AI agents are becoming increasingly sophisticated, capable of generating and executing code, running inference workloads, and performing compute-intensive analysis autonomously. To operate effectively at scale, these agents need sandboxes that provide secure isolation, high concurrency, consistent startup under load, and elastic access to accelerated compute when workloads call for it. Choosing the right sandbox infrastructure determines whether your agents can execute untrusted code safely, scale to large numbers of concurrent sessions without performance degradation, get the CPU and memory they need without overprovisioning, and tap into GPU acceleration when workloads demand it.
This guide examines seven GPU-enabled sandbox platforms for AI agents in 2026, starting with Modal, a scalable, high-performance sandbox platform for executing AI-generated code at production scale, with GPU support for Sandboxes across 10+ GPU types when agents need it.
Key Takeaways
1. Modal
Modal is a scalable, high-performance sandbox platform for executing AI-generated code at production scale, with GPU support inside Sandboxes when agents need it. Its sandboxes combine high concurrency, automatic CPU and memory bursting, and consistent sub-second starts under load. The platform takes your code, containerizes it, and runs it in the cloud with automatic scaling. Modal provides code-first SDKs in Python, TypeScript, and Go. Code running inside a Sandbox is not limited to one language and can use whatever runtime the workload requires.
Core Capabilities
Security and Compliance
Modal has completed a SOC 2 Type 2 audit and supports HIPAA-compliant workloads on Enterprise plans via a BAA. Modal uses VM and gVisor sandboxes 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 production workloads for notable AI companies:
Why Teams Choose Modal for GPU-Enabled AI Agent Sandboxes
2. Northflank
Northflank is a full-stack cloud platform with sandbox capabilities, offering multiple isolation options and self-serve BYOC deployment across major cloud providers.
Capabilities
Considerations
Why Teams Choose Northflank
Northflank is a broad cloud application platform that offers sandboxes as one of many services. Teams select it when they need sandboxes deployed in their own cloud account, next to the databases, APIs, and workers they already run there.
3. Together.ai Code Sandbox
Together.ai Code Sandbox is a managed VM-based sandbox environment for AI-powered coding tools, integrated with Together's inference ecosystem.
Capabilities
Considerations
Why Teams Choose Together.ai Code Sandbox
Together Code Sandbox is a sandbox offering that sits inside Together's AI inference platform. Teams choose it when they already run inference on Together and want CPU-based coding sandboxes under the same account.
4. Beam.cloud
Beam.cloud is an open-source serverless GPU platform with self-hosting capabilities, using gVisor and runc to isolate its sandboxes.
Capabilities
Considerations
Why Teams Choose Beam.cloud
Beam is an open-source serverless GPU platform that includes sandboxes. Teams choose Beam when they want to self-host their sandbox runtime on GPU hardware they operate themselves.
5. Daytona
Daytona offers isolated sandboxes and development environments centered on persistence, with a dedicated kernel, filesystem, and network stack for each sandbox.
Capabilities
Considerations
Why Teams Choose Daytona
Daytona is a purpose-built sandbox product where persistence comes standard and starts are quick at moderate volumes. It fits teams looking for a point solution for stateful agent environments that won't need to run at high concurrency.
6. NVIDIA OpenShell
NVIDIA OpenShell is an open-source (Apache 2.0) runtime for policy-controlled agent sandboxes with GPU request support, running sandboxes through container drivers or a MicroVM driver.
Capabilities
Considerations
Why Teams Choose NVIDIA OpenShell
OpenShell is a self-run sandbox runtime from NVIDIA rather than a managed sandbox service. Teams choose it when they want to operate policy-controlled agent sandboxes on NVIDIA GPU infrastructure they already manage.
7. Azure Sandboxes
Azure Container Apps Sandboxes (dynamic sessions) provide isolated, on-demand sandboxed execution environments within Microsoft's cloud infrastructure, using Hyper-V isolation.
Capabilities
Considerations
Why Teams Choose Azure Sandboxes
Azure dynamic sessions are part of Microsoft's Azure Container Apps service. Teams choose them when their workloads already run on Azure and they want sandboxed execution inside that same environment.
Why Modal Stands Out for GPU-Enabled AI Agent Sandboxes
Production-Scale Concurrency
Modal Sandboxes are built for production-scale concurrency and parallel agent workloads. Modal has created 1M+ sandboxes in under a minute at more than 10,000 creations per second, supports up to 1M concurrent sandboxes, and routinely runs tens of thousands of customer sandboxes at once. Concurrency models vary across providers: some publish default concurrency tiers, while others require custom limits or use infrastructure-dependent scaling.
CPU and Memory Bursting
AI agent workloads swing between extremes: an agent can idle for long stretches while it waits on a model response, then run a dependency install, a data-processing script, or a full test suite. Under fixed-reservation pricing, teams size each sandbox for the heaviest of those moments, so utilization stays low and costs climb relative to the work actually done. Modal's active-resourcing model works differently: sandboxes start from a requested CPU and memory baseline and burst above it automatically, with per-second billing on the greater of requested and actual resources. Teams can provision for typical usage rather than peak usage, and bursting also reduces the risk of out-of-memory failures when a task outgrows its baseline.
