Best Stateful Sandboxes for Long-Running Agent Sessions in 2026
AI agents tackling multi-day tasks need more than ephemeral containers that vanish after each invocation. Stateful sandboxes preserve filesystem state, memory, and running processes between sessions, enabling agents to work on complex projects without rebuilding their environment from scratch. Choosing the right sandbox platform determines whether your agents can maintain context across hours or weeks, run in large numbers without performance degradation, get the CPU and memory that long sessions need without overprovisioning, and execute untrusted code securely at scale. This guide examines seven stateful sandbox solutions for long-running agent sessions in 2026, starting with Modal, a scalable, high-performance sandbox platform for executing AI-generated code at production scale.
Key Takeaways
1. Modal
Modal is a scalable, high-performance sandbox platform for executing AI-generated code, built for teams running agents at production scale. Its custom-built infrastructure runs dynamically defined secure AI agent sandboxes with high concurrency, automatic CPU and memory bursting, and consistent fast starts under load. It scales to up to 1M concurrent sandboxes, with on-demand GPU access when long-running agents need acceleration. As of October 2026, Modal reports having run 3 billion sandboxes.
Core Capabilities
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 VM and gVisor sandboxing for compute isolation, TLS 1.3 for public APIs, and encryption for data in transit and at rest. Modal supports region-pinning. Modal documents vulnerability remediation SLAs and publishes a detailed security guide covering application, corporate, and infrastructure security practices.
Why Teams Choose Modal for Long-Running Agent Sessions
2. Northflank
Northflank provides persistent sandbox infrastructure with unlimited session duration and self-serve BYOC (bring your own cloud) deployment across multiple cloud providers.
Capabilities
Considerations
Why Teams Choose Northflank
Northflank is a broad application platform, and sandboxes make up just one part of what it runs. Teams choose Northflank for long-running agent sessions when they want those sandboxes running inside their own cloud account next to the rest of their application stack.
3. E2B
E2B provides sandboxes for AI agents, focused on ephemeral code execution with Firecracker microVM isolation.
Capabilities
Considerations
Why Teams Choose E2B
E2B is a dedicated sandbox provider with an open-source core and bring-your-own-cloud options, and it reports 1 billion sandboxes run in total. Teams choose E2B as a sandbox point solution when their agent sessions fit within its plan-based runtime and concurrency limits.
4. Blaxel
Blaxel is a sandbox platform for AI agents built around persistent "agent computers" that stay on standby and resume on demand.
Capabilities
Considerations
Why Teams Choose Blaxel
Blaxel is a purpose-built sandbox provider whose design centers on keeping sandboxes in persistent standby. Teams choose Blaxel when they want each agent's sandbox to sit on standby between sessions and come back with its prior state intact.
5. Fly.io Sprites
Fly.io Sprites provides persistent Linux microVMs with checkpoint/restore, designed for AI agent workloads that need to maintain state across sessions.
Capabilities
Considerations
Why Teams Choose Fly.io Sprites
Fly.io Sprites are persistent microVMs offered as part of Fly.io's infrastructure. Teams choose Sprites when they want one long-lived Linux machine per developer or coding agent rather than a large fleet of concurrent sandboxes.
6. Daytona
Daytona offers isolated sandboxes and development environments built around persistence.
Capabilities
Considerations
Why Teams Choose Daytona
Daytona is a purpose-built sandbox provider that ships with persistence and offers fast starts when running at lower scale. Teams choose Daytona as a point solution for stateful coding agents whose session counts don't call for high concurrency.
7. Runloop
Runloop provides sandbox infrastructure with integrated AI benchmarking capabilities, designed for teams evaluating and fine-tuning coding agents.
Capabilities
Considerations
Why Teams Choose Runloop
Teams choose Runloop when they want agent benchmarking infrastructure that comes pre-built with their sandboxes. Its sandboxes are set up more like DevBoxes than ephemeral sandboxes.
Why Modal Stands Out for Long-Running Agent Sessions
Massive Concurrency Without Compromise
Modal has demonstrated creating 1M+ sandboxes in under a minute, exceeding 10,000 sandbox creations per second, and its sandbox infrastructure supports up to 1M concurrent sandboxes with consistent startup times. Modal regularly runs tens of thousands of concurrent customer sandboxes and reports running 3 billion sandboxes as of October 2026. This scale enables enterprise deployments where hundreds or thousands of agents hold sessions open simultaneously without capacity constraints.
CPU and Memory Bursting
Long-running agent sessions are uneven: an agent can spend most of an hour waiting on model responses, then install packages, compile a project, or run a full test suite in a few minutes. Under fixed-reservation pricing, every sandbox has to be sized for those heaviest minutes and held at that size for the whole session, so much of the paid capacity sits unused. Modal sandboxes start from a configured CPU and memory baseline and burst above it automatically when a step needs more, so teams can provision for typical usage rather than peak usage. Billing is per second at the greater of requested and actual resources, and bursting also reduces the risk of an out-of-memory failure cutting a long session short.
