Coding agents are reshaping how developers build and ship software. These AI-powered systems write code, execute tasks, and iterate autonomously, but they need robust infrastructure to run reliably at scale. In practice, most coding-agent infrastructure work is secure CPU-based execution of the code the agent writes, with GPUs called upon when specific workloads require acceleration. Choosing the right AI infrastructure platform determines whether your agents can execute code securely, scale without manual intervention, and tap into GPU acceleration when workloads require it. This guide examines seven infrastructure platforms serving different coding agent needs in 2026, starting with Modal, a serverless compute platform built for secure code execution at scale, and broad GPU support layered on top.
Key Takeaways
1. Modal
Modal delivers serverless compute for secure code execution at scale, the core sandbox workload for coding agents, with on-demand GPU access layered on top for workloads that require acceleration. The platform takes your code, containerizes it, and executes it in the cloud with automatic scaling, all defined through native Python/TypeScript/Go SDKs.
Core Capabilities
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.
Production-Proven Results
Modal powers production workloads for notable AI companies:
What Makes Modal Unique
Best For: Teams building coding agents that need secure code execution at scale, with on-demand GPU access when workloads call for ML inference, model fine-tuning, or compute-intensive analysis, especially those seeking production-grade infrastructure with proven enterprise scale.
2. E2B
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. E2B says it is used by 88% of Fortune 100 companies, though the methodology behind this figure is not publicly disclosed.
Core Capabilities
Use Case Focus
E2B excels at ephemeral code execution, spinning up isolated environments for agents to run generated code, then tearing them down. The platform supports up to 1,100 concurrent sandboxes on higher-tier plans.
Best For: Teams building coding agents focused on code execution and testing where GPU acceleration is not required, particularly those needing the possible sandbox cold starts.
3. Daytona
Daytona provides persistent development environments with sandbox creation times. The platform's open source GitHub repo had about 72.2k stars as of April 2026 and offers both GPU support and configurable runtime persistence.
Core Capabilities
Architecture Approach
Daytona focuses on persistent workspaces that maintain state across sessions. This approach benefits agents that need to preserve context, cached dependencies, or intermediate results without recreation overhead.
Best For: Teams building coding agents that require persistent development environments and prefer workspace continuity over ephemeral execution.
4. 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. Unlike browser-based prototyping tools, Blaxel is positioned around secure sandboxed compute runtimes for agents that need to run commands, manage files, and preserve execution state across sessions.
Core Capabilities
Architecture Approach
Blaxel emphasizes persistent state rather than purely ephemeral execution. Its documentation recommends treating sandboxes as persistent computers that retain shell history, installed dependencies, and context over time, which can benefit agents that need continuity across workflows instead of clean-room execution on every task.
Best For: Teams building coding agents that need persistent sandbox environments, resume times, and secure code execution with continuity across sessions.
5. Together Code Sandbox
Together Code Sandbox is a managed sandbox environment for AI-powered coding tools. It is positioned around secure, configurable VM-based development environments with startup times, snapshotting, and support for running untrusted code at scale. Together also offers a separate Code Interpreter product for sandboxed Python execution through an API.
Core Capabilities
Use Case Focus
Together Code Sandbox is geared toward building and scaling AI coding tools that need isolated development environments rather than lightweight browser prototyping. Together positions the product around secure code sandboxes for full-scale AI development environments and AI-powered coding workflows.
Best For: Teams building AI coding tools or coding agents that need configurable sandbox VMs, stateful development environments, and secure execution of untrusted code at scale.
6. Vercel Sandbox
Vercel Sandbox is an isolated code execution environment built for running untrusted code in temporary Linux microVMs. Vercel positions it for use cases like AI agents, code execution, testing, and development workflows where teams need a secure environment to run code without managing the underlying infrastructure.
Core Capabilities
Architecture Approach
Vercel Sandbox is best understood as an execution layer for secure, isolated code running rather than a full infrastructure platform for GPU-heavy AI workloads. Its fit is strongest for agent or developer workflows that involve repeated start-run-stop cycles, short-lived tasks, or safe execution of generated code.
Best For: Teams that need isolated environments for code execution, testing, or agent workflows, especially when the priority is secure ephemeral execution rather than GPU access or broader ML infrastructure.
7. Cloudflare Sandbox
Cloudflare Sandbox is a code execution environment exposed through the Sandbox SDK. Cloudflare positions it for running Python and Node.js workloads, executing commands, managing files, and supporting agent-style workflows through a TypeScript API, without requiring teams to manage infrastructure directly.
Core Capabilities
Use Case Focus
Cloudflare Sandbox is framed more around secure code execution and programmable sandbox workflows than around browser-based app building. Cloudflare's own tutorials include an AI code executor and an AI coding agent built with the OpenAI Agents SDK, which makes it a more relevant fit for a coding-agent infrastructure list than general-purpose vibe-coding tools.
Best For: Teams looking for isolated code execution, file handling, and agent-oriented workflows in a Cloudflare-native environment, particularly if they prefer a TypeScript-first development model.
Why Modal Stands Out for Coding Agent Infrastructure
Purpose-Built for 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 elastic infrastructure with fast cold-starts, sandboxed code execution, GPU-accelerated computation, and dynamic scaling that coding agents require.
Secure Sandboxed Execution
Most coding-agent sandbox work is CPU-based execution of the code the agent writes, and Modal's sandboxes are built to handle that workload at scale. The platform supports 50,000+ concurrent sessions with fast cold starts, gVisor isolation, and full observability, essential for coding agents that generate and execute untrusted code.
On-Demand GPU Access
On top of the CPU baseline, agents can call upon GPUs on demand when workloads require acceleration, a key differentiator for a sandbox platform. Modal supports a broad GPU lineup, from T4 and L4 through H100, H200, and B200/B200+, letting agents match compute to the task at hand, whether running lightweight code analysis models or large language models for code generation.
Developer Experience Without Compromise
Modal's native Python, TypeScript, and Go SDKs eliminate infrastructure configuration overhead. Teams define compute requirements, container images, and scaling behavior directly in code, no YAML or config files required. This approach enables the 95 deployments per day that Sync Labs achieves, iteration velocity that YAML-based platforms struggle to match.
Production-Proven Scale
Modal powers cloud infrastructure for over 10,000 teams, including AI companies like Ramp, Lovable, and Applied Compute. This production track record demonstrates the platform's ability to handle enterprise-scale coding agent workloads reliably.
Enterprise Security and Compliance
With SOC 2 Type II certification, HIPAA support via BAA, and comprehensive security practices including gVisor sandboxing and TLS 1.3, Modal meets the compliance requirements that enterprise coding agent deployments demand.
For teams building coding agents that require secure code execution, production-grade reliability, and on-demand CPU and GPU access, Modal's combination of AI-native infrastructure, sandboxed execution at scale, and proven enterprise scale makes it the clear choice.
Explore the Modal documentation to get started.
Explore the Modal documentation to get started.
