Best Serverless Sandboxes for AI Code Execution in 2026
AI agents and autonomous systems are transforming how developers build software, but they need secure, scalable environments to execute code safely. Whether you're building coding assistants, data analysis tools, or autonomous research agents, the infrastructure running AI-generated code must isolate untrusted execution, scale to large numbers of parallel executions, and give each task the CPU and memory it needs without overprovisioning. Choosing the right serverless sandbox platform determines whether your AI systems can execute code securely at production scale while keeping startup times consistent as concurrency grows. This guide examines seven serverless sandbox platforms serving different AI code execution needs 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, combining high concurrency, automatic CPU and memory bursting, and consistent fast starts under load, with on-demand GPU compute when agents need it. The platform takes your code, containerizes it, and executes it in the cloud with automatic scaling, all defined through native Python, TypeScript, and Go SDKs. Modal reports running 3 billion sandboxes as of October 2026.
Core Capabilities
Security and Compliance
Modal has successfully completed a SOC 2 Type 2 audit and supports HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement. The platform uses VM and gVisor sandboxing for compute isolation, TLS 1.3 for public APIs, and encryption for data in transit and at rest. Modal supports region-pinning.
Production-Proven Results
Modal powers cloud infrastructure for over 10,000 teams, including notable AI companies:
Why Teams Choose Modal for Serverless Sandboxes
2. E2B
E2B provides sandboxes for AI agents, centered on ephemeral code execution with Firecracker microVM isolation.
Capabilities
Considerations
Why Teams Choose E2B
E2B is a sandbox-only provider with an open-source core and bring-your-own-cloud options, and it reports running 1 billion sandboxes in total. Teams choose E2B when they want a dedicated sandbox point solution.
3. Daytona
Daytona provides isolated sandboxes and development environments built with an emphasis on persistence.
Capabilities
Considerations
Why Teams Choose Daytona
Daytona is a dedicated sandbox provider with persistence built in and quick starts at smaller scale. Teams choose it as a point solution for stateful coding agents whose workloads don't call for high concurrency.
4. Vercel Sandbox
Vercel Sandbox is an isolated code execution environment for running untrusted code in temporary Linux microVMs. The platform positions itself around the Vercel ecosystem with native integration to the Vercel AI SDK and Next.js.
Capabilities
Considerations
Why Teams Choose Vercel
Vercel Sandbox is one component of Vercel's wider cloud platform. Teams choose it when their applications already run on Vercel and active-CPU billing, without active-memory billing, fits their workloads.
5. Cloudflare Sandbox
Cloudflare Sandbox SDK runs untrusted code in isolated Linux containers, coordinated through Workers and Durable Objects, with a TypeScript-first SDK for code execution from Workers applications.
Capabilities
Considerations
Why Teams Choose Cloudflare
Cloudflare Sandbox is part of Cloudflare's developer platform. Teams choose it when their stack already runs on Cloudflare Workers and they want sandboxes wired into that architecture from the start.
6. AWS Bedrock AgentCore
AWS Bedrock AgentCore provides sandbox capabilities for AI agents within the broader AWS ecosystem, including a Code Interpreter that runs agent-generated code in a containerized sandbox environment.
Capabilities
Considerations
Why Teams Choose AWS Bedrock AgentCore
AgentCore is a set of agent services inside AWS, with sandboxed code execution as one piece. Teams choose it when they are already committed to AWS and want agent sandboxes procured, billed, and governed through their existing AWS accounts.
7. Riza.io
Riza previously offered a hosted WASM-based Code Interpreter API, but the hosted service shut down on October 1, 2025.
Capabilities
Considerations
Why Teams Choose Riza
Riza is a WebAssembly-based code execution tool whose hosted API is no longer available. Teams choose Riza only when they already run its self-hosted container under an existing offline license key.
Why Modal Stands Out for AI Code Execution
Massive Concurrent Scale
Modal has demonstrated creating 1M+ sandboxes in under a minute, exceeding 10,000 sandbox creations per second, supports up to 1M concurrent sandboxes, and regularly runs tens of thousands of concurrent customer sandboxes, with a custom-built scheduler and VM or gVisor isolation. This scale enables production AI systems that serve thousands of users simultaneously, from coding agents generating and executing code to data analysis pipelines processing millions of requests.
