Key Takeaways
- Isolation models vary across platforms: Hardware-virtualized microVMs such as Firecracker (used by E2B and Vercel) and Kata Containers provide hardware-level security boundaries, Modal uses gVisor containers with custom syscall filtering, and platforms like Cloudflare and Daytona use container-based isolation (Cloudflare Containers and Sysbox, respectively).
- Cold start and resume performance vary across platforms: Resume from standby and initial sandbox creation are separate metrics, and creation times can extend to several seconds depending on image complexity. Modal supports fast sandbox scheduling and strong cold-start performance on custom images.
- GPU access separates ML-capable platforms from pure code execution: Only Modal, Northflank, and Daytona offer GPU support for sandboxed workloads. Modal supports a broad GPU lineup including T4, L4, A10, L40S, A100 variants, RTX PRO 6000, H100, H200, and B200/B200+ (GPU docs).
- Compliance certifications matter for enterprise deployments: Modal is SOC 2 Type II compliant and supports HIPAA-compliant workloads on Enterprise plans via a BAA.
- Production scale proves platform reliability: Modal powers infrastructure for over 10,000 teams including companies like Quora, Ramp, and Lovable, demonstrating enterprise-grade reliability for sandbox workloads.
1. Modal
Modal delivers serverless sandboxed compute at massive scale, combining secure code execution with on-demand GPU access for AI workloads that require acceleration. The platform's custom-built infrastructure, including its container runtime, scheduler, and file system, is engineered specifically for AI and machine learning workloads.
Core Capabilities
- gVisor container isolation: Secure sandboxed execution using gVisor with custom syscall filtering, protecting against untrusted AI-generated code
- Massive concurrent scale: Supports 100k+ concurrent sandboxes, with 1B+ sandboxes run, and fast sandbox scheduling
- Code-first SDKs: Modal is code-first and avoids YAML, with SDKs in Python, TypeScript, and Go for defining infrastructure, running Sandboxes, calling Functions, and managing Modal resources such as Volumes, Secrets, and Queues; code running inside a sandbox is not limited to one language and can use whatever runtime the workload requires
- Broad GPU support: A broad GPU lineup including T4, L4, A10, L40S, A100 variants, RTX PRO 6000, H100, H200, and B200/B200+, with B200+ able to run on B200 or B300 where compatible
- 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
- Snapshotting: Modal Sandboxes support filesystem, directory, and memory snapshots for preserving state and reducing startup work. Modal also offers GPU Memory Snapshots for deployed Functions
Security and Compliance
Modal is SOC 2 Type II compliant, having completed its SOC 2 Type II audit, and supports HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement. The platform's security architecture includes gVisor-based sandboxing 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:
- Quora uses Modal Sandboxes to securely execute LLM-generated code in Poe, with sandbox creation throughput stress-tested to 1,000 sandboxes per second supporting thousands of simultaneous users
- Ramp built a background coding agent on Modal's infrastructure to generate code changes and write them back as commits or pull requests
- Modal's unified platform combines sandboxed execution with inference, training, and batch processing in a single SDK
What Makes Modal Unique
- Unified AI platform: Modal combines secure code execution with inference, training, batch processing, GPU-backed notebooks, and infrastructure primitives in one AI infrastructure platform
- AI-native architecture: Custom file system, container runtime, scheduler, and container image builder optimized for AI workloads
- Multi-cloud capacity pool: Deep GPU capacity across major cloud providers ensures availability without reservations
- Configurable session persistence: Modal Sandboxes support configurable runtimes up to 24 hours plus snapshot-based state preservation, with filesystem snapshots that persist until deleted
Best For: Teams building AI agents that require massive concurrent scale, GPU access for ML workloads, and production-grade reliability with enterprise compliance.
2. E2B
E2B focuses on secure sandboxes for AI agents using Firecracker microVM isolation. The platform provides hardware-level security boundaries specifically designed for running untrusted AI-generated code at scale.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation providing strong security boundaries for untrusted code execution
- AI-first SDK design: Purpose-built Python and JavaScript SDKs for agent workflows with OpenAI Agents SDK integration
- Open-source foundation: Apache-2.0 licensed with experimental BYOC options for self-hosting
- Template system: Reproducible sandbox environments with versioning for consistent deployments
Use Case Focus
E2B reported in July 2025 that 88% of the Fortune 100 had signed up, and its current homepage claims 94% of Fortune 100 companies use E2B. Public sources describe E2B operating at multi-million monthly sandbox volume. The platform supports up to 1,100 concurrent sandboxes on higher-tier plans and supports cold starts.
