AI coding agents like Devin are transforming software development by autonomously writing, testing, and executing code. But these agents require secure, isolated environments to run AI-generated code safely at scale. A code execution sandbox provides an isolated execution environment, implemented with containers, gVisor, microVMs, VMs, or isolates, that limits access to host systems and other workloads. Choosing the right sandbox environment determines whether your AI coding agents can execute code securely, scale dynamically, and access GPU acceleration when complex workloads demand it. This guide examines seven code execution sandbox platforms for teams building AI coding agents similar to Devin in 2026, starting with Modal, a serverless compute platform built for secure code execution at massive scale.
Key Takeaways
- Secure isolation is non-negotiable for AI-generated code: Devin and similar coding agents execute code autonomously, making sandboxed environments critical. Modal uses gVisor-based containers with custom logic to prevent malicious system calls, while E2B employs Firecracker microVMs for hardware-level isolation
- Scale capacity varies dramatically across platforms: Modal supports 50,000+ concurrent sessions, while E2B's Pro plan includes 100 concurrent sandboxes with purchased concurrency available up to 1,100; Enterprise concurrency is custom. Choose based on your expected concurrency needs
- GPU access differentiates AI-native platforms: Modal provides extensive GPU support, including T4, L4, A10, L40S, A100, H100, H200, and B200 variants, for workloads requiring ML inference or model training alongside code execution. Several alternatives offer CPU-only sandboxes
- Code-first SDKs accelerate agent development: Modal's code-defined SDK, available in Python, TypeScript, and Go, eliminates YAML configuration, enabling faster iteration cycles for teams building AI coding tools
- Production-proven platforms reduce operational risk: Modal powers over 10,000 teams including major AI companies, demonstrating enterprise-scale reliability for agent infrastructure
1. Modal
Modal delivers serverless compute purpose-built for AI workloads, offering secure sandboxes that scale to tens of thousands of concurrent containers. The platform combines isolated code execution with on-demand GPU access, making it ideal for Devin and similar AI agents that need both safe code execution and ML acceleration.
Core Capabilities
- gVisor container isolation: Modal Sandboxes use gVisor-based isolation for secure execution of untrusted user or agent code, with custom logic to prevent malicious system calls
- Massive concurrent scale: Supports 50,000+ concurrent sessions with fast cold starts enabled by Modal's custom scheduler, AI-native container runtime, and support for filesystem and memory snapshotting, proven at production scale by companies like Lovable and Quora
- 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
- Code-first SDK: Define compute, storage, and networking in code with no YAML or infrastructure configuration required; Modal supports SDKs in Python, TypeScript, and Go
- Extensive GPU support: Access NVIDIA GPUs including T4, L4, A10, L40S, A100-40GB/80GB, RTX-PRO-6000, H100/H100!, H200, and B200/B200+ when agent workloads require ML inference or model fine-tuning
- Granular network controls: Configure sandbox networking with options to block all network access, set CIDR allowlists, or enable port forwarding
Security and Compliance
Modal maintains SOC 2 Type II certification and 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. Security practices include Rust-based runtime infrastructure, external penetration testing, and published vulnerability remediation severity timeframes.
Production-Proven Results
Modal powers production workloads for notable AI companies building agent infrastructure:
- Lovable scales to handle viral traffic spikes with Modal's autoscaling sandboxes
- Quora uses Modal Sandboxes to securely execute LLM-generated code in Poe, with sandbox creation throughput stress-tested to 1,000 sandboxes per second supporting thousands of simultaneous users
- Ramp built a full-context background coding agent on Modal's infrastructure
- Mistral AI and Harvey leverage Modal for AI-powered applications
What Makes Modal Unique
- Unified ML platform: Run inference, training, batch processing, and sandboxed code execution through a single SDK
- Sandbox snapshotting: Modal supports filesystem snapshots that reduce startup latency and persist indefinitely until deleted. Sandbox Memory Snapshots are available and subject to documented constraints
- AI-native container runtime: Custom-built infrastructure, including Modal's container runtime, filesystem, and scheduler, optimized for AI workloads
- Multi-cloud capacity pool: Modal pools GPU capacity across major cloud providers, providing access to the latest GPUs without quotas or reservations
Best For: Teams building AI coding agents like Devin that need secure code execution at massive scale, with on-demand GPU access for ML inference and model fine-tuning, especially those seeking production-grade infrastructure with proven enterprise reliability.
