AI app builders need secure, scalable environments to execute code safely and iterate quickly. Whether you're building autonomous agents, running AI-generated code, or deploying inference workloads, the right sandbox platform determines how fast you can ship and how reliably your applications perform in production. Secure sandboxes provide the isolation necessary to run untrusted code while limiting infrastructure risk through secure-by-default networking, strong isolation, and restricted access to other workspace resources, while serverless architectures eliminate the overhead of managing capacity. This guide examines seven sandbox platforms serving different AI development needs in 2026, starting with Modal—a serverless compute platform that combines gVisor-isolated containers with comprehensive GPU support for AI workloads at scale.
Key Takeaways
- Secure isolation is non-negotiable for AI apps: Running AI-generated code requires robust sandboxing. Modal uses gVisor containers, while E2B and Vercel Sandbox employ Firecracker microVMs—all proven isolation technologies designed to provide strong isolation boundaries for untrusted workloads
- GPU access separates ML-focused platforms from code-only sandboxes: Modal offers one of the broadest GPU-backed sandbox and AI infrastructure stacks, spanning T4 through B200/B200+, enabling teams to run training, inference, and code execution on unified infrastructure
- Cold start performance varies across platforms: Blaxel supports fast resume from standby, Daytona offers fast sandbox creation, and E2B supports fast sandbox starts—choose based on your latency requirements
- Enterprise compliance requirements shape platform choice: Modal maintains SOC 2 Type II certification and supports HIPAA-compliant workloads on Enterprise plans via a BAA, meeting the security bar that regulated industries demand
1. Modal
Modal delivers serverless compute for AI app builders who need secure code execution, instant scaling, and on-demand GPU access—all defined through a code-first SDK with support for Python, Go, and JavaScript/TypeScript. The platform takes your code, containerizes it, and executes it in the cloud with automatic scaling to thousands of containers.
Core Capabilities
- gVisor container isolation: Modal Sandboxes are built on gVisor, which provides strong isolation properties and custom logic to prevent malicious system calls, limiting the blast radius of untrusted code to the Sandbox container
- 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. Modal supports filesystem snapshots and memory snapshotting capabilities for Sandboxes, with Sandbox Memory Snapshots available as an Alpha feature
- Code-first SDK: Define compute, storage, and networking via code—no YAML or Kubernetes configuration required. Modal offers SDKs for Python, Go, and JavaScript/TypeScript, and Sandboxes can run whatever runtime or language the workload requires
- Comprehensive GPU support: Modal offers one of the broadest GPU-backed sandbox and AI infrastructure stacks, with GPU access spanning T4, L4, A10, L40S, A100, A100-40GB, A100-80GB, RTX PRO 6000, H100, H200, and B200/B200+, enabling everything from lightweight inference to large-scale model training
- Massive concurrency: Supports 50,000+ concurrent sessions with full observability for monitoring sandbox behavior
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. Enterprise features include audit logs, Okta SSO, and region selection for Function execution.
Production-Proven Results
Modal powers cloud infrastructure for over 10,000 teams, including companies like Quora, Lovable, and Ramp. The platform's serverless architecture means teams pay for compute they use or request, without requiring pre-provisioned idle capacity, and its AI-native container runtime—including custom file system, scheduler, and image builder—is purpose-built for the demands of AI workloads.
Best For: Teams building AI apps that need secure code execution at scale with on-demand GPU access, particularly those seeking production-grade infrastructure with proven enterprise reliability.
2. E2B
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. The platform has started 500M+ sandboxes and reports adoption by 88% of Fortune 100 companies.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation for running untrusted AI-generated code with strong security boundaries
- Fast sandbox starts: E2B supports fast sandbox starts when the sandbox is in the same region as the client, enabling responsive agent interactions
- Multi-language SDKs: Support for Python and TypeScript integration patterns
- Code Interpreter Sandbox: Pre-built Jupyter environment for data analysis agents requiring zero configuration
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 100 concurrent sandboxes on Pro plans and offers BYOC deployment on AWS for Enterprise customers with data sovereignty requirements.
