OpenHands, the open-source AI coding agent framework, requires secure sandbox environments to execute AI-generated code safely and at scale. As these autonomous agents write, test, and iterate on code, the underlying infrastructure must provide robust isolation, fast startup times, and the flexibility to handle diverse workloads. Choosing the right code execution sandbox determines whether your agent deployment can run untrusted code securely, scale to thousands of concurrent sessions, and access GPU acceleration when ML workloads demand it. This guide examines seven sandbox platforms that could support OpenHands-style agent workloads in 2026, starting with Modal, a serverless AI infrastructure platform that combines secure sandboxed execution with GPU support and a complete ML platform.
Key Takeaways
- Secure isolation is non-negotiable for AI-generated code: OpenHands agents generate and execute code autonomously, making sandboxed execution critical. Modal uses gVisor containers, while E2B and Northflank employ Firecracker microVMs for hardware-level isolation
- GPU-enabled sandboxes unlock ML agent capabilities: Modal combines secure sandboxes with on-demand GPU access, enabling AI coding agents to run GPU-dependent ML inference and training workloads
- Cold start performance varies across platforms: Some providers focus on startup times and low-latency resume from standby, while Modal delivers fast cold starts with massive concurrency support and an optimized filesystem engineered to keep containers coming online quickly
- OpenHands integration maturity differs across platforms: Modal provides Docker-in-Sandboxes support for coding agents needing containerized development environments, E2B and Daytona have legacy V0 OpenHands runtime documentation, and Daytona also publishes its own OpenHands runtime guidance
- Unified ML platforms reduce operational complexity: Modal's integrated infrastructure for sandboxes, inference, training, and batch processing eliminates multi-vendor complexity that point solutions require
1. Modal
Modal delivers serverless AI infrastructure that combines secure sandboxed execution with GPU support and a complete ML platform. For AI coding-agent deployments, Modal provides gVisor-based Sandboxes for running AI-generated code alongside on-demand access to GPUs when agents need to execute ML workloads.
Core Capabilities
- gVisor-based isolation: Modal Sandboxes are built on gVisor, which provides strong isolation properties and has custom logic to prevent malicious system calls for running untrusted AI-generated code
- GPU-enabled sandboxes: Modal supports GPU-backed Sandboxes, offering NVIDIA GPU options including H100, A100, L4 and others within isolated environments
- Fast cold starts: Engineered for fast cold starts and faster feedback loops, with an optimized filesystem that helps containers come online quickly without letting large images slow startup down
- Massive concurrency: Support for 50,000+ concurrent sessions with fast cold starts
- Code-first SDK: Create Sandboxes with
modal.Sandbox.create(...) in Python, TypeScript, or Go; Modal Functions use decorators for deployment and compute configuration, all without YAML-heavy infrastructure configuration
OpenHands Integration
Modal provides Docker-in-Sandboxes support, intended for coding agents that need containerized development environments. Modal has upstreamed support into SWE-bench, a high-profile benchmark for testing coding agents, enabling the 500-task Verified benchmark to run in 7 minutes with a simple --modal flag and demonstrating high-throughput cloud execution for agent evaluation workloads.
Security and Compliance
Modal has successfully completed a SOC 2 Type 2 audit and is SOC 2 Type II compliant. Modal supports HIPAA-compliant workloads on Enterprise plans via a BAA. The platform uses gVisor-based sandboxing for compute isolation, TLS 1.3 for public APIs, and encryption for data in transit and at rest.
Unified ML Platform Advantage
Unlike point-solution sandbox providers, Modal integrates sandboxed code execution with model inference, training, and batch processing in a single platform. This architecture enables AI coding agents to seamlessly chain sandbox execution with GPU-accelerated ML workloads (writing code, executing it in isolation, then running inference or fine-tuning) all within the same infrastructure.
Best For: Teams deploying AI coding agents that need secure code execution combined with GPU access for ML workloads, especially those seeking a unified platform that eliminates multi-vendor complexity.
2. E2B
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. E2B has legacy OpenHands runtime documentation and third-party OpenHands runtime artifacts, and provides hardware-level isolation for running untrusted code.
Core Capabilities
- Firecracker microVMs: Hardware-level VM boundaries for isolating AI-generated code execution
- Supports cold starts: Supports sandbox boot times for quick agent iterations
- Multi-language SDKs: Support for Python and TypeScript/JavaScript integration patterns
- Template system: Reproducible sandbox environments using Dockerfile-based sandbox templates with versioning and pre-built configurations
OpenHands Integration
E2B has legacy OpenHands runtime documentation and third-party OpenHands runtime artifacts, with Dockerfile-based sandbox template support including a premade OpenHands sandbox template. The platform's clean SDK developer experience makes it straightforward to integrate with agent frameworks.
