Goose, an open-source AI agent originally created by Block (formerly Square) and now hosted under the Agentic AI Foundation, has become a go-to tool for developers building autonomous coding workflows. Now under Linux Foundation governance, Goose supports MCP-based extensions, multi-step orchestration, and connections to the broader MCP ecosystem, with third-party directories tracking 3,000+ MCP servers across the ecosystem. While Goose v1.25.0 introduced OS-level sandboxing for Goose Desktop on macOS, teams building production-grade agent systems need dedicated code execution infrastructure that can scale securely. Choosing the right secure sandbox determines whether your Goose agents can execute AI-generated code safely, scale without manual intervention, and access GPU acceleration when workloads demand it. This guide examines seven code execution sandbox platforms serving different Goose deployment needs in 2026, starting with Modal, a serverless AI infrastructure platform built for secure sandboxed execution at massive scale.
Key Takeaways
- Secure isolation is non-negotiable for AI-generated code: Goose agents autonomously generate and execute code, making sandboxed execution critical. Modal uses gVisor containers for compute isolation, while E2B uses Firecracker microVMs and Blaxel describes its sandbox architecture as lightweight virtual machines with Firecracker-derived microVM orchestration for hardware-level security boundaries.
- GPU access differentiates sandbox platforms: Modal combines secure Sandboxes with GPU-backed serverless compute, offering one of the broadest GPU selections among sandbox-capable platforms, spanning B200, H200, H100, A100, L40S, L4, A10, T4, and RTX Pro 6000 Blackwell, enabling Goose agents to run ML inference alongside code execution without platform switching.
- Massive concurrency enables production-scale agent deployments: Modal advertises autoscaling to 50,000+ concurrent Sandboxes, essential for teams running thousands of Goose agent instances simultaneously, subject to plan-level limits.
- State persistence varies significantly across platforms: Blaxel advertises sub-25ms resume times with persistent standby state; Modal provides snapshot primitives with varying retention periods; and E2B supports pause/resume with paused sandbox state retained indefinitely per current documentation.
- Enterprise compliance requirements narrow the field: Modal has completed a SOC 2 Type II audit and supports HIPAA-compliant workloads on Enterprise plans via a BAA; Blaxel holds SOC 2 Type II, HIPAA, and ISO 27001 certifications.
1. Modal
Modal delivers serverless AI infrastructure combining secure sandboxes with GPU access, making it a strong platform where Goose agents can execute AI-generated code securely while also running ML inference workloads. The platform powers cloud infrastructure for over 10,000 teams, including production deployments at Ramp, Lovable, and Quora. Lovable used Modal to run over 1 million sandboxes across a 48-hour event, peaking at 20,000 concurrent sandboxes, while Quora stress-tested Sandbox creation throughput to 1,000 Sandboxes per second.
Core Capabilities
- gVisor container isolation: Secure sandboxed execution for running AI-generated code with compute isolation that protects against untrusted code execution
- 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 also supports Sandbox snapshotting to preserve or restore state and Memory Snapshots to reduce initialization-heavy startup latency, subject to documented retention periods and feature limitations
- 50,000+ concurrent Sandboxes: Modal advertises autoscaling to 50,000+ concurrent Sandboxes for peak demand; actual container and GPU concurrency limits depend on the customer's plan and Enterprise configuration
- Code-first SDKs in Python, TypeScript, and Go: Modal provides a code-first Python SDK with no YAML configuration required, along with beta JavaScript/TypeScript and Go SDKs for working with Sandboxes, invoking Modal Functions, and managing resources. Sandboxes support all programming languages within the container runtime
- Broad GPU support: On-demand access to B200, H200, H100, A100, L40S, L4, A10, T4, and RTX Pro 6000 Blackwell GPUs when Goose agents need ML acceleration
Security and Compliance
Modal has completed a SOC 2 Type II audit. Modal supports HIPAA-compliant workloads on Enterprise plans via a BAA. The platform uses TLS 1.3 for public APIs, encrypts data in transit and at rest, and employs gVisor-based sandboxing for compute isolation.
