Code execution sandboxes have become essential infrastructure for AI-powered development workflows. As coding agents, AI assistants, and automated development tools generate and execute code autonomously, secure isolation is no longer optional; it's foundational. Windsurf developers and teams building AI-native applications need sandbox environments that combine security, speed, and scale. This guide examines seven code execution sandbox platforms serving different development needs in 2026, starting with Modal's secure sandboxes, which support massive concurrency with gVisor isolation and optional GPU access for workloads that require acceleration.
Key Takeaways
- Security isolation is non-negotiable for AI-generated code: Sandboxes protect against untrusted code execution. Modal uses gVisor containers for isolation, while E2B and Vercel employ Firecracker microVMs for hardware-level security boundaries
- Cold start performance: Competing sandbox platforms support cold starts, while Modal is engineered for fast cold starts with the added benefit of comprehensive GPU support
- GPU access differentiates platforms: Modal stands out as one of the strongest choices for teams that need secure sandboxes with first-class, deeply integrated GPU access (T4, L4, A10, A100, H100, H200, B200) on the same AI infrastructure platform, enabling AI workloads that require ML inference alongside code execution
- Concurrency limits matter at scale: Modal supports 100k+ concurrent sandboxes, making it suitable for high-traffic multi-tenant applications
- Enterprise compliance requirements shape platform choice: Modal offers SOC 2 Type II certification and HIPAA support via BAA on Enterprise plans, meeting regulated industry requirements
1. Modal
Modal delivers serverless compute for secure code execution at scale, with on-demand GPU access available when workloads require acceleration. The platform containerizes your code and executes it in the cloud with automatic scaling, all defined through a code-first SDK approach in Python, TypeScript, and Go, without YAML configuration files. Sandboxes support all programming languages; the SDK language used to define and manage sandboxes is independent of what runs inside them.
Core Capabilities
- gVisor container isolation: Secure sandboxed execution for running AI-generated code, with each container isolated using gVisor-based sandboxing
- Massive concurrent scale: Support for 100k+ concurrent sandbox sessions, proven at production scale with major AI products
- 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 can further reduce initialization-heavy startup times
- Comprehensive GPU support: Access to NVIDIA GPUs including T4, L4, A10, A100, H100, H200, and B200 for workloads requiring ML inference or GPU-accelerated computation
- Code-first development: Define Modal apps through code-first SDKs in Python, TypeScript, and Go; sandboxes support all programming languages and are not limited to the SDK language
Security and Compliance
Modal has successfully completed a SOC 2 Type II audit; Modal's January 2025 announcement stated that no deviations were found in that audit. Modal supports HIPAA-compliant workloads on Enterprise plans via a BAA. Security infrastructure includes TLS 1.3 for public APIs, encryption for data in transit and at rest, and gVisor-based compute isolation.
Production-Proven Results
Modal powers cloud infrastructure for over 10,000 teams, including AI companies building production applications:
- Powers major AI products including Lovable and Quora with millions of daily executions
- Ramp uses Modal Sandboxes for background coding agents that generate code changes
- The platform's scale-to-zero architecture eliminates idle capacity costs for spiky workloads
What Makes Modal Unique
- Integrated AI platform: Sandboxes combined with inference, training, and batch processing in a unified platform, eliminating vendor fragmentation
- Dynamic environment definition: Define execution environments programmatically at runtime through SDKs
- Filesystem snapshots: Persist sandbox state for faster resume times on subsequent executions
- Multi-cloud capacity pool: Deep GPU and CPU capacity across cloud providers ensures availability without reservations
Best For: Teams building AI agents and coding assistants that need secure code execution at scale, with on-demand GPU access when workloads require ML inference or compute-intensive analysis.
2. E2B
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. The platform is positioned around integration and SDK-first development for AI agent builders.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation for running untrusted AI-generated code
- Sandbox provisioning: E2B's Firecracker-based sandboxes support cold starts
- Multi-language SDKs: Support for Python and TypeScript integration patterns
- Template system: Reproducible sandbox environments with versioning for standardized execution
- Pause/resume functionality: Ability to pause sandboxes and resume them later
Session and Concurrency
E2B supports up to 100 concurrent sandboxes on Pro tier plans. Session duration extends to 24 hours on Pro plans, with shorter limits on free tiers. The platform focuses on ephemeral execution patterns where sandboxes spin up, execute code, and tear down.
Enterprise Features
E2B offers BYOC (bring-your-own-cloud) deployment for Enterprise customers on AWS and GCP, addressing data residency requirements for organizations that need to run sandboxes within their own cloud accounts.