Best For: Teams building AI coding agents focused on code execution where GPU acceleration is not required, particularly those needing proven enterprise-scale reliability and fast sandbox starts.
3. Northflank
Northflank provides a full-stack AI platform with flexible sandbox environments, BYOC deployment across multiple clouds, and support for various isolation technologies. The platform says it runs over 2 million microVMs monthly and offers unlimited session duration.
Core Capabilities
- Multi-cloud BYOC: Self-serve deployment across AWS, GCP, Azure, Oracle, CoreWeave, and on-premises
- Flexible isolation: Supports Kata Containers, Firecracker, and gVisor per workload, letting teams match isolation technology to requirements
- Unlimited sessions: No platform-imposed time limits on sandbox duration
- Full-stack platform: Sandboxes alongside APIs, databases, and GPU inference in one control plane
- GPU support: Access to L4, A100, H100, and H200 GPUs for ML workloads
Architecture Approach
Northflank positions itself as comprehensive infrastructure rather than sandboxes alone. The platform's BYOC model appeals to teams with existing cloud contracts or strict data residency requirements who want to run sandbox workloads on their own infrastructure.
Best For: Teams needing multi-cloud deployment flexibility, unlimited session duration, and full-stack infrastructure beyond just sandboxes—especially those with existing cloud commitments.
4. Daytona
Daytona provides persistent development environments with fast sandbox creation. The platform's open-source GitHub repository has accumulated significant community adoption, and it supports Computer Use automation on Linux, with Windows and macOS in private alpha.
Core Capabilities
- Fast sandbox creation: Fast sandbox creation for rapid agent interactions
- Computer Use support: Daytona supports desktop automation on Linux; Windows and macOS support is currently in private alpha and requires access
- Open-source option: Daytona is open source under AGPL-3.0; for managed or on-premises enterprise setups, Daytona's pricing page points users to Enterprise
- Configurable persistence: Sandboxes auto-pause after 15 minutes of inactivity by default, with configurable runtime behavior
- GPU support: Available for ML workloads alongside persistent storage
Use Case Focus
Daytona's Computer Use capability sets it apart for teams building agents that need to interact with graphical interfaces. The open-source model appeals to teams wanting full control over their sandbox infrastructure.
Best For: Teams building agents that require desktop automation (browser control, GUI interaction) or need fast sandbox creation with an open-source deployment option.
5. Blaxel
Blaxel is a sandbox platform built specifically for AI agents, offering a "perpetual sandbox" model where environments stay on standby and resume quickly when needed. The platform supports fast resume from standby, enabling responsive agent interactions.
Core Capabilities
- Fast resume from standby: Environments resume quickly from standby, enabling near-instant agent responses
- Perpetual sandboxes: Environments can preserve state in standby, with unlimited persistence available on higher quota tiers; standby memory may be free, but storage for snapshots and volumes is still billed
- Native MCP support: Built-in Model Context Protocol server support for AI agent tool integration
- Template system: Reusable sandbox templates for standardized environments
- Cost-efficient idle: Blaxel claims 74% lower cost than Daytona in a specific intermittent-workload pricing example
Architecture Approach
Blaxel's standby/resume model differs fundamentally from ephemeral sandboxes. Instead of creating and destroying environments, sandboxes hibernate when idle and wake quickly when needed—preserving shell history, installed dependencies, and execution context across sessions.
Best For: Teams building agents with intermittent, bursty workloads where low latency matters and preserving state across sessions provides workflow continuity.
6. Vercel Sandbox
Vercel Sandbox provides isolated code execution environments built for running untrusted code in temporary Linux microVMs. The platform integrates natively with Vercel's deployment ecosystem and uses Firecracker for isolation.