Why Goose Teams Choose Modal
- Unified AI platform: Sandboxes, inference, training, batch processing, and notebooks in a single platform eliminates vendor sprawl
- Production track record: Ramp uses Modal Sandboxes to power background coding agents that generate code changes and write them back as commits or pull requests
- GPU-enabled code execution: Modal Sandboxes can run GPU-backed workloads when configured with GPUs, allowing Goose agents to combine sandboxed code execution with GPU-accelerated inference, fine-tuning, or analysis workflows on the same platform
Best For: Teams building Goose-powered coding agents that need secure code execution at enterprise scale, with on-demand GPU access for ML inference, model fine-tuning, 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 used by companies including Hugging Face, Perplexity, and Groq for agent-based code execution workflows.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation providing strong security boundaries for running untrusted AI-generated code
- Cold starts: E2B supports cold starts through its Firecracker microVM architecture; startup characteristics vary by workload and configuration
- Open-source core: Self-hosting available for organizations with data sovereignty requirements
- Multi-language support: First-party SDKs for Python and JavaScript/TypeScript; other languages may be supported through runtime execution or community integration patterns
- Template system: Reproducible sandbox environments with versioning for consistent agent deployments
Architecture Approach
E2B excels at ephemeral code execution, spinning up isolated environments for Goose agents to run generated code, then tearing them down. The platform supports up to 1,100 concurrent sandboxes on higher-tier plans with additional purchases.
Considerations for Goose Deployments
- Pause/resume with indefinite retention: E2B supports pause and resume with preserved state; paused sandboxes are retained indefinitely per current E2B documentation
- Session duration: Supports up to 24-hour sessions on Pro plans
- CPU-focused: Designed primarily for CPU-based code execution workloads
Best For: Teams building Goose agents focused on ephemeral code execution where microVM security is a priority and GPU acceleration is not required.
3. Blaxel
Blaxel is a sandbox platform built specifically for AI agents, with a focus on persistent "agent computers" that stay on standby and resume when needed. The platform advertises persistent standby without active compute billing, though persisted state may still incur storage-related charges.
Core Capabilities
- Sub-25ms resume: Blaxel advertises approximately 25ms resume times for restoring sandbox state
- Perpetual standby: Sandbox state preserved without time-based automatic deletion limits found on some other platforms
- Lightweight virtual machine isolation: Hardware-enforced security boundaries; Blaxel has publicly discussed Firecracker-derived microVM orchestration in its sandbox architecture
- Native MCP hosting: Built-in support for hosting Model Context Protocol servers alongside sandboxes
- 50,000+ concurrent sandboxes: Matches scale for massive deployments
Security and Compliance
Blaxel holds SOC 2 Type II, HIPAA support via BAA, and ISO 27001 certifications, making it well-suited for regulated industries.
Architecture Approach
Blaxel emphasizes persistent state rather than purely ephemeral execution. The platform recommends treating sandboxes as persistent computers that retain shell history, installed dependencies, and context over time, which benefits Goose agents needing continuity across workflows.
Best For: Teams building Goose agents that require persistent sandbox environments, state restoration on resume, and comprehensive compliance certifications.
4. Daytona
Daytona provides development environments with sandbox capabilities and an open-source foundation. The platform's GitHub repository had approximately 72.3k stars as of early 2026, reflecting strong community adoption.
Core Capabilities
- Open-source core: Full transparency and self-hosting option for organizations requiring control over infrastructure
- GPU support: Access to H100 and RTX PRO GPUs for ML workloads alongside code execution
- Cold starts: Daytona supports cold starts, including from a warm pool for reduced latency
- Docker/OCI compatibility: Standard container image support for flexible environment configuration
- Configurable persistence: Sandboxes can be configured for extended runtime with auto-stop after inactivity
Architecture Approach
Daytona describes its sandboxes as isolated environments with a dedicated kernel, filesystem, and network stack, alongside OCI/Docker compatibility. The platform focuses on development workspace continuity, maintaining state across sessions for Goose agents that need preserved context.
Considerations for Goose Deployments
- Configurable lifecycle controls: Daytona supports auto-stop, archive, and delete behavior with configurable lifecycle policies
- OCI/Docker-based isolation: Sandbox isolation uses a dedicated-kernel model with OCI/Docker compatibility; verify current deployment mode documentation for detailed security boundary comparisons against microVM platforms
- Community-driven: Development pace tied to open-source contribution
Best For: Teams building Goose agents who prefer open-source infrastructure with GPU access and OCI/Docker compatibility.
5. Vercel Sandbox
Vercel Sandbox provides isolated code execution environments built on Firecracker microVMs, integrated within the broader Vercel deployment platform. It's designed for AI agents, code execution, and development workflows requiring secure ephemeral environments.
