This guide examines seven GPU-enabled sandbox platforms for AI agents in 2026, starting with Modal, a serverless compute platform purpose-built for AI workloads with native GPU support for Sandboxes spanning T4 through B200-class GPUs.
Key Takeaways
- GPU-enabled sandboxes unlock agent capabilities beyond CPU-bound tasks: AI agents increasingly need GPU acceleration for inference and compute-intensive analysis. Modal supports native GPU reservations for Sandboxes across a broad GPU lineup that runs from T4 through B200-class GPUs, while several alternatives now document some form of GPU-capable sandbox support
- Secure isolation protects against untrusted code execution: Coding agents generate and run code autonomously, making sandboxed execution critical. Modal uses gVisor containers, while alternatives offer various isolation models including Firecracker microVMs and Kata Containers
- Production-scale concurrency separates production platforms from prototyping tools: Modal Sandboxes are built for production concurrency and parallel agent workloads. Modal's Lovable case study describes 20,000 sandboxes running concurrently over a 48-hour event
- Cold start performance impacts agent responsiveness: Modal Sandboxes are engineered for fast cold starts, with an optimized filesystem that helps containers come online quickly. 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
- Unified platforms reduce operational complexity: Teams benefit from platforms that combine sandboxes, inference, training, and batch processing in a single unified platform rather than integrating multiple point solutions
1. Modal
Modal delivers serverless compute for secure code execution at scale, with native GPU support inside Sandboxes. The platform takes your code, containerizes it, and runs it in the cloud with automatic scaling, defined through Modal's code-first SDKs in Python, TypeScript, and Go for running Sandboxes, calling Modal Functions, and managing resources. Code running inside a Sandbox is not limited to one language and can use whatever runtime the workload requires.
Core Capabilities
- Native GPU support for Sandboxes: Modal supports native GPU reservations for Sandboxes, with supported GPU types including T4, L4, A10, L40S, A100 variants, RTX PRO 6000, H100, H200, and B200-class GPUs, enabling agents to run inference and compute-intensive analysis
- gVisor container isolation: Modal compute jobs are containerized and virtualized using gVisor for secure execution of AI-generated code
- Production-scale concurrency: Modal Sandboxes are built for production concurrency and parallel agent workloads; Modal's Lovable case study describes 20,000 sandboxes running concurrently over a 48-hour event
- Fast cold starts: Engineered for fast cold starts and faster feedback loops, with an optimized filesystem that helps containers come online quickly without letting large images slow startup down
- Memory Snapshots: Modal Memory Snapshots can reduce cold-start latency for initialization-heavy Functions, often by 3x to 10x. GPU Memory Snapshots are Alpha and capture GPU state, helping reduce startup time when initialization is dominated by work such as imports, JIT compilation, or GPU kernel compilation
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 gVisor 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:
- Ramp runs its Inspect background coding agent on Modal Sandboxes; Modal's Series C post says Inspect authored 70% of merged PRs
- Suno brought its music-generation model to market four months early by using Modal instead of spending that time building infrastructure
- Modal says it powers cloud infrastructure for 10,000+ teams; Modal-published customer stories include Lovable and Applied Compute
What Makes Modal Unique
- Native GPU support for Sandboxes: Modal provides native GPU reservations inside isolated Sandboxes; several alternatives now document some form of GPU-capable sandbox support, but Modal pairs GPU Sandboxes with an integrated serverless AI platform
- Unified AI platform: Combines Sandboxes, inference, training, batch processing, and notebooks in one platform, reducing multi-vendor integration overhead
- Code-first developer experience: Modal offers code-first SDKs for defining applications and Functions in Python, TypeScript, and Go, with decorator-based function deployment and minimal infrastructure configuration; Sandboxes themselves can run code in any language the workload requires
Best For: Teams building AI agents that need GPU-accelerated Sandboxes for inference and compute-intensive analysis, who can also combine Sandboxes with Modal's broader platform for training and fine-tuning, especially those seeking production-grade infrastructure at scale.
2. Northflank
Northflank provides a full-stack platform with sandbox capabilities, offering multiple isolation options and self-serve BYOC deployment across major cloud providers.
Core Capabilities
- Multiple isolation options: Support for Kata Containers, Firecracker, and gVisor isolation models
- Self-serve BYOC: Deploy into AWS, GCP, Azure, Oracle, or on-premises infrastructure without enterprise sales calls
- GPU support: Available for ML workloads alongside the broader PaaS offering
- No session limits: Sandboxes can run for unlimited duration without forced timeouts
- SOC 2 Type 2 certified: Enterprise-grade compliance for regulated workloads
Architecture Approach
Northflank operates as a broader PaaS platform rather than a sandbox-focused solution. This approach benefits teams that need APIs, databases, workers, and sandboxes unified in a single platform with data residency controls.
Best For: Teams with strict data sovereignty requirements who need BYOC deployment options, or those seeking a full-stack platform that includes sandbox capabilities alongside other infrastructure services.
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. Together's official documentation describes configurable sandboxed development environments, not GPU allocation inside the sandbox itself. Together's broader inference platform is GPU-backed, but that is separate from sandbox-level GPU support.
Core Capabilities
- Configurable VM sandboxes: Fully configurable development environments where agents can run code, install dependencies, and run servers inside a sandboxed VM
- Template-based startup: Sandboxes can be spun up from a template
- CodeSandbox integration: Together acquired CodeSandbox to expand sandbox capabilities
- Inference integration: Sandboxes work alongside Together's separate inference platform for end-to-end AI workflows
- Snapshotting support: State preservation for environments that need continuity across sessions
Use Case Focus
Together Code Sandbox is geared toward building and scaling AI coding tools that need isolated development environments. The platform combines sandbox execution with Together's inference offerings for integrated AI workflows, though GPU acceleration sits in the inference layer rather than inside the sandbox.
