Infrastructure
AI agents and autonomous systems are transforming how developers build software, but they need secure, scalable environments to execute code safely. This guide examines seven serverless sandbox platforms serving different AI code execution needs 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 on demand, and provide the compute resources your workloads require. Choosing the right serverless sandbox platform determines whether your AI systems can execute code securely at production scale. This guide examines seven serverless sandbox platforms serving different AI code execution needs in 2026, starting with Modal, a serverless compute platform built for secure execution with native CPU and GPU support.
Modal delivers serverless compute purpose-built for AI workloads, combining secure sandboxed execution with on-demand CPU and GPU compute. 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 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 gVisor-based sandboxing for compute isolation, TLS 1.3 for public APIs, and encryption for data in transit and at rest.
Modal powers cloud infrastructure for over 10,000 teams, including notable AI companies:
Best For: Teams building AI agents that need secure code execution at scale with CPU and/or GPU compute for ML inference, model fine-tuning, or compute-intensive analysis, especially those seeking production-grade infrastructure with proven enterprise scale.
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. E2B publicly highlights broad Fortune 100 adoption.
E2B highlights integration, with public case studies describing week-scale implementation timelines. Customers like Perplexity shipped advanced data analysis in 1 week using the platform.
E2B serves AI companies including Perplexity, Hugging Face, Groq, and Lindy, demonstrating adoption across the AI ecosystem.
Best For: Teams building AI agents focused on code execution and testing that want straightforward integration and hardware-level isolation through Firecracker microVMs.
Daytona provides persistent development environments along with sandbox creation. The platform offers both managed and self-hosted deployment options, with an open-source core for teams requiring infrastructure control.
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.
Daytona's open-source core gives teams visibility into the platform's architecture and the option to self-host for complete infrastructure control.
Best For: Teams building AI agents that require persistent development environments, cold starts, and prefer workspace continuity over ephemeral execution.
Vercel Sandbox is an isolated code execution environment built 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.
Vercel Sandbox is designed 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 workflows that involve repeated start-run-stop cycles or safe execution of generated code.
The platform integrates seamlessly with Vercel's broader platform, including edge functions, serverless functions, and the AI SDK, making it natural for teams already building on Vercel.
Best For: Teams building AI agents within the Vercel ecosystem that need isolated environments for code execution, especially when workloads are I/O-heavy and can benefit from active CPU billing.
Cloudflare Sandbox SDK runs untrusted code in isolated Linux containers, coordinated through Workers and Durable Objects, with a TypeScript-first SDK, positioning itself for secure code execution from Workers applications.
Cloudflare Sandbox is framed around secure code execution and programmable sandbox workflows integrated with Cloudflare's broader Workers ecosystem. The platform's documentation includes tutorials for AI code executors and coding agents.
Cloudflare Sandbox SDK is designed for secure code execution from Workers applications and inherits Cloudflare Containers' lifecycle, placement, and routing behavior.
Best For: Teams building AI agents that need code execution from Workers applications and prefer a TypeScript-first development model within the Cloudflare ecosystem.
AWS Bedrock AgentCore provides sandbox capabilities for AI agents within the broader AWS ecosystem, offering native integration with AWS services and compliance frameworks.
AgentCore is positioned for enterprises already invested in AWS infrastructure, providing a path to agent capabilities without introducing new vendors or procurement processes.
The platform benefits organizations with existing AWS commitments, allowing them to apply committed spend and leverage existing security and compliance configurations.
Best For: Large enterprises with significant AWS investments that need agent sandbox capabilities integrated with their existing cloud infrastructure and compliance posture.
Riza previously offered a hosted WASM-based Code Interpreter API, but the hosted service shut down on October 1, 2025.
Note: Riza's hosted Code Interpreter API shut down on October 1, 2025. New account signups were disabled on July 28, 2025. Riza directed users toward Daytona for continued cloud-based execution, while its self-hosted container continued only for users with existing offline license keys.
Best For: Existing licensed users running Riza's self-hosted container; new teams should consider active providers given the October 1, 2025 hosted API shutdown.
Modal offers one of the broadest published GPU catalogs for sandboxed AI workloads, while some competitors, including Daytona, also offer GPU sandboxes. Modal provides access to GPUs including T4, L4, A10, L40S, A100 variants, RTX PRO 6000, H100/H100!, H200, and B200/B200+, enabling AI agents to run ML inference, fine-tuning, and compute-intensive analysis within secure execution contexts.
Modal's architecture is specifically engineered for AI and machine learning workloads. The platform's AI-native container runtime, optimized filesystem, memory snapshotting, and multi-cloud capacity pool are built for the unique demands of AI execution, including fast cold starts and dynamic scaling that AI agents require.
Modal Sandboxes support 100k+ concurrent sandboxes, with fast scheduling and gVisor-based 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.
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 gVisor-based sandboxing, TLS 1.3 encryption, and comprehensive security practices provide the foundation for running sensitive AI workloads.
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.
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.
For teams building AI agents that require secure code execution, production-grade reliability, and on-demand CPU and GPU compute, Modal's combination of AI-native infrastructure, sandboxed execution at scale, and proven enterprise adoption makes it a clear choice.
Explore the Modal documentation to get started.
Explore the Modal Sandboxes documentation to get started.
View Sandboxes DocsA 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 with gVisor isolation 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 gVisor-based 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. When AI workloads need GPU acceleration for inference or training, they can access GPUs instantly, paying only for actual usage. 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 and supports HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement. Additional considerations include data encryption practices, isolation technologies, and vulnerability remediation SLAs.