Key Takeaways
- Stateful sandboxes preserve agent context across sessions: Unlike ephemeral containers, these platforms retain filesystem state, installed dependencies, and intermediate results, eliminating rebuild overhead for multi-day agent workflows
- Session duration limits vary significantly: Some platforms cap sessions at 24 hours, while others offer unlimited runtime. Modal Sandboxes can be configured to run up to 24 hours, with filesystem snapshots to preserve state for workflows beyond that window
- Security isolation protects against untrusted code: Modal uses gVisor containers for secure sandboxed execution, while other platforms employ Firecracker microVMs or Kata containers
- GPU availability separates agent-ready platforms: Modal offers the broadest GPU lineup among the platforms in this guide, from T4 through B200, enabling agents to call upon acceleration when workloads demand it
- Resume speed impacts real-time agent interactions: Cold start and resume performance varies across platforms depending on architecture and warm pool strategies
1. Modal
Modal delivers serverless compute for secure AI agent sandboxes at massive scale, with on-demand GPU access for workloads requiring acceleration. The platform's custom-built infrastructure handles dynamically defined containers that can support 100k+ concurrent sandboxes with fast startup times.
Core Capabilities
- gVisor container isolation: Secure sandboxed execution for running AI-generated code safely, with syscall-level isolation that adds a strong isolation layer between workloads and host systems
- Memory snapshotting: For Modal Functions, Memory Snapshots reduce initialization-heavy cold starts, with CPU Memory Snapshots capturing CPU memory and alpha GPU Memory Snapshots additionally capturing GPU state. For Modal Sandboxes, state can be preserved through filesystem snapshots, directory snapshots, and Sandbox memory snapshots in alpha
- 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
- Broad GPU support: Agents can access GPUs on demand, with options spanning T4, L4, A10, L40S, A100 variants, RTX PRO 6000, H100, H200, and B200 for ML inference and compute-intensive analysis
- Filesystem and networking primitives: Volumes for persistent storage, tunnels for network access, and queues for coordinating multi-agent workflows
Security and Compliance
Modal maintains SOC 2 Type II certification and supports HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement. The platform uses gVisor-based sandboxing for compute isolation, TLS 1.3 for public APIs, and encryption for data in transit and at rest. Modal documents vulnerability remediation SLAs and publishes a detailed security guide covering application, corporate, and infrastructure security practices.
Architecture Approach
Modal's AI-native platform page describes an AI-native container runtime, optimized filesystem behavior, a multi-cloud capacity pool, storage, data, and networking primitives, and observability. Modal also provides image-building APIs and scheduling and resource controls in its docs. The architecture supports dynamically defined sandboxes that can preserve state through Volumes and filesystem snapshots while scaling elastically based on demand. Observability for individual sandboxes enables monitoring and debugging of long-running agent sessions.
Best For: Teams building AI agents that need secure code execution at scale, with persistent state through Volumes and filesystem snapshots, broad GPU access, and enterprise-grade compliance for production deployments. Sandbox memory snapshots can additionally clone full sandbox state and are currently in alpha.
2. Northflank
Northflank provides persistent sandbox infrastructure with unlimited session duration and self-serve BYOC (bring your own cloud) deployment across multiple cloud providers.
Core Capabilities
- Unlimited session runtime: Sandboxes can run indefinitely without time caps, supporting agents working on multi-week projects
- Isolation options: Northflank supports Kata Containers by default and may use gVisor as an alternative; public docs do not support Firecracker as a Northflank sandbox isolation choice
- Self-serve BYOC deployment: Deploy sandboxes in your own AWS, GCP, Azure, or bare-metal infrastructure without enterprise sales contracts
- GPU support: H100, A100, and L4 GPUs available for ML workloads alongside persistent storage
- Infrastructure configuration: Northflank supports UI, CLI, API, GitOps workflows, and reusable templates for infrastructure configuration
Security and Compliance
Northflank reports SOC 2 Type 2 compliance. Its public security page does not support HIPAA BAA availability and currently lists HIPAA as "No." The platform supports data residency requirements through BYOC deployment.
Architecture Approach
Northflank focuses on persistent workspaces that maintain state across sessions indefinitely. The platform provides full infrastructure alongside sandboxes, including databases and APIs, making it suitable for agents that need access to additional services.