Use Case Focus
E2B excels at ephemeral code execution scenarios, spinning up isolated environments for agents to run generated code, then tearing them down. The platform supports varying levels of concurrent sandboxes depending on plan tier, with compliance materials available through its trust center for enterprise security requirements.
Best For: Teams building agent deployments focused on quick prototyping and code execution where SDK integration is prioritized and GPU acceleration is not required.
3. Northflank
Northflank provides a production-grade microVM sandbox platform with flexible isolation options. The platform handles 2M+ microVMs monthly and offers bring-your-own-cloud (BYOC) deployment for organizations with data residency requirements.
Core Capabilities
- Multiple isolation technologies: Choice of Kata Containers, Firecracker, or gVisor depending on workload security requirements
- GPU workload support: GPU workloads supported on services and jobs alongside its sandbox infrastructure
- Unlimited session times: No 24-hour cap on sandbox lifetime, supporting long-running agent workflows
- BYOC deployment: Self-serve bring-your-own-cloud option across AWS, GCP, Azure, Oracle, and Civo
OpenHands Integration
Northflank can be evaluated for OpenHands-style workloads via Docker-compatible or remote execution patterns. The platform's API and CLI-driven approach supports infrastructure-as-code workflows.
Architecture Approach
Northflank positions itself as a full production stack: sandboxes plus APIs, databases, and workers in one platform. Northflank supports sandbox boot times with strong isolation options and unlimited persistence.
Best For: Enterprise teams deploying AI coding agents that need flexible isolation options, BYOC deployment, or unlimited session times for long-running agent workflows.
4. Daytona
Daytona provides persistent development environments with sandbox startup times. The platform focuses on container-based workspaces that maintain state across sessions.
Core Capabilities
- Supports cold starts: Supports sandbox startup times for quick agent iterations
- GPU support: Available for ML workloads alongside persistent storage
- Multi-language SDKs: Support for Python, TypeScript, Ruby, Go, and Java integration patterns
- Self-hosted option: Open-source deployment available with enterprise features for larger teams
OpenHands Integration
Daytona publishes a dedicated OpenHands runtime guide for building agent deployments with Daytona sandboxes, and OpenHands has legacy V0 Daytona runtime documentation covering custom Daytona sandbox implementation for agent workloads.
Architecture Approach
Daytona's container-based approach focuses on persistent workspaces that maintain state across sessions. This benefits AI coding agents that need to preserve cached dependencies, intermediate results, or execution context without recreation overhead.
Best For: Teams deploying AI coding agents that prioritize cold starts and need persistent workspace continuity across agent sessions.
5. Fly.io Sprites
Fly.io Sprites provides stateful sandboxes with checkpoint/restore capabilities for persistent development environments. The platform focuses on sandboxes that maintain state and can be suspended and resumed efficiently.
Core Capabilities
- Checkpoint/restore: Persistent sandbox state with efficient suspend and resume cycles
- Persistent state and storage: Durable storage and checkpoint/restore that survives sandbox sleep cycles
- Idle billing optimization: Sleep after inactivity with billing only during active compute
- Full Linux environment: Any language and package installation in a complete Linux environment
Architecture Approach
Sprites emphasizes stateful sandbox patterns where execution context persists across sessions. Sandboxes can be suspended when idle and resumed quickly, supporting agent workflows that span multiple interactions without losing state.
Use Case Focus
Sprites fits agent deployments where agents need to maintain development environment state (installed packages, cached data, shell history) across extended workflows without paying for idle time between agent interactions.
Best For: Teams building agent deployments that require persistent sandbox state with efficient idle cost management, particularly for agents with sporadic usage patterns.
6. Cloudflare Workers (Sandbox)
Cloudflare Workers Sandbox provides code execution environments through a TypeScript SDK, leveraging Cloudflare's edge infrastructure for globally distributed execution.