Best For: Teams building coding agents focused on code execution and testing where GPU acceleration is not required, particularly those prioritizing integration and SDK simplicity.
3. Northflank
Northflank provides a full-stack cloud platform with sandbox capabilities, positioning itself around production-grade microVM isolation and flexible deployment options. Northflank says it processes over 2 million isolated workloads monthly and offers self-serve BYOC deployment.
Core Capabilities
- Flexible isolation options: Northflank publishes support for microVM-backed sandboxes and gVisor-based isolation, with Firecracker, Kata Containers, gVisor, and Cloud Hypervisor documented as supported or relevant isolation technologies
- BYOC deployment: Self-serve bring-your-own-cloud across AWS, GCP, Azure, and Oracle without requiring enterprise sales processes
- GPU support: Available for ML workloads alongside sandbox execution
- Full platform scope: Sandboxes integrated with databases, APIs, workers, and jobs in one control plane
- Session duration: Northflank does not prominently publish a fixed short session limit
Cold Start Performance
Northflank's microVM-backed sandboxes support cold starts.
Deployment Flexibility
The platform supports standard OCI container images, enabling teams to use existing container workflows. Northflank's self-serve BYOC model addresses data residency and compliance requirements without enterprise-tier restrictions.
Best For: Teams that need sandbox capabilities alongside broader infrastructure (databases, APIs, workers) in a unified platform, or organizations with strict data residency requirements needing BYOC deployment.
4. Daytona
Daytona provides development environments that support cold starts. The platform offers both open-source self-hosting and managed cloud options, with experimental GPU support and configurable runtime persistence.
Core Capabilities
- Cold starts: Daytona supports cold starts for sandbox provisioning
- Isolated sandbox environments: Daytona supports OCI/Docker-compatible images and creates isolated sandbox environments with a dedicated kernel, filesystem, network stack, and allocated compute resources
- Open-source option: Self-hosting available for organizations requiring full control over their sandbox infrastructure
- GPU support: Experimental GPU sandbox support is available through GPU snapshots
- Configurable session duration: Sandboxes can be configured to run indefinitely by disabling auto-stop; the default auto-stop interval is 15 minutes, and long-running background tasks may require explicit configuration
Architecture Approach
Daytona focuses on persistent workspaces that maintain state across sessions. This benefits agents that need to preserve context, cached dependencies, or intermediate results without recreation overhead between tasks.
Development Focus
The platform's open-source positioning and cold start support make it suitable for teams that want to self-host sandbox infrastructure or need environment provisioning for latency-sensitive workflows.
Best For: Teams building coding agents where cold start latency is the primary concern, or organizations that prefer open-source self-hosting for sandbox infrastructure.
5. Koyeb
Koyeb offers a serverless sandbox platform currently in public preview, with scale-to-zero architecture and SDK-driven sandbox creation. The platform focuses on developer experience with automatic scaling and managed infrastructure.
Core Capabilities
- Scale-to-zero architecture: Sandboxes automatically scale down when idle, reducing costs for intermittent workloads
- Startup: Koyeb supports cold starts and offers Light Sleep and Deep Sleep wake modes for idle services
- Container-based isolation: Sandboxes run in isolated containers with configurable resource allocation
- SDK and API-driven creation: Sandboxes are created and managed programmatically through Koyeb's SDK and API
- GPU support: Available through Koyeb's broader platform for workloads requiring GPU acceleration
- Session duration: Koyeb sandboxes are temporary environments; current documentation allows lifecycle and auto-deletion configuration, with maximum auto-deletion windows of 24 hours after creation or 12 hours after scale-to-zero
Serverless Model
Koyeb's serverless approach eliminates the need to manage sandbox infrastructure directly. The platform handles provisioning, scaling, and teardown automatically based on demand patterns.
Developer Experience
The platform emphasizes straightforward deployment workflows, making it suitable for teams that want managed sandbox infrastructure without complex configuration.
Best For: Teams looking for managed serverless sandbox infrastructure in public preview with scale-to-zero economics and SDK-driven automated workflows.
6. Fly.io Sprites
Fly.io Sprites provides persistent VM-based sandboxes with checkpoint and restore capabilities. The platform focuses on maintaining state across sandbox sessions with Firecracker microVM isolation.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation similar to E2B and Vercel Sandbox
- Checkpoint/restore: Save and restore sandbox state for continuity across sessions
- Persistent state: Sandboxes designed to maintain context rather than ephemeral execution
- Persistent Linux environments: Sprites provide persistent hardware-isolated Linux environments where users can install tools and manage files and state across sessions
- Session duration: Sprites are persistent Linux environments that can idle, hibernate, and preserve state, without a documented guarantee of unlimited continuous runtime
Cold Start Characteristics
Fly.io Sprites support cold starts, and warm Sprites can wake from hibernation. The checkpoint/restore functionality helps reduce effective startup time for resumed sandboxes.