Best For: Teams requiring unlimited session duration, BYOC deployment for data residency requirements, and a choice of isolation technologies.
3. E2B
E2B specializes in secure sandboxes for AI agents with Firecracker microVM isolation and cold starts for ephemeral code execution.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation using the same technology that powers AWS Lambda
- Template system: Reproducible sandbox environments with versioning for consistent agent deployments
- Open-source option: Self-hosting available for organizations with data sovereignty requirements
- Multi-language SDKs: Python and TypeScript/JavaScript integration patterns for agent frameworks
- MCP integration: Native support for the Model Context Protocol for agent tool connectivity
Session Characteristics
E2B offers 1-hour sessions on base plans and 24-hour sessions on Pro plans. Paused sandboxes can be retained for resumption, with the platform supporting up to 1,100 concurrent sandboxes on higher-tier plans.
Architecture Approach
E2B excels at ephemeral code execution patterns, spinning up isolated environments for agents to run generated code. E2B caps continuous running sessions by plan, but supports long-lived state via pause/resume; paused sandboxes can preserve full state beyond the continuous runtime window.
Best For: Teams building agents focused on code execution and testing with continuous sessions under 24 hours, particularly those needing Firecracker-level isolation and cold starts.
4. Blaxel
Blaxel positions itself as a perpetual sandbox platform for AI agents, with a focus on persistent "agent computers" that stay on standby and resume on demand.
Core Capabilities
- Resume from standby: Sandboxes remain on automatic standby rather than being destroyed, enabling resume when agents need them
- Persistence across standby and resume: Blaxel sandboxes can preserve filesystem, process, and memory state across standby and resume; unlimited persistence is available on higher quota tiers, while Starter quotas enforce TTLs
- MicroVM isolation: Hardware-level security for running untrusted AI-generated code
- Volume storage: Persistent storage that survives sandbox destruction and recreation
- Template support: Reusable sandbox templates for standardized agent environments
Security and Compliance
Blaxel offers SOC 2 Type II, HIPAA BAA, and ISO 27001 certifications, providing comprehensive compliance coverage for enterprise deployments.
Architecture Approach
Blaxel emphasizes persistent state over ephemeral execution. The platform recommends treating sandboxes as persistent computers that retain context over time, benefiting agents that need continuity across workflows instead of clean-room execution on every task.
Best For: Teams building coding agents requiring persistent sandbox environments, resume from standby, and comprehensive compliance certifications.
5. Fly.io Sprites
Fly.io Sprites delivers persistent Linux VMs with checkpoint/restore capabilities designed specifically for AI agent workloads that need to maintain state across sessions.
Core Capabilities
- Unlimited session duration: VMs can run indefinitely without time constraints
- Checkpoint/restore: Save and restore VM state for pause/resume cycles
- Global edge deployment: Fly supports deployment across its documented global hostable regions for agent interactions worldwide
- Firecracker isolation: Hardware-level VM isolation for secure code execution
- Checkpoint and wake: Sprites support checkpoint/restore and wake from hibernation, as noted in a third-party report
Architecture Approach
Sprites are positioned as persistent VMs rather than ephemeral containers, designed for AI agents that need to preserve state between invocations. Sprites are designed for AI agent and coding workflows, including Claude Code-style persistent coding environments.
Best For: Individual developers and teams using Claude Code or similar agentic coding tools that need persistent Linux VMs with global edge deployment.
6. Daytona
Daytona provides persistent development environments with Docker compatibility and strong editor integration.
Core Capabilities
- Sysbox container runtime: Docker-compatible container isolation for flexible environment configuration
- Configurable persistence: Sandboxes can be configured for extended runtime with auto-stop after inactivity
- Editor integration: Daytona supports editor-oriented workflows, including VS Code and browser access and a VS Code extension
- Open-source foundation: Self-hosting available with the open-source GitHub repository, which has 72k+ stars
- GPU-backed options: Daytona lists GPU-backed sandbox options, including H100 and RTX PRO 6000 configurations
Security and Compliance
Daytona offers SOC 2 Type I certification with HIPAA support. The platform supports on-premises deployment for organizations with data residency requirements.
Architecture Approach
Daytona focuses on workspace continuity, maintaining state across sessions with emphasis on developer experience through editor integration. The Docker-compatible runtime enables use of standard container images.