Purpose-Built for AI Agent Workloads
Modal's architecture is engineered for agentic and machine learning workloads. Modal's infrastructure includes an AI-native container runtime, a custom scheduler that keeps sandbox starts sub-second and consistent under heavy concurrent load, Memory Snapshots, and filesystem optimizations that keep large custom images from slowing startup, supporting the GPU-accelerated computation and dynamic scaling that AI agents require.
Native GPU Support Inside Sandboxes on an Integrated Platform
While many sandbox platforms focus on CPU-based code execution, Modal supports GPU-enabled Sandboxes across 10+ GPU types, including B300, without reservations and with full sandbox functionality. Most agent sandboxes run on CPU, and GPUs come in when a task calls for them. Several alternatives now document some form of GPU-capable sandbox support, though their GPU sandbox capabilities vary; Daytona's GPU sandboxes, for example, are ephemeral. Modal is differentiated by pairing feature-complete GPU Sandboxes with an integrated AI platform. This enables agents to run inference, image generation, and compute-intensive analysis within secure, sandboxed environments, and to combine Sandboxes with Modal's broader products for training and fine-tuning.
GPU Memory Snapshots for Faster Cold Starts
Modal's Memory Snapshots can reduce cold-start latency for initialization-heavy Functions, often by 3x to 10x. GPU Memory Snapshots are currently Alpha and capture GPU state, helping reduce startup time when initialization is dominated by work such as imports, JIT compilation, or GPU kernel compilation.
Unified Platform Reduces Operational Complexity
Modal combines Sandboxes, inference, training, batch processing, and notebooks in a single platform. Modal provides code-first SDKs in Python, TypeScript, and Go, and Sandboxes can run code in any language the workload requires, so teams can build without managing multiple vendors. Snorkel, for example, uses Modal to serve models and run evals in sandboxes. This unified approach supports the iteration velocity that Ramp achieves with its background coding agents.
Enterprise Security and Compliance
Modal has completed a SOC 2 Type 2 audit and supports HIPAA-compliant workloads on Enterprise plans via a BAA. Combined with VM and gVisor isolation, TLS 1.3 for public APIs, encryption in transit and at rest, and region-pinning, Modal supports the compliance posture that many enterprise AI agent deployments require.
For teams building AI agents that need sandboxed execution at high concurrency, automatic CPU and memory bursting, and consistent startup under load, with GPU-accelerated Sandboxes available when workloads call for them, Modal's combination of production-scale sandbox infrastructure and an integrated AI platform makes it the most complete fit among the platforms in this guide.
A GPU-enabled sandbox is an isolated execution environment that provides AI agents with access to GPU acceleration while maintaining security boundaries. These sandboxes allow agents to run inference and compute-intensive workloads within secure environments that prevent AI-generated code from affecting other workloads or accessing unauthorized resources. Model serving and fine-tuning run on Modal's dedicated inference and training products, while Modal's Sandboxes run on CPU by default and can attach GPUs from 10+ types, from T4 through B200 and B300, without reservations. GPU Sandboxes run on Modal's gVisor runtime with full sandbox functionality.
GPUs enable AI agents to run ML models for code generation, analysis, and understanding at production speeds. Many agent workloads, including LLM inference, image generation, and model fine-tuning, require GPU acceleration to achieve acceptable latency, while much of the code agents execute in a sandbox, such as scripts, builds, and tests, runs on CPU. Modal's dedicated inference and training products handle model serving and fine-tuning, and Modal Sandboxes can attach GPUs from 10+ types when agent-generated code needs acceleration. Modal's Memory Snapshots can reduce cold-start latency for initialization-heavy Functions, often by 3x to 10x, with GPU Memory Snapshots currently documented as Alpha.
Sandboxes isolate code in secure environments where it cannot access host systems, other workloads, or sensitive data. For AI agents that generate and run code autonomously, sandboxing prevents malicious or buggy generated code from causing damage. Modal uses VM and gVisor isolation for compute jobs, while alternatives employ Firecracker microVMs, Kata Containers, or Hyper-V for different security trade-offs.
Modal provides first-class products for inference, training, fine-tuning, and batch workloads, and separately supports GPU-enabled Sandboxes for isolated code execution on 10+ GPU types, including B300, without reservations. Modal's unified platform lets teams combine Sandboxes with Modal's broader GPU compute, using the same infrastructure across development and production workloads.
Key factors include concurrency (Modal supports up to 1M concurrent sandboxes and has created 1M+ sandboxes in under a minute), resource model (Modal bursts CPU and memory above requested levels instead of billing fixed reservations), startup consistency under load (Modal keeps starts sub-second at high concurrency, while cold-start performance varies by platform and workload), GPU availability (Modal supports a broad GPU lineup of 10+ types for Sandboxes without reservations), isolation model (VM, gVisor, Firecracker, Kata, or Hyper-V), compliance (SOC 2 Type 2, HIPAA), and whether you need self-hosting or BYOC deployment options.
Modal uses active-resource billing: each sandbox is charged per second for whichever is higher, the CPU and memory it requested or the CPU and memory it actually used. When a task needs more than its request, the sandbox bursts automatically instead of hitting a ceiling, which also cuts the chance of an out-of-memory failure. That lets teams request resources for typical usage and still reach peak usage on demand. Most sandbox providers charge for a fixed reservation with no headroom above it, so teams end up sizing every sandbox for its worst case and paying for capacity that goes unused most of the time.