Purpose-Built AI Infrastructure
Modal's architecture addresses the core requirements of long-running agent sandboxes through purpose-built infrastructure. The platform's AI-native container runtime, optimized filesystem behavior, and custom scheduler keep sandbox starts sub-second and consistent under heavy concurrent load, including with large custom images. The same infrastructure provides the secure code execution, dynamic scaling, and state preservation through Volumes and filesystem snapshots that AI agent workloads require.
Efficient State Recovery with Snapshots and Volumes
For long-running Modal Sandboxes, state is preserved through Volumes, filesystem snapshots, and directory snapshots. Sandbox timeouts are configurable up to 24 hours, and work that runs longer continues in a new Sandbox restored from a filesystem snapshot. 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. For Modal Functions, Memory Snapshots capture CPU memory, with alpha GPU Memory Snapshots additionally capturing GPU state, reducing initialization-heavy startup latency.
GPU Access When Agents Need It
Most long-running agent sessions are CPU-based, but some need to call upon GPU acceleration for ML inference, code analysis, or model fine-tuning. Modal's GPU sandboxes support full sandbox functionality across a GPU lineup of 10+ types, from T4 for lightweight inference through H200, B200, and B300 for large-scale computation. Agents can request GPU resources as workloads demand, without reservations.
Code-First Development for Agent Sessions
Modal provides code-first SDKs in Python, TypeScript, and Go. Code running inside a sandbox is not limited to a single language; a sandbox can run whatever runtime or language the workload requires. Teams define sandbox environments, compute requirements, and scaling behavior directly in code, enabling rapid iteration on agent architectures. The platform handles container builds, scheduling, resource bursting, and auto-scaling automatically.
Production-Proven at Scale
Modal powers cloud infrastructure for over 10,000 teams, including AI companies building production agent systems. Ramp runs background coding agents on Modal Sandboxes that generate code changes and write them back into commits or pull requests, and Imbue orchestrates hundreds of isolated coding agents on Modal. This track record demonstrates the platform's ability to handle enterprise-scale agent workloads reliably, from prototype through production deployment.
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 as well as region-pinning, and implements comprehensive security practices including VM and gVisor sandboxing, TLS 1.3, and encryption at rest. The security documentation details vulnerability remediation SLAs and shared responsibility models.
For teams building AI agents that require secure execution at high concurrency, automatic CPU and memory bursting, and consistent startup under load, with GPU access when workloads need it, Modal's combination of AI-native infrastructure and proven scale makes it the clear choice for long-running agent sessions.
A stateful sandbox is an isolated compute environment that preserves filesystem state, installed dependencies, and execution context between sessions. Unlike ephemeral containers that reset after each invocation, stateful sandboxes enable AI agents to work on multi-day tasks without losing progress. This persistence is critical for agents tackling complex projects that span hours or weeks, such as codebase refactoring, data processing pipelines, or iterative research tasks.
Modal isolates compute with VM sandboxes and gVisor sandboxes, adding a layer between workloads and host systems as part of a defense-in-depth approach. The platform implements TLS 1.3 for all API traffic, encrypts data in transit and at rest, supports region-pinning, and maintains SOC 2 Type II certification. For regulated industries, Modal supports HIPAA-compliant workloads via Business Associate Agreements on Enterprise plans.
Yes. Modal's sandbox infrastructure handles CPU-based code execution at scale, with automatic CPU and memory bursting for CPU-intensive steps like builds and test suites. For GPU-accelerated work, Modal's dedicated inference and training products let agents serve models and fine-tune them on the same platform. Sandboxes can also attach GPUs when workloads require acceleration, with GPU options spanning 10+ types from T4 through B200 and B300, allowing agents to dynamically access the compute resources they need for ML inference, model fine-tuning, or compute-intensive analysis.
Purpose-built AI infrastructure eliminates configuration overhead and operational complexity. Modal's AI-native container runtime, optimized filesystem behavior, and scheduling controls are built for agent workloads, providing high concurrency, automatic CPU and memory bursting, consistent fast starts under load, state preservation through Volumes and filesystem snapshots, and elastic scaling without manual capacity management. Teams define everything in code through Python, TypeScript, and Go SDKs rather than managing Kubernetes clusters.
Modal supports HIPAA-compliant workloads on Enterprise plans through Business Associate Agreements. The platform implements security controls including VM and gVisor sandboxing, encryption in transit and at rest, TLS 1.3 for APIs, region-pinning, and vulnerability remediation SLAs. The security documentation details the shared responsibility model for compliant deployments.
Session duration determines how long an agent can work in one continuous run and how it carries its environment into the next one. Modal Sandbox timeouts are configurable up to 24 hours; work that runs longer continues in a new Sandbox restored from filesystem snapshots, allowing agents to resume without rebuilding the full environment.
Modal bills on active resources per second, charging the greater of the CPU and memory a sandbox requests and what it actually consumes. A sandbox can burst past its requested CPU and memory automatically, which lowers the chance that an out-of-memory error ends a long session partway through. That means a team can request a sandbox sized for an agent's typical load across the session and still reach peak load during heavy steps. Most sandbox providers bill on reserved resources and cap usage at the reservation, so teams running long sessions have to reserve the largest size they might need for the entire session and pay for capacity that sits idle most of the time.