2. E2B
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. The platform reports that 88% of Fortune 100 companies have signed up. E2B has publicly cited customer usage at millions of sandboxes per month for individual customers, though a platform-wide weekly figure is not publicly verified.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation through lightweight virtual machines provides strong security boundaries for untrusted AI-generated code
- Multi-language SDKs: Python and JavaScript/TypeScript SDKs with a native OpenAI Agents SDK integration and cookbook examples for common agent frameworks and LLM providers
- Open-source components and BYOC: E2B has open-source components, and its Enterprise BYOC option deploys sandboxes into a customer's AWS or GCP environment for data sovereignty requirements
- Template system: Reproducible sandbox environments with Docker-based custom templates and versioning
Use Case Focus
E2B excels at ephemeral code execution, spinning up isolated environments for agents to run generated code, then tearing them down. E2B's Pro plan includes 100 concurrent sandboxes and allows purchased concurrency up to 1,100; Enterprise concurrency is custom. The platform supports cold starts.
Architecture Approach
E2B's Firecracker-based isolation provides hardware-level security boundaries. Each sandbox runs in its own microVM with dedicated kernel, making it well-suited for executing untrusted code from AI agents. The platform offers 24-hour maximum session durations on Pro plans.
Best For: Teams building AI coding agents focused on secure ephemeral code execution where GPU acceleration is not required, particularly those needing strong hardware-level isolation and clean multi-language SDK support.
3. Daytona
Daytona provides persistent development environments with sandbox creation. The platform's open source GitHub repository offers both self-hosted and managed options, with GPU support and configurable runtime persistence.
Core Capabilities
- Cold starts: Daytona supports cold starts for responsive agent execution
- Unlimited session duration: Sandboxes can run indefinitely without platform-imposed time limits, supporting long-running agent workflows
- Open-source foundation: Self-hosting available with full transparency for security audits
- Stateful environments: Persistent filesystem across stopped sessions with snapshot-based sandbox creation; memory state is cleared on stop
- GPU support: Available for ML workloads alongside persistent storage
Architecture Approach
Daytona supports Docker/OCI-compatible images and describes its sandboxes as dedicated, isolated environments with their own kernel, filesystem, network stack, vCPU, RAM, and disk. The platform focuses on persistent workspaces that maintain filesystem state across sessions, benefiting agents that need to preserve cached dependencies or intermediate results without recreation overhead.
Use Case Focus
Daytona positions itself for AI coding agents requiring workspace continuity. The LSP (Language Server Protocol) support enables code intelligence and autocomplete for coding agents, while desktop environment options support computer-use agents.
Best For: Teams building AI coding agents that require persistent development environments, cold starts, and prefer workspace continuity over ephemeral execution patterns.
4. Northflank
Northflank offers a comprehensive platform for AI agent sandboxes with flexible isolation options and self-serve bring-your-own-cloud (BYOC) deployment. Northflank says it processes 2M+ isolated workloads monthly with production use since 2019.
Core Capabilities
- Flexible isolation options: Northflank markets flexible isolation options including microVM-backed execution and support for multiple isolation technologies, including Kata Containers, Firecracker microVMs, and gVisor, on a per-workload basis
- Self-serve BYOC: Deploy to AWS, GCP, Azure, or on-premises infrastructure without requiring enterprise sales conversations
- Language-agnostic API: REST API and CLI support any programming language rather than SDK-specific integrations
- Any OCI image support: Use standard container images without modification, simplifying migration from existing workflows
- GPU support: Access L4 through H200 GPUs alongside sandbox workloads
Architecture Approach
Northflank provides a complete platform encompassing sandboxes, databases, APIs, and CI/CD pipelines in a unified control plane. The platform's SOC 2 Type 2 certification and unlimited session duration support enterprise compliance requirements.
