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, run many Devin-style sessions in parallel without slowing down, and get the CPU and memory each task needs without reserving capacity for the peak. This guide examines seven code execution sandbox platforms for teams building AI coding agents similar to Devin in 2026, starting with Modal, a scalable, high-performance sandbox platform for executing AI-generated code at production scale.
Key Takeaways
1. Modal
Modal is a scalable, high-performance sandbox platform for executing AI-generated code at production scale, built for teams running fleets of autonomous coding agents. Its secure sandboxes pair high concurrency with automatic CPU and memory bursting and consistent fast starts under load. When Devin-style agents need ML acceleration, GPU access and inference are available on the same platform.
Core Capabilities
Security and Compliance
Modal maintains SOC 2 Type II certification and supports HIPAA-compliant workloads on Enterprise plans via a BAA. Workloads are isolated in VM and gVisor sandboxes, Modal's public APIs run over TLS 1.3, and data stays encrypted both in transit and at rest. Security practices include Rust-based runtime infrastructure, external penetration testing, and published vulnerability remediation severity timeframes. Modal supports region-pinning.
Production-Proven Results
Modal powers production workloads for notable AI companies building agent infrastructure:
Why Teams Choose Modal for Devin
2. E2B
E2B provides secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation.
Capabilities
Considerations
Why Teams Choose E2B
E2B is a purpose-built sandbox provider with an open-source core and bring-your-own-cloud deployment, and it has run 1 billion sandboxes in total. Teams building Devin-style agents choose E2B when they want a standalone sandbox point solution for short-lived execution.
3. Daytona
Daytona offers isolated sandboxes and development environments built with an emphasis on persistence, with GPU support and configurable runtime lifecycles.
Capabilities
Considerations
Why Teams Choose Daytona
Daytona is a purpose-built sandbox provider that ships with persistence and offers fast starts at modest scale. Teams choose it as a point solution when their Devin-style agents need a stateful workspace and they don't need support for high concurrency.
4. Northflank
Northflank is a cloud application platform that includes AI agent sandboxes, with microVM isolation and self-serve bring-your-own-cloud (BYOC) deployment.
Capabilities
Considerations
Why Teams Choose Northflank
Northflank is a general-purpose cloud platform, and sandboxes are one of its building blocks. Teams choose Northflank when they want the sandboxes behind a Devin-style agent deployed in their own cloud account, alongside the databases and services the rest of their product already uses.
5. Vercel Sandbox
Vercel Sandbox provides isolated code execution environments built on Firecracker microVMs, designed for running untrusted code in Linux environments as part of the Vercel platform.
Capabilities
Considerations
Why Teams Choose Vercel
Vercel Sandbox ships as one piece of Vercel's cloud platform for coding-agent builders. Teams choose it when their coding-agent product already runs on Vercel and they want active-CPU pricing without active-memory pricing.
6. Cloudflare Sandboxes
Cloudflare Sandboxes provide isolated code execution through the Sandbox SDK, built on Cloudflare Workers, Durable Objects, and Containers. Dynamic Workers is a separate feature for runtime-created Workers. The platform supports Python and Node.js workloads with a TypeScript-first SDK.
Capabilities
Considerations
Why Teams Choose Cloudflare
Cloudflare Sandboxes are part of Cloudflare's existing infrastructure. Teams choose them when their agent stack already runs on Cloudflare and they want sandboxes pre-integrated into that architecture.
7. Fly.io Sprites
Fly.io Sprites provide sandbox execution as part of the broader Fly.io platform, offering persistent, hardware-isolated Linux environments backed by microVM-style isolation.
Capabilities
Considerations
Why Teams Choose Fly.io Sprites
Fly.io Sprites are persistent sandboxes integrated into Fly.io's infrastructure. Teams choose Sprites when they already deploy on Fly.io and want sandboxes for their coding agents without adopting a separate service.
Why Modal Stands Out for Devin-like AI Coding Agents
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 VM or gVisor isolation. Modal has demonstrated creating 1M+ sandboxes in under a minute and supports up to 1M concurrent sandboxes, essential for coding agents serving multiple users simultaneously.
Production-Proven Scale and Reliability
Modal powers cloud infrastructure for over 10,000 teams, including AI companies like Cognition, Quora, and Ramp building production coding agents. Modal reports running 3 billion sandboxes as of October 2026 and regularly runs tens of thousands of concurrent customer sandboxes. Imbue orchestrates hundreds of isolated coding agents on the platform. This track record demonstrates the platform's ability to handle enterprise-scale agent workloads reliably, from sudden traffic spikes to sustained high-concurrency execution.