Core Capabilities
- Firecracker microVMs: Each environment runs in an isolated Linux microVM with its own filesystem, network, and process space
- High concurrency: Supports 2,000 concurrent sandboxes on Pro plans
- CPU billing excludes I/O wait: Vercel's CPU billing excludes I/O wait time, though memory, sandbox creations, data transfer, and snapshot storage are billed or limited separately
- Explicit snapshots: Vercel Sandbox supports explicit snapshots that capture filesystem state and installed packages for later reuse
- Vercel ecosystem integration: Native integration with Vercel AI SDK and deployment platform
Use Case Focus
Vercel Sandbox fits teams already invested in the Vercel ecosystem who need sandboxed execution for AI agents or code testing. The CPU billing model that excludes I/O wait time can benefit workloads with significant I/O wait.
Best For: Teams building within the Vercel ecosystem who need isolated code execution with high concurrency and prefer CPU billing that excludes I/O wait time.
7. Fly.io Sprites
Fly.io Sprites takes a persistent VM approach with checkpointing capabilities, offering an alternative model to ephemeral sandboxes. The platform launched in January 2026 and focuses on stateful agent workflows.
Core Capabilities
- Persistent Linux environments: Sprites provide persistent Linux environments where users can install tools and preserve filesystem and memory state across runs
- Checkpointing: Sprites support checkpointing and restoring filesystem state for consistent agent context, with persistent Linux environments that preserve filesystem and memory between runs
- Fly.io infrastructure: Leverages Fly.io's global edge network for low-latency deployment
- Flexible environment configuration: Install tools and configure the environment directly within the persistent Linux VM
Architecture Approach
Sprites emphasize state persistence over ephemeral execution. The checkpointing model allows agents to pause, snapshot their filesystem state, and resume later—useful for long-running workflows that need to preserve context across interruptions.
Best For: Teams building stateful agents that need persistent Linux VM environments with checkpointing capabilities, particularly those already using Fly.io infrastructure.
Why Modal Stands Out for AI App Builders
Purpose-Built for AI Workloads
Modal's architecture is specifically engineered for AI and machine learning workloads. The platform's custom container runtime, scheduler, and file system are optimized for the unique demands of sandboxed code execution with GPU acceleration—a combination other sandbox platforms don't provide.
One of the Broadest GPU-Backed Platforms for AI Workloads
While competitors focus solely on CPU-based code execution, Modal layers GPU access on top of secure sandboxes, offering one of the broadest GPU-backed sandbox and AI infrastructure stacks available. Teams can run AI-generated code in isolated containers, then tap into T4, L4, A10, L40S, A100 variants, RTX PRO 6000, H100, H200, and B200/B200+ GPUs when workloads require acceleration for inference, fine-tuning, or compute-intensive analysis.
Secure Sandboxed Execution at Massive Scale
Modal's sandboxes support 50,000+ concurrent sessions with fast startup times, gVisor isolation, and full observability. This scale enables AI app builders to handle unpredictable traffic without manual capacity planning or infrastructure management.
Code-First Developer Experience
The code-first SDK eliminates YAML configuration and Kubernetes complexity. Modal offers SDKs for Python, Go, and JavaScript/TypeScript, letting teams define compute requirements, container images, and scaling behavior in code—and Sandboxes can run whatever runtime or language the workload requires. This approach enables rapid iteration: everything from sandboxes to batch processing to inference endpoints can be defined in a single file.
Enterprise Security and Compliance
With SOC 2 Type II certification, support for HIPAA-compliant workloads on Enterprise plans via a BAA, and comprehensive security practices including gVisor sandboxing and TLS 1.3, Modal provides enterprise security and compliance features including audit logs, SSO, and region selection.
Production-Proven at Scale
Modal powers infrastructure for over 10,000 teams, demonstrating enterprise-scale reliability. The platform's multi-cloud capacity pool ensures GPU and CPU availability without reservations, and its serverless model means teams pay for compute they use or request, without requiring pre-provisioned capacity.
For AI app builders who need secure code execution, on-demand GPU access, and production-grade infrastructure, Modal's combination of AI-native architecture, massive sandbox scale, and comprehensive GPU support makes it the clear choice.
Explore the Modal documentation to get started building secure AI app sandboxes.
View Modal Docs