CPU and Memory Bursting
Serverless code execution is bursty by nature: an agent can sit idle for long stretches while it waits on a model response, then need several cores and gigabytes of memory the moment it runs a build, a data job, or a test suite. Under fixed-reservation pricing, every sandbox has to be sized for that spike, so utilization stays low and costs climb relative to the work performed. Modal's active-resourcing model works differently from the fixed-reservation billing most Sandbox providers use: sandboxes start from a configured baseline and automatically burst above it, so teams request small sandboxes for typical usage and burst during peaks. Charges accrue per second on the greater of requested and actual resources, and bursting also reduces the risk of out-of-memory failures when a task outgrows its baseline.
Purpose-Built for AI Workloads
Modal's architecture is specifically engineered for AI and machine learning workloads. The platform's AI-native container runtime, optimized filesystem, custom-built scheduler, memory snapshotting, and multi-cloud capacity pool are built for the unique demands of AI execution, including sub-second sandbox starts that stay consistent under heavy concurrent load, even with large, arbitrary custom images, and the dynamic scaling that AI agents require.
Code-First Developer Experience
Modal's native Python, TypeScript, and Go SDKs eliminate infrastructure configuration overhead. Teams define compute requirements, container images, and scaling behavior directly in code. This approach enables rapid iteration. Sync Labs achieves 95 deployments per day using Modal's code-first infrastructure.
GPU Sandboxes When Agents Need Them
Most AI code execution runs on CPU, but when agents need acceleration, Modal's GPU sandboxes support full sandbox functionality across its GPU catalog of 10+ types without reservations. Modal provides access to GPUs including T4, L4, A10, L40S, A100 variants, RTX PRO 6000, H100, H200, B200, and B300, enabling AI agents to run ML inference, fine-tuning, and compute-intensive analysis within secure execution contexts.
Unified AI Infrastructure
Beyond sandboxes, Modal provides a complete AI infrastructure platform. Sandboxes integrate with Modal's inference serving, model training, and batch processing capabilities, eliminating the need to stitch together multiple vendors for different AI workloads.
Enterprise-Grade Security and Compliance
Modal has successfully completed a SOC 2 Type 2 audit and can support HIPAA-compliant workloads on Enterprise plans via a BAA. The platform's VM and gVisor sandboxing, TLS 1.3 encryption, region-pinning, and comprehensive security practices provide the foundation for running sensitive AI workloads.
For teams building AI agents that need secure code execution at very high concurrency, automatic CPU and memory bursting, and startup times that hold steady under load, with GPU compute available when workloads call for it, Modal's combination of AI-native infrastructure, sandboxed execution at scale, and proven enterprise adoption makes it a clear choice.
A serverless sandbox is an isolated compute environment that executes code without requiring teams to manage underlying infrastructure. For AI applications, these sandboxes run AI-generated code securely, scaling automatically based on demand. Modal's sandboxes combine serverless scaling up to 1M concurrent sandboxes with VM and gVisor isolation, automatic CPU and memory bursting, and optional GPU access for ML workloads.
AI agents generate and execute code autonomously, often from untrusted sources or based on user input. Without proper isolation, malicious or buggy generated code could access sensitive data, affect other workloads, or compromise host systems. Modal uses VM and gVisor sandboxing to isolate compute jobs, while platforms like E2B employ Firecracker microVMs for hardware-level isolation.
Serverless platforms like Modal provide on-demand GPU access without requiring reservations or managing idle infrastructure. Modal's dedicated inference and training products serve and fine-tune models on that capacity, and when code running in a sandbox needs GPU acceleration, it can use any of 10+ GPU types, including B300, with per-second billing. Modal's GPU Memory Snapshots, currently an alpha feature, further optimize this by capturing initialized GPU state for faster cold starts on subsequent requests, subject to documented limitations.
Yes, Modal supports multi-node training workloads alongside sandboxed execution. The platform provides access to H100 and B200 clusters with high-speed networking for distributed training, enabling teams to use the same infrastructure for both secure code execution and large-scale model training.
For enterprise deployments, look for SOC 2 Type 2 as a baseline for security controls. Modal has successfully completed a SOC 2 Type 2 audit, supports HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement, and supports region-pinning. Additional considerations include data encryption practices, isolation technologies, and vulnerability remediation SLAs.
Modal uses active-resource billing. Each sandbox starts from the CPU and memory it requests, can burst above that baseline automatically, and is charged per second at whichever is higher: the requested amount or what it actually used. Teams can size sandboxes around everyday workloads and still reach peak capacity when a task spikes, and automatic bursting lowers the chance of a sandbox failing on an out-of-memory error. Most sandbox providers charge for a fixed reservation and cap usage at that reservation, so teams end up provisioning every sandbox for its largest possible task and paying for headroom that sits idle most of the time.