Best For: Teams already using Together.ai for inference who want integrated, VM-based sandbox capabilities, or those building AI coding tools that need configurable sandbox VMs with stateful development environments.
4. Beam.cloud
Beam.cloud is an open-source serverless GPU platform with self-hosting capabilities, offering teams full control over their sandbox infrastructure.
Core Capabilities
- Open source (AGPL-3.0): Full self-hosting capability with the same API as the managed offering
- GPU support in self-hosted deployments: Run GPU sandboxes on your own hardware without vendor lock-in
- gVisor and runc isolation: Multiple isolation options for different security requirements
- beta9 runtime: Open-source runtime for serverless GPU workloads
- Unlimited session duration: No forced timeouts for long-running workloads
Architecture Approach
Beam.cloud prioritizes infrastructure control and self-hosting. Teams can run the platform on their own hardware, avoiding vendor lock-in while maintaining GPU access for agent workloads. Beam supports sandbox cold boots and serverless GPU cold starts.
Best For: Teams with self-hosting requirements who need GPU support without vendor lock-in, or those preferring open-source platforms they can audit and modify.
5. Daytona
Daytona provides persistent development environments with sandbox creation and configurable runtime persistence.
Core Capabilities
- Cold starts: Daytona supports sandbox creation and cold starts
- Unlimited session duration: Sandboxes can be configured for indefinite runtime with state preservation
- Multi-language SDKs: Support for Python, TypeScript, Go, and Ruby
- Open-source option: Daytona offers an AGPL-licensed self-hosted option, with active core development maintained in a private codebase
- Recent funding: $24M Series A in February 2026 led by FirstMark Capital
Architecture Approach
Daytona focuses on persistent workspaces that maintain state across sessions. The platform pivoted to AI sandboxes in early 2025, bringing its development environment expertise to the agent infrastructure space. GPU support is available within its development-environment focus.
Best For: Teams prioritizing cold starts for CPU-bound workloads, or those needing persistent development environments with workspace continuity.
6. NVIDIA OpenShell
NVIDIA OpenShell is an open-source runtime for policy-controlled agent sandboxes with GPU request support, leveraging NVIDIA's expertise in accelerated computing infrastructure.
Core Capabilities
- NVIDIA GPU integration: Sandbox GPU requests are supported through a dedicated GPU request flag
- Container-based execution: Sandboxed environments for running AI workloads
- Developer tools integration: Compatibility with NVIDIA's broader AI development toolkit
- Maturity level: NVIDIA's GitHub repository describes the project as alpha software, with proof-of-life and single-player characteristics, and its Kubernetes deployment as experimental
Architecture Approach
OpenShell leverages NVIDIA's position as the leading GPU hardware provider to offer sandbox environments that can request NVIDIA accelerators. Teams already invested in the NVIDIA ecosystem may find integration advantages.
Best For: Teams experimenting with NVIDIA's ecosystem who want an open-source sandbox runtime with GPU request support.
7. Azure Sandboxes
Azure Container Apps Sandboxes (dynamic sessions) provide isolated, on-demand sandboxed execution environments within Microsoft's cloud infrastructure.
Core Capabilities
- Isolated execution environments: Microsoft documents Azure Container Apps Sandboxes / dynamic sessions as isolated, on-demand sandboxed compute
- Serverless GPU (documented separately): Azure Container Apps offers serverless GPU capabilities, which Microsoft documents separately from Sandboxes; GPU support for the Sandboxes themselves is not established in Microsoft's sandbox documentation
- Enterprise cloud integration: Connects with Azure's broader cloud services and compliance frameworks
- Managed infrastructure: Microsoft-operated sandbox environments
Architecture Approach
Azure Sandboxes operate within Microsoft's enterprise cloud ecosystem, offering isolated sandboxed execution for teams already using Azure infrastructure. The platform benefits from Azure's enterprise compliance certifications and global infrastructure footprint, while any GPU usage runs through Azure's separate serverless GPU capabilities rather than the Sandboxes themselves.
Best For: Teams with existing Azure infrastructure investments who want isolated sandboxed execution environments integrated with their cloud environment, noting that GPU support for Sandboxes is not documented by Microsoft.
Why Modal Stands Out for GPU-Enabled AI Agent Sandboxes
Native GPU Support Inside Sandboxes on an Integrated Platform
While many sandbox platforms focus on CPU-based code execution, Modal supports native GPU reservations for Sandboxes across a broad GPU lineup. Several alternatives now document some form of GPU-capable sandbox support, but Modal is differentiated by pairing GPU Sandboxes with an integrated serverless 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.
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, fast cold-start optimizations, Memory Snapshots, and filesystem optimizations designed for AI workloads such as GPU-accelerated computation and dynamic scaling that AI agents require.
Production-Scale Concurrency
Modal Sandboxes are built for production-scale concurrency and parallel agent workloads. Modal's Lovable case study describes generating 1 million code sandboxes, with 20,000 running concurrently over a 48-hour event. Concurrency models vary across providers: some publish default concurrency tiers, while others require custom limits or use infrastructure-dependent scaling.
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 offers code-first SDKs in Python, TypeScript, and Go for running Sandboxes, calling Functions, and managing resources, while Sandboxes can run code in any language the workload requires, so teams can build without managing multiple vendors. 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 gVisor isolation, TLS 1.3 for public APIs, and encryption in transit and at rest, Modal supports the compliance posture that many enterprise AI agent deployments require.
For teams building AI agents that require GPU-accelerated Sandboxes, production-grade reliability, and unified infrastructure, Modal's combination of native GPU support for Sandboxes, production-scale concurrency, and an integrated AI platform makes it a strong choice.