Architecture Approach
E2B focuses on ephemeral code execution, spinning up isolated Firecracker microVM environments for agents to run generated code. E2B limits continuous runtime by plan (for example, 24 hours on Pro and 1 hour on lower tiers), but it also supports pause/resume with preserved state, so the practical persistence model depends on workload and plan.
Best For: Teams building coding agents focused on ephemeral code execution where hardware-level microVM isolation is the priority and GPU acceleration is not required.
3. Northflank
Northflank provides a complete cloud platform with flexible microVM sandbox options, offering the ability to choose between multiple isolation technologies per workload. Northflank says it processes over 2M isolated workloads monthly and has operated since 2019.
Core Capabilities
- Flexible isolation options: Choose between Kata Containers, Firecracker, Cloud Hypervisor, or gVisor per workload
- Self-serve BYOC: Deploy in your own AWS, GCP, Azure, Oracle, CoreWeave, or bare-metal infrastructure without sales calls
- Any OCI image support: Standard container images work without proprietary formats or SDK-defined images
- GPU support: L4, A100 (40GB/80GB), H100, and H200 GPUs available for ML workloads
- Unlimited session duration: No hard time limits on sandbox runtime
Architecture Approach
Northflank says its engineering team contributes to Kata Containers, QEMU, containerd, and Cloud Hypervisor, providing deep expertise in isolation technologies. The platform offers a complete infrastructure stack including sandboxes, databases, APIs, GPUs, and CI/CD in a unified experience.
Production Track Record
According to a Northflank customer story, cto.new used Northflank while serving 30,000+ developers and thousands of daily deployments. The platform maintains SOC 2 Type 2 compliance for enterprise requirements.
Best For: Teams requiring flexibility in isolation technology, self-serve BYOC deployment options, and a complete infrastructure platform beyond just sandboxes.
4. Daytona
Daytona provides sandbox environments and supports sandbox creation. The platform combines provisioning with compliance work, making it suitable for teams that prioritize startup workflows.
Core Capabilities
- Provisioning: Daytona supports sandbox creation/provisioning
- Sysbox/container isolation: Daytona's security exhibit documents Sysbox as its container runtime, providing VM-level isolation without hardware virtualization overhead
- GPU support: Daytona supports GPU sandboxes created from GPU snapshots, though current documentation says GPU sandboxes must be ephemeral
- Multi-SDK support: Python, TypeScript, Ruby, Go SDKs plus REST API
- Computer Use API: Programmatic desktop interactions for IDE-adjacent workflows
Security and Compliance
Daytona's security exhibit states it has achieved SOC 2 Type I, with SOC 2 Type II listed as in progress; a HIPAA BAA is available. The platform offers native IDE integration with VS Code, Cursor, Windsurf, and JetBrains IDEs via SSH.
Architecture Approach
Daytona focuses on stateful sandbox environments with configurable persistence. Sandboxes can be configured for indefinite runtime, though they auto-stop after 15 minutes of inactivity by default and auto-archive after 7 days by default.
Best For: Teams building coding agents that value sandbox provisioning, compliance certifications, and IDE integration for development workflows.
5. Blaxel
Blaxel offers an approach to sandbox persistence with perpetual standby capabilities. The platform focuses on a model where sandboxes remain on standby indefinitely at zero compute cost.
Core Capabilities
- Perpetual standby: Blaxel claims unlimited standby with no compute charges while idle
- Resume from standby: Blaxel supports resume from standby while preserving filesystem and memory state
- MicroVM isolation: Blaxel uses microVM isolation similar in approach to AWS Lambda's Firecracker-based isolation
- Co-located agent hosting: Eliminates network roundtrip between agent and sandbox
- 15-second auto-shutdown: Sandboxes sleep after brief network inactivity to optimize costs
Security and Compliance
Blaxel publicly lists SOC 2 Type II, ISO 27001, and HIPAA support. Note that there is no official HIPAA certification, so this reflects HIPAA support and a BAA rather than a certification. This compliance posture addresses regulatory requirements across healthcare, finance, and enterprise sectors.
Architecture Approach
Unlike ephemeral execution models, Blaxel treats sandboxes as persistent computers that retain shell history, installed dependencies, and context over time. E2B currently documents indefinite state preservation for paused sandboxes via pause/resume, while Blaxel maintains sandboxes on standby.
Best For: Teams building coding agents, PR review agents, or data analysis agents that benefit from persistent state, resume from standby, and a broad compliance posture.