Core Capabilities
- Firecracker microVMs: Each sandbox runs in an isolated Linux microVM with dedicated filesystem, network, and process space
- Active CPU billing: Pricing based on active CPU time rather than wall-clock time, reducing costs for I/O-bound workloads
- State persistence (Beta): Vercel offers Persistent Sandboxes in beta, which save filesystem state on stop and restore it on resume; snapshots expire after 30 days by default
- Vercel platform integration: Vercel Sandbox integrates with the Vercel platform through @vercel/sandbox, CLI tooling, project auth, and deployment workflows
Considerations for Goose Deployments
- Session limits: Maximum 5-hour session duration on Pro tier, the shortest among compared platforms
- Vercel-centric: Best value realized within the Vercel/Next.js ecosystem
- CPU-focused: Designed for code execution without GPU acceleration
Best For: Teams building Goose agents within the Vercel/Next.js ecosystem who prioritize TypeScript-first development and tight platform integration over GPU access.
6. Cloudflare Sandbox
Cloudflare Sandbox provides code execution environments through the Sandbox SDK, built on Cloudflare Workers, Durable Objects, and Containers and positioned for edge-oriented execution of Python and Node.js workloads.
Core Capabilities
- Container isolation: Each sandbox runs in a dedicated Linux container with isolated filesystem
- TypeScript-first SDK: API for sandbox lifecycle management, command execution, file operations, and WebSocket connections
- Edge-oriented execution: Built on Cloudflare Workers, Durable Objects, and Containers for reduced latency across distributed environments
- Python and Node.js support: Execution of scripts, applications, code compilation, and data processing workloads
- Configurable persistence: Support for keepAlive and configurable sleep behavior
Considerations for Goose Deployments
- Cold starts: Cloudflare Sandbox supports cold starts for container-based workloads; startup characteristics vary by workload and configuration
- Container isolation model: Lighter-weight isolation compared to Firecracker microVMs
- Cloudflare-native: Best suited for teams already using Cloudflare infrastructure
Best For: Teams building Goose agents who want edge-oriented code execution within a Cloudflare-native environment and prefer TypeScript-first development.
7. Runloop
Runloop provides sandbox infrastructure for AI agent workloads with a focus on enterprise deployment scenarios. The platform uses microVM isolation and offers state persistence through snapshots.
Core Capabilities
- MicroVM isolation: Hardware-level security boundaries for running untrusted code
- State snapshots: Ability to save and restore sandbox state across sessions
- Enterprise focus: Positioned for larger-scale deployments requiring dedicated support
- Python and TypeScript SDKs: Standard language support for agent integration
Architecture Approach
Runloop emphasizes reliable sandbox execution with state persistence capabilities, suited for Goose agents that need to checkpoint progress and resume from saved states.
Best For: Teams building Goose agents requiring enterprise-grade microVM isolation with state snapshot capabilities.
Why Modal Stands Out for Goose Agent Sandboxes
One of the Broadest GPU Selections Among Sandbox Platforms
Modal combines secure Sandboxes with GPU-backed serverless compute, offering one of the broadest GPU catalogs among sandbox-capable platforms. While some Goose agent workflows are CPU-focused, many benefit from GPU acceleration for ML inference, code analysis models, or embedding generation. Modal's GPU lineup includes B200, H200, H100, A100, L40S, L4, A10, T4, and RTX Pro 6000 Blackwell, enabling Goose agents to run a wide spectrum of AI workloads without switching platforms.
Unified AI Infrastructure Platform
Unlike dedicated sandbox providers, Modal combines sandboxes, inference, training, batch processing, and notebooks in a single platform. This unified approach eliminates vendor sprawl and reduces integration overhead when Goose agents need capabilities beyond basic code execution.
Developer-First Experience
Modal provides a code-first SDK with support for Python, TypeScript, and Go, letting teams define sandboxes, compute requirements, and scaling behavior directly in code without YAML configuration. Beta JavaScript/TypeScript and Go SDKs are available for working with Sandboxes, invoking Modal Functions, and managing resources. This code-first approach accelerates iteration cycles and enables rapid prototyping of Goose agent workflows.
Security Without Compromise
Modal's gVisor-based sandboxing, completed SOC 2 Type II audit, and support for HIPAA-compliant workloads on Enterprise plans via a BAA meet enterprise compliance requirements. The platform uses TLS 1.3 for public APIs and encrypts data in transit and at rest, providing the security posture that regulated industries demand for autonomous code execution.
Massive Concurrency at Production Scale
Modal advertises autoscaling to 50,000+ concurrent Sandboxes for peak demand. Actual container and GPU concurrency limits depend on the customer's plan and Enterprise configuration, but the platform is built to handle the scale that large Goose deployments require.
For teams that need secure sandboxed execution, autoscaling to very high concurrency, and optional GPU acceleration in one serverless platform, Modal is the strongest fit among the options discussed in this article.
Explore the Modal Sandboxes documentation to get started with Goose agent integration.
View Sandboxes Docs