Use Case Focus
Northflank excels for teams requiring deployment flexibility and data residency control. The BYOC model enables running sandboxes within your own VPC with per-workload network isolation, addressing compliance scenarios that managed-only platforms cannot serve.
Best For: Teams building AI coding agents that require self-serve BYOC deployment, flexible isolation options per workload, or need to run sandbox infrastructure within existing cloud accounts for compliance reasons.
5. Vercel Sandbox
Vercel Sandbox provides isolated code execution environments built on Firecracker microVMs, designed for running untrusted code in temporary Linux environments. The platform integrates natively with Vercel's broader developer ecosystem.
Core Capabilities
- Firecracker microVM isolation: Each sandbox runs in an on-demand Linux microVM with its own filesystem, network, and process space
- Ephemeral runtime model: Sandboxes are temporary by design, starting when needed and stopping after use
- Developer-friendly Linux access: Full Linux environment with sudo access, package managers, and standard command-line workflows
- State persistence options (Beta): Vercel offers beta persistent sandboxes that can save and restore filesystem state; standard sandboxes are ephemeral unless snapshots or persistent mode are used
- Snapshot support: Create and restore sandbox snapshots for reproducible environments
Architecture Approach
Vercel Sandbox operates as an execution layer for secure, isolated code running rather than a full infrastructure platform for GPU-heavy AI workloads. The platform supports cold starts with session limits varying by plan tier.
Use Case Focus
Vercel Sandbox fits teams already invested in the Vercel ecosystem building AI agents that need isolated environments for code execution and testing. The platform integrates with Node.js and Python SDKs for agent workflows.
Best For: Teams building AI coding agents within the Vercel ecosystem that need isolated ephemeral execution environments, especially when the priority is seamless integration with existing Vercel deployments rather than GPU access.
6. Cloudflare Sandboxes
Cloudflare Sandboxes provide isolated code execution through the Sandbox SDK, built on Cloudflare Workers, Durable Objects, and Containers, leveraging Cloudflare's global network for distributed sandbox execution. Dynamic Workers is a separate feature for runtime-created Workers. The platform supports Python and Node.js workloads with a TypeScript-first SDK.
Core Capabilities
- Global platform deployment: Cloudflare Sandboxes are deployed through Cloudflare's global platform, with placement and routing following Cloudflare Containers behavior
- Python and Node.js execution: Run scripts, applications, code compilation, and data-processing workloads in isolated environments
- TypeScript-first SDK: Manage sandbox lifecycle, command execution, file operations, and WebSocket connections through a TypeScript API
- Isolated Linux containers: Each sandbox has an isolated filesystem and runs in a dedicated container with state maintained while active
- Configurable persistence: Support for keepAlive settings and configurable sleep behavior for sandboxes that need to remain active
Architecture Approach
Cloudflare Sandboxes are built on the Workers platform alongside Durable Objects and Containers, bringing Cloudflare's global network capabilities to sandbox execution. The platform's tutorials include AI code executor and AI coding agent implementations, positioning it for agent-oriented workflows.
Use Case Focus
Cloudflare Sandboxes suit teams requiring globally distributed code execution with low latency. The platform benefits agents that need to execute code across Cloudflare's worldwide infrastructure.
Best For: Teams building AI coding agents that need globally distributed sandbox execution, particularly those already using Cloudflare's infrastructure or preferring a TypeScript-first development model.
7. Fly.io Sprites
Fly.io Sprites provide sandbox execution capabilities as part of the broader Fly.io platform, offering persistent, hardware-isolated Linux environments backed by microVM-style isolation across Fly.io's infrastructure.
