for this AI sandbox product

• Hobby: 5 CPU hours, 420 GB-hours memory, 5,000 sandbox creations free
• Pro: $0.128/CPU-hour, $0.0106/GB-hour memory, $0.60/million creations

Choose Northflank if:

• You need the strongest isolation options (microVM + gVisor) in an AI sandbox platform
• Sessions must persist longer than 24 hours
• BYOC deployment is required for compliance or data residency
• You want unified infrastructure beyond just sandbox runners, databases, APIs, GPUs included

Choose E2B if:

• SDK quality is your top priority for AI code execution
• 24-hour sessions are sufficient
• You prefer open-source foundations in your sandbox product

Choose Modal if:

• Your team is Python-focused for ML workloads
• You need massive autoscaling in an AI sandbox runner
• gVisor isolation meets your security requirements

Choose Daytona if:

• Cold start speed is the critical factor for your sandbox platform
• Docker-level isolation is acceptable for your AI code execution needs
• You're running high-volume, short-duration agent workflows

Choose Together if:

• You already use Together AI for model inference
• Integrated AI sandbox and inference simplifies your architecture

Choose Vercel if:

• You're deeply invested in Vercel's ecosystem
• Short session limits (under 5 hours) work for your sandbox product needs

To start using the leading AI sandbox platform:

1. Sign up at northflank.com
2. Create a project, select your region or connect your cloud account for BYOC
3. Deploy a service, choose any container image from any registry
4. Configure isolation, Northflank provisions microVM-backed infrastructure automatically

For enterprise requirements, schedule a demo with Northflank's engineering team to discuss custom AI sandbox platform configurations, compliance needs, or volume pricing.

An AI sandbox platform is infrastructure providing isolated environments for executing code generated by AI systems. These sandbox products prevent untrusted AI-generated code from accessing production resources, leaking data, or compromising host systems. The platform handles provisioning, isolation, networking, and teardown of code execution environments.

Sandbox platforms using microVMs (Firecracker, Kata Containers) provide stronger isolation than container-based solutions because each workload receives a dedicated kernel. Northflank offers both Kata Containers and gVisor, making it the most flexible AI sandbox platform for security requirements. E2B and Vercel also use Firecracker microVMs.

Many AI sandbox products impose time limits: Vercel caps at 5 hours, E2B at 24 hours. For AI agents maintaining state across extended interactions, these limits require complex state serialization. Northflank's sandbox platform offers unlimited sessions, avoiding this architectural overhead.

BYOC (Bring Your Own Cloud) means the sandbox platform vendor manages the control plane while provisioning resources in your cloud account, you get managed operations with data in your VPC. Self-hosting means operating everything yourself. Northflank offers production-ready BYOC; E2B's self-hosting remains experimental.

Pricing varies by workload pattern. For CPU-intensive AI code execution, Northflank ($0.01667/vCPU-hour) costs approximately 65% less than Modal ($0.047/vCPU-hour). For GPU workloads, Northflank's all-inclusive pricing ($2.74/hour for H100) runs approximately 62% cheaper than sandbox products billing GPU, CPU, and RAM separately.

Some AI sandbox products support GPU-accelerated code execution. Northflank offers NVIDIA H100, A100, and other GPUs with all-inclusive pricing. Modal also provides GPU access but charges separately for GPU, CPU, and RAM. Verify your sandbox platform supports required GPU types before committing.