6. Vercel Sandbox
Vercel Sandbox provides Firecracker-based isolated execution environments designed for teams already using the Vercel ecosystem. The platform offers an active-CPU-only billing model where you pay only when code actively executes.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation powered by Firecracker for secure code execution
- Active CPU billing: Pay only when code actively executes, not during idle time
- State persistence: Vercel supports automatic persistence for persistent sandboxes; as of the cited Vercel changelog, that capability is in beta
- Linux environment: Full sudo access, package managers, and standard command-line workflows
- Vercel integration: Native connection to the Vercel deployment and hosting ecosystem
Current Status
Vercel Sandbox is now generally available, with persistent sandboxes and tags offered as beta features. Sessions support up to 5 hours on Pro and Enterprise plans, with language support focused on Node.js and Python. The platform operates in a single region (iad1) currently.
Use Case Focus
Vercel Sandbox fits teams that need secure, ephemeral execution for agent workflows, testing, and development tasks. The 45-minute session cap on Hobby plans may constrain longer-running agent workflows.
Best For: Teams already on Vercel who want sandboxed agent execution for playgrounds, demos, and shorter-lived tasks, particularly those who value the active-CPU billing model.
7. Cloudflare Sandboxes
Cloudflare Sandboxes provide code execution environments that integrate with the broader Cloudflare Workers and Containers ecosystem. The platform connects to Cloudflare's network.
Core Capabilities
- Workers and Containers foundation: Cloudflare's Sandbox SDK is built on Workers, Durable Objects, and Containers
- Cloudflare platform integration: Cloudflare Sandboxes integrate with Cloudflare Workers and the broader platform
- Python and Node.js support: Primary language runtimes for sandbox execution
- Workers ecosystem integration: Native connection to R2, KV, and Durable Objects
- TypeScript-first SDK: API for sandbox lifecycle management, command execution, and file operations
Architecture Approach
Cloudflare Sandboxes use container-based isolation rather than hardware-virtualized microVMs. Inactive sandboxes sleep after 10 minutes by default, and commands have no timeout unless one is configured. Sandboxes are ephemeral; state does not persist after sandbox termination.
Use Case Focus
Cloudflare Sandboxes suit applications built around the Cloudflare ecosystem.
Best For: Teams building applications that need integration with the Cloudflare Workers and Containers ecosystem.
Why Modal Stands Out for Sandbox Workloads
Massive Scale with Production Reliability
Modal's sandbox infrastructure supports 100k+ concurrent sandboxes with sandbox creation throughput stress-tested to 1,000 sandboxes per second. This scale has been proven in production by companies like Quora, which uses Modal Sandboxes to execute LLM-generated code in Poe for thousands of simultaneous users.
Unified AI Infrastructure Platform
Unlike dedicated sandbox providers, Modal combines secure code execution with inference, training, batch processing, and GPU-backed notebooks in a single SDK. This unified approach means AI agents can execute code in sandboxes and call GPU-accelerated inference endpoints without switching platforms or managing multiple vendor relationships.
Broad GPU Support for ML Workloads
Modal supports a broad GPU lineup including T4, L4, A10, L40S, A100 variants, RTX PRO 6000, H100, H200, and B200/B200+, with B200+ able to run on B200 or B300 where compatible. This enables agents to match compute to workload requirements, whether running lightweight code analysis models on T4s or large language models on H100s.
Code-First Developer Experience
Modal is code-first and avoids YAML configuration, with SDKs in Python, TypeScript, and Go for defining infrastructure, running Sandboxes, calling Functions, and managing Modal resources. Code running inside a sandbox is not limited to one language and can use whatever runtime the workload requires. Teams define sandboxes, compute requirements, and scaling behavior directly in code, enabling rapid iteration and deployment velocity that configuration-file-based platforms struggle to match.
Enterprise Security and Compliance
Modal is SOC 2 Type II compliant and supports HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement. The platform's security practices include gVisor-based sandboxing, TLS 1.3 for APIs, and encryption for data in transit and at rest.
Fast Scheduling with Snapshotting
Modal supports fast sandbox scheduling and strong cold-start performance on custom images, aided by techniques such as memory snapshotting and an optimized filesystem. Initialization-heavy workloads may benefit from snapshots. This combination of fast startup and state preservation makes Modal suitable for both ephemeral execution and longer-running agent workflows.
For teams building AI systems that require secure code execution at scale, GPU access for ML workloads, and production-grade reliability, Modal's combination of AI-native infrastructure, massive concurrent capacity, and proven enterprise scale makes it the clear choice.