CPU and Memory Bursting
A single Devin-style task is uneven: the agent reads code and waits on model output for minutes, then needs a burst of CPU and memory to install packages, compile, or run a test suite. Under fixed-reservation pricing, every sandbox has to be sized for that heaviest moment, so utilization stays low and costs run high relative to the work completed. With Modal, each sandbox begins from the CPU and memory it requested and automatically bursts past that request during heavy steps, so teams can provision for typical usage instead of peak usage. Billing is per second on the greater of requested and actual resources, and bursting also reduces the risk of out-of-memory failures when a build outgrows its baseline.
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 keep sandbox starts sub-second and consistent under heavy concurrent load, and they support the dynamic scaling that AI coding agents like Devin require.
Code-First Development with Custom Images
The code-first SDK removes infrastructure configuration overhead. Teams define compute requirements, container images, and scaling behavior directly in code, with SDK support in Python, TypeScript, and Go. Large, custom container images that package a project's languages, dependencies, and tools still start quickly, which matters for teams iterating on AI agent capabilities.
On-Demand GPU Access When Agents Need It
Most coding-agent work runs on CPU sandboxes, but some steps benefit from acceleration. Modal's GPU sandboxes offer full sandbox functionality across its GPU lineup of 10+ types, including B300, B200, H200, H100, A100, L40S, A10, L4, and T4, without reservations. Whether Devin needs to run code analysis models, execute ML inference, or fine-tune models as part of a workflow, Modal matches compute to the task at hand.
Unified Platform for the Full AI Lifecycle
Beyond sandboxes, Modal provides a full 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.
Enterprise Security and Compliance
With SOC 2 Type II certification, HIPAA support via BAA for Enterprise customers, region-pinning, and security practices including VM and gVisor sandboxing, TLS 1.3, and published vulnerability remediation severity timeframes, Modal meets the compliance requirements that enterprise AI agent deployments demand.
For teams building AI coding agents like Devin that require secure code execution at high concurrency, automatic CPU and memory bursting, and consistent startup under load, with GPU access when workloads need it, 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.
Frequently Asked Questions
A code execution sandbox is an isolated environment where AI-generated code runs without access to host systems, other workloads, or sensitive data. For AI coding agents like Devin that generate and execute code autonomously, sandboxing prevents malicious or buggy generated code from causing damage. Modal's secure sandboxes support up to 1M concurrent sandboxes with full observability for monitoring agent behavior, using VM or gVisor isolation.
Modal isolates AI-generated code in VM sandboxes or gVisor sandboxes. The platform maintains SOC 2 Type II certification, uses TLS 1.3 for public APIs, encrypts data in transit and at rest, implements Rust-based runtime infrastructure for memory safety, and supports region-pinning. Sandbox networking controls enable teams to block all network access, configure CIDR allowlists, or enable specific port forwarding as needed.
Yes. Modal has demonstrated creating 1M+ sandboxes in under a minute, exceeding 10,000 sandbox creations per second, and supports up to 1M concurrent sandboxes with consistent sub-second starts under load. In production, Modal regularly runs tens of thousands of concurrent customer sandboxes, and Quora uses Modal Sandboxes to securely execute LLM-generated code in Poe. Modal's custom scheduler and AI-native container runtime are engineered specifically for this level of concurrent sandboxed execution.
Modal provides a unified AI infrastructure platform that includes model inference, model training with multi-node GPU cluster support, batch processing for large-scale parallel jobs, and collaborative notebooks with GPU acceleration. This enables teams to run sandboxed code execution alongside ML workloads through a single SDK (available in Python, TypeScript, and Go), eliminating multi-vendor complexity.
For model serving and fine-tuning, Modal's dedicated inference and training products let AI coding agents run ML models for code generation, analysis, and understanding at production speeds alongside sandboxed execution. Modal Sandboxes can also run GPU-backed workloads, with GPU support across 10+ types, including B300, B200, H200, H100, A100, L40S, A10, L4, and T4, without reservations. Modal also offers Memory Snapshots (alpha) that can reduce startup latency for sandboxes with initialization-heavy workloads, subject to documented constraints. This combination allows agents to execute generated code in secure sandboxes while calling upon GPU acceleration when workloads require ML inference or model fine-tuning.
Modal bills Sandboxes on active resources: each second is charged at whichever is greater, the CPU and memory a sandbox requested or what it actually consumed. When a coding agent's build or test run needs more than its request, the sandbox bursts above it automatically, which also cuts the chance of the task dying from an out-of-memory error. That lets teams set requests around an agent's typical footprint and still reach peak capacity during heavy steps. Most Sandbox providers charge for a fixed reservation and don't let a sandbox go beyond it, so teams size every sandbox for the heaviest step an agent might take and end up paying for capacity that stays idle for most of the session.