Core Capabilities
- Isolated Linux container/VM-backed architecture: Cloudflare Sandbox runs each sandbox as an isolated Linux container with VM-backed isolation
- TypeScript-first SDK: Sandbox lifecycle management through TypeScript API for command execution, file operations, and terminal access
- Python and Node.js execution: Support for Python scripts and Node.js applications in sandboxed environments
- Global edge distribution: Access to Cloudflare's network spanning 330+ cities
Architecture Approach
Cloudflare Sandbox runs each sandbox as an isolated Linux container; state is maintained while the container is active, but when the container stops after inactivity, previous state is lost: files, processes, and shell state are deleted unless data is persisted externally. keepAlive can prevent idle shutdown for active sessions. Cloudflare's tutorials include AI code executor and coding agent examples built with the OpenAI Agents SDK.
Use Case Focus
The platform suits agent deployments where agents need edge-distributed execution or TypeScript-first development patterns.
Best For: Teams deploying AI coding agents in TypeScript-first environments or needing globally distributed code execution through Cloudflare's edge network.
7. Blaxel
Blaxel is a sandbox platform built specifically for AI agents, with a focus on persistent "agent computers" that stay on standby and resume quickly when needed.
Core Capabilities
- Perpetual standby: Sandboxes that remain on automatic standby rather than being torn down after each task
- Resume from standby: Supports resume from standby with memory/filesystem continuity; for durable long-term storage, Blaxel volumes provide persistent storage
- Firecracker microVM isolation: Hardware-level isolation for secure code execution
- Template support: Reusable sandbox templates for standardized agent environments
Architecture Approach
Blaxel emphasizes persistent state over ephemeral execution. Its documentation recommends treating sandboxes as persistent computers that retain shell history, installed dependencies, and context over time, benefiting AI coding agents that need continuity across workflows.
Use Case Focus
Blaxel's perpetual standby model supports 50,000+ concurrent sandboxes with zero compute cost during dormancy. This architecture fits agent deployments where agents have long idle periods between active sessions.
Best For: Teams deploying AI coding agents that need instant resume from standby, persistent state across sessions, and cost efficiency during extended idle periods.
Why Modal Stands Out for AI Coding Agent Deployments
A Strong Combination of GPU-Enabled Sandboxes and ML Infrastructure
Modal combines secure sandboxed execution with on-demand GPU access. For AI coding agents that need to run GPU-dependent workloads (model inference, code analysis with ML models, or fine-tuning), Modal provides H100, A100, L4, and other NVIDIA GPUs directly within isolated Sandbox environments. Modal's combination of secure Sandboxes, GPU access, and integrated ML infrastructure is a strong fit for agent workloads that need both code execution and ML compute.
Unified ML Infrastructure
Unlike point-solution providers, Modal integrates sandboxes with model inference, training, and batch processing in a single AI infrastructure platform. Agent deployments can chain sandbox code execution with GPU-accelerated ML workloads seamlessly, all through the same SDK, available in Python, TypeScript, and Go, without managing multiple vendors or complex integrations.
Production-Proven Scale
Modal powers cloud infrastructure for over 10,000 teams, including AI companies running production agent workloads at scale. Ramp uses Modal Sandboxes for background coding agents that generate code changes and write them back into commits and pull requests (see Ramp's engineering blog). Lovable uses Modal Sandboxes as preview environments for generated apps and websites. This scale demonstrates Modal's ability to support large-scale sandboxed code execution and agent workloads reliably, with SOC 2 Type II compliance and HIPAA support for regulated industries.
SWE-bench Integration
Modal has upstreamed support into SWE-bench, a high-profile benchmark for testing coding agents. Running the 500-task Verified benchmark takes just 7 minutes with Modal's --modal flag, enabling rapid iteration on agent development and evaluation.
Dynamic Batching for Agent Workloads
For compatible Modal Functions (especially GPU inference workloads), Modal's dynamic batching can deliver significant throughput improvements. In Modal's Whisper example, adding dynamic batching produced a 2.8x, or almost 3x, throughput increase. This translates to meaningful efficiency gains for agent deployments processing GPU inference requests.
Developer Experience
Modal's code-first SDK avoids YAML-heavy infrastructure configuration. Teams create Sandboxes with API calls such as modal.Sandbox.create(...) in Python, TypeScript, or Go; Modal Functions use decorators for deployment and compute configuration. This approach enables the fast iteration velocity that AI coding-agent development demands while maintaining production-grade reliability.
For teams deploying AI coding agents that need secure code execution combined with GPU access, ML platform integration, and enterprise-scale reliability, Modal's combination of AI-native infrastructure and unified platform makes it a strong choice.
Explore the Modal Sandboxes documentation to get started building OpenHands agent deployments.
View Sandboxes Docs