Architecture Approach
Sprites emphasizes persistence and state management over pure ephemeral execution. The checkpoint/restore model suits workflows where agents need to pick up where they left off rather than starting fresh each time.
Best For: Teams building agents that require persistent sandbox environments with state continuity across sessions, particularly when checkpoint/restore functionality is valuable.
7. Vercel Sandbox
Vercel Sandbox provides isolated code execution environments in temporary Linux microVMs. The platform uses Firecracker for isolation and positions itself around secure, ephemeral execution for AI agents and developer workflows.
Core Capabilities
- Firecracker microVMs: Each sandbox runs in an on-demand Linux microVM with isolated filesystem, network, and process space
- Ephemeral runtime model: Sandboxes are temporary by design, started when needed and stopped after use
- Linux environment access: Full Linux environment with sudo, package managers, and standard command-line tools
- State persistence options: Vercel supports snapshot-based state persistence; persistent sandboxes are documented as a beta capability; otherwise, sandbox filesystem data is lost when stopped
- Session limits: Default timeout is 5 minutes; maximum runtime is 45 minutes on Hobby and 5 hours on Pro and Enterprise plans
Architecture Approach
Vercel Sandbox fits workflows involving repeated start-run-stop cycles, short-lived tasks, or safe execution of generated code. The ephemeral model prioritizes clean execution environments over persistent state.
Integration Context
As part of the broader Vercel platform, Sandbox integrates with Vercel's deployment and hosting infrastructure, making it convenient for teams already using Vercel for frontend applications.
Best For: Teams already using Vercel's platform that need isolated environments for code execution, testing, or agent workflows with ephemeral execution requirements.
Why Modal Stands Out for Windsurf Development
Sandboxes With Comprehensive, Integrated GPU Access
Unlike most sandbox platforms, Modal layers broad GPU support on top of secure code execution, with integrated access to a broad GPU lineup spanning T4, L4, A10, A100, H100, H200, and B200. Some sandbox vendors also offer GPU-related capabilities, but availability, breadth, and integration vary across platforms. Modal's stronger claim is that it combines sandboxes with a broad, integrated serverless GPU platform for inference, training, fine-tuning, and batch workloads, all within a single AI infrastructure platform. For Windsurf developers building AI-native applications, this means coding agents can securely execute generated code and run ML inference within the same infrastructure.
Proven Scale for Production Workloads
Modal's support for 100k+ concurrent sandbox sessions sets it apart for high-traffic, multi-tenant workloads. The platform powers millions of daily executions for major AI products including Lovable and Quora, demonstrating enterprise-scale reliability. For teams building multi-tenant SaaS products or high-traffic AI applications, this proven scale reduces operational risk.
Enterprise Security Without Compromise
Modal has successfully completed a SOC 2 Type II audit; Modal's January 2025 announcement stated that no deviations were found in that audit. Modal supports HIPAA-compliant workloads on Enterprise plans via a BAA. The combination of gVisor-based isolation, TLS 1.3, and encryption for data at rest and in transit meets the security bar that regulated industries require.
Developer Experience Through Code-First SDKs
Modal's code-first model eliminates YAML configuration files, enabling faster iteration cycles. Modal supports code-first SDKs in Python, TypeScript, and Go, with sandboxes supporting all programming languages. Teams define container images, compute requirements, and scaling behavior directly in application code. This approach accelerates development velocity compared to platforms requiring separate infrastructure configuration.
Unified AI Infrastructure Platform
Beyond sandboxes, Modal provides a complete AI infrastructure platform including inference serving, model training, and batch processing. This unified approach eliminates the need to manage multiple vendors and separate billing relationships. For Windsurf developers building AI applications that span code execution, ML inference, and compute-intensive workloads, Modal consolidates infrastructure complexity.
Fast Scheduling With Memory Snapshotting
Modal's fast scheduling and optimized filesystem help Sandboxes start quickly. Memory Snapshots can further reduce initialization-heavy cold starts by restoring initialized state rather than starting from scratch, and the optimized filesystem helps containers come online quickly without large images slowing startup. For interactive AI applications where response time matters, this performance engineering translates to better user experience.
Get started with Modal's sandbox documentation to build secure, scalable code execution for your Windsurf applications.