Best For: Development teams that prioritize editor integration and Docker compatibility, particularly those standardizing on containers for agent development workflows.
7. Runloop
Runloop provides sandbox infrastructure with integrated AI benchmarking capabilities, designed for teams evaluating and fine-tuning coding agents.
Core Capabilities
- Configurable session lifetimes: Runloop Devboxes have configurable maximum lifetimes and support suspend/resume with disk-state preservation; public docs do not support unlimited continuously running sessions
- Integrated benchmarking: SWE-bench and other evaluation frameworks built into the platform
- MicroVM isolation: Secure execution environment for untrusted code
- VPC deployment option: Deploy sandboxes within your own virtual private cloud
- Enterprise SDKs: Python and TypeScript SDKs for programmatic Devbox, Blueprint, and benchmark management
Security and Compliance
Runloop reports SOC 2 Type II and GDPR compliance, and HIPAA-eligible architecture with BAA availability for eligible workloads, with VPC deployment for additional data isolation.
Architecture Approach
Runloop combines sandbox execution with agent evaluation tooling, enabling teams to run benchmarks alongside production workloads. The platform supports both development iteration and production deployment.
Best For: Teams actively evaluating and fine-tuning coding agents who need integrated benchmarking alongside secure sandbox execution.
Why Modal Stands Out for Long-Running Agent Sessions
Purpose-Built AI Infrastructure
Modal's architecture addresses the core requirements of stateful agent sandboxes through purpose-built infrastructure. The platform's AI-native container runtime, optimized filesystem behavior, and scheduling controls are built for the unique demands of AI agent workloads: secure code execution, dynamic scaling, and efficient state preservation through Volumes and filesystem snapshots.
Massive Concurrency Without Compromise
While some platforms cap concurrent sessions at 1,100, Modal's sandbox infrastructure supports 100k+ concurrent sandboxes with fast startup times. This scale enables enterprise deployments where hundreds or thousands of agents operate simultaneously without capacity constraints.
GPU Access When Agents Need It
Long-running agents often need to call upon GPU acceleration for ML inference, code analysis, or model fine-tuning. Modal offers the broadest GPU lineup among the platforms covered in this guide, from T4 for lightweight inference through H200 and B200 for large-scale computation. Agents can dynamically request GPU resources as workloads demand, without pre-provisioning.
Efficient State Recovery with Snapshots and Volumes
For Modal Functions, Memory Snapshots capture CPU memory, with alpha GPU Memory Snapshots additionally capturing GPU state, reducing initialization-heavy cold starts. For long-running Modal Sandboxes, state is preserved through Volumes and filesystem snapshots, which persist indefinitely until deleted. Sandbox memory snapshots can clone full sandbox state and are currently in alpha, so filesystem snapshots and Volumes are the recommended primitives for agents with heavyweight dependencies or model loading requirements.
Enterprise Security and Compliance
Production agent deployments require enterprise-grade security. Modal maintains SOC 2 Type II certification, supports HIPAA-compliant workloads via BAA on Enterprise plans, and implements comprehensive security practices including gVisor sandboxing, TLS 1.3, and encryption at rest. The security documentation details vulnerability remediation SLAs and shared responsibility models.
Developer Experience Without Configuration Overhead
Modal takes a code-first approach, with SDKs in Python, TypeScript, and Go for creating Sandboxes, calling Modal Functions, and managing resources. Code running inside a sandbox is not limited to a single language; a sandbox can run whatever runtime or language the workload requires. These SDKs eliminate YAML configuration files and infrastructure management overhead. Teams define sandbox environments, compute requirements, and scaling behavior directly in code, enabling rapid iteration on agent architectures. The platform handles container builds, scheduling, and auto-scaling automatically.
Production-Proven at Scale
Modal powers cloud infrastructure for over 10,000 teams, including AI companies building production agent systems. Lovable uses Modal Sandboxes as preview environments for generated apps and websites, and Ramp runs background coding agents on Modal Sandboxes that generate code changes and write them back into commits or pull requests. This track record demonstrates the platform's ability to handle enterprise-scale agent workloads reliably, from prototype through production deployment.
For teams building AI agents that require secure stateful execution, persistent state through Volumes and filesystem snapshots, broad GPU access, and enterprise compliance, Modal's combination of AI-native infrastructure and proven scale makes it the clear choice for long-running agent sessions.