Core Capabilities
- microVM-based sandboxes: Sprites provide persistent, hardware-isolated Linux environments backed by microVM-style isolation, with checkpointing and restore capabilities
- Fly.io-hosted deployment: Sprites run on Fly.io's infrastructure; Fly.io has a globally distributed platform, though multi-region Sprites placement is not separately documented as a Sprites-specific capability
- Platform integration: Sprites provide persistent filesystems, checkpoint/restore, proxying, and network-policy controls; Sprites use their own persistence model separate from Fly's standard persistent volumes
- CLI-driven management: Control sandbox lifecycle through Fly.io's command-line tooling
Architecture Approach
Fly.io Sprites are purpose-built persistent, hardware-isolated Linux environments and are not standard Fly containers. Each Sprite runs as a dedicated microVM with its own filesystem, supporting checkpointing and restore. The platform enables teams already using Fly.io to add sandbox capabilities without adopting a separate service.
Use Case Focus
Fly.io Sprites fit teams already invested in the Fly.io ecosystem that need to add sandboxed code execution for AI agents. The platform provides a straightforward path to sandbox capabilities within existing Fly.io deployments.
Best For: Teams already using Fly.io infrastructure that need to add sandbox execution capabilities for AI coding agents without migrating to a separate platform.
Why Modal Stands Out for Devin-like AI Coding Agents
Purpose-Built for AI Agent Workloads
Modal's architecture is specifically engineered for agentic and machine learning workloads. The platform's custom container runtime, scheduler, and file system are optimized for the unique demands of secure sandboxed execution with fast cold starts, dynamic scaling, and GPU acceleration that AI coding agents like Devin require.
Secure Sandboxed Execution at Massive Scale
AI coding agents generate and execute untrusted code autonomously, making isolation critical. Modal's sandboxes handle this workload with gVisor-based isolation with custom logic to prevent malicious system calls. The platform supports 50,000+ concurrent sessions with fast cold starts, essential for coding agents serving multiple users simultaneously.
On-Demand GPU Access When Agents Need It
Unlike CPU-only sandbox platforms, Modal provides extensive GPU support that agents can call upon when workloads require acceleration. Whether Devin needs to run code analysis models, execute ML inference, or fine-tune models as part of a workflow, Modal's GPU lineup, including T4, L4, A10, L40S, A100, H100, H200, and B200 variants, matches compute to the task at hand.
Developer Experience Without Compromise
The code-first SDK eliminates infrastructure configuration overhead. Teams define compute requirements, container images, and scaling behavior directly in code using decorators, with SDK support in Python, TypeScript, and Go. This approach enables rapid iteration cycles that YAML-based platforms struggle to match, critical for teams iterating quickly on AI agent capabilities.
Production-Proven Scale and Reliability
Modal powers cloud infrastructure for over 10,000 teams, including AI companies like Lovable, Quora, and Ramp building production coding agents. This track record demonstrates the platform's ability to handle enterprise-scale agent workloads reliably, from viral traffic spikes to sustained high-concurrency execution.
Enterprise Security and Compliance
With SOC 2 Type II certification, HIPAA support via BAA for Enterprise customers, and comprehensive security practices including gVisor sandboxing, TLS 1.3, and published vulnerability remediation severity timeframes, Modal meets the compliance requirements that enterprise AI agent deployments demand.
Unified Platform for the Full AI Lifecycle
Beyond sandboxes, Modal provides a comprehensive suite of AI infrastructure components. Run inference, training, and batch processing alongside sandboxed code execution through a single SDK, eliminating multi-vendor complexity for teams building sophisticated AI agents.
For teams building AI coding agents like Devin that require secure code execution, production-grade reliability, and on-demand GPU access, Modal's combination of AI-native infrastructure, sandboxed execution at scale, and proven enterprise reliability makes it the clear choice.
Explore the Modal documentation to get started with sandboxes for your AI coding agents.
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