Top AI sandbox platforms in 2026, ranked

AI sandbox platforms give agents somewhere to execute code, install packages, edit repositories, and run tools without handing them unrestricted access to your application infrastructure. A sandbox might run a coding agent, an API, a browser session, or a GPU task. The platform needs to start those environments quickly, isolate their execution, and handle concurrent demand.

Compare startup latency, concurrency, isolation, and workload support first, then persistence and cost. The recommendations below reflect those requirements; the numbered order is not a measured performance ranking.

An AI sandbox platform creates and manages isolated environments for running AI-generated or other untrusted code. Your application uses an API or SDK to provision an environment, execute commands, collect results, and stop or delete it.

For a coding agent, that environment might contain a repository, shell, compiler, test runner, and development server. For a data analysis agent, it might contain Python and uploaded files. Browser automation, reinforcement learning environments, and GPU workloads can require different images and resources.

Isolation limits what executing code can affect. Your application still controls which credentials, files, and network destinations it receives; placing a production credential inside a sandbox does not make that credential safe to misuse.

Compare isolation, usable startup time, concurrency, runtime compatibility, persistence, and the cost of the complete task. These determine whether a platform fits your application more reliably than a single startup-time claim.

• Isolation: Identify the runtime used for your specific configuration. A microVM has a separate guest kernel; gVisor mediates system calls through a userspace kernel. GPU and CPU environments may use different boundaries.
• Startup: Measure the time until your agent can execute its first useful command, including image preparation and dependencies. Separate cold starts from snapshot resumes.
• Concurrency: Check account quotas, creation-rate limits, and how quickly capacity becomes available during a burst.
• Runtime: Test your actual tools, including browsers, native packages, container tooling, or GPU libraries.
• Persistence: Establish whether a stop preserves files, memory, both, or neither. A disk snapshot is not necessarily a resumable process.
• Access and operations: Check outbound restrictions, private connectivity, secret handling, logs, and automated cleanup.

For sensitive workloads, use the AI agent sandbox security checklist to turn these requirements into concrete controls.

The platforms differ in the sandbox runtimes they provide, the workloads those runtimes support, and how environments are created, scaled, and preserved between tasks.

These six platforms cover code execution APIs, coding-agent workspaces, and environments for more demanding workloads. Each profile focuses on what you can run and the constraints that affect a production integration.

Northflank is an AI sandbox platform for running coding agents, generated code, APIs, browser automation, and CPU or GPU workloads in isolated environments. Create and control sandboxes through its API, use your own container images, and run tasks concurrently with configurable resources.

Capabilities include:

• Fast sandbox provisioning and concurrent execution for parallel agent tasks and isolated workers.
• Custom container images for code interpreters, coding agents, APIs, browsers, and their dependencies.
• MicroVM isolation through Kata Containers and Cloud Hypervisor, with gVisor used for GPU sandboxes and configurations where nested virtualisation is unavailable.
• Persistent volumes and container filesystem snapshots for supported Kata workloads.
• Bring your own cloud (BYOC), where Northflank acts as the control plane managing Kubernetes and deployments while sandbox workloads run inside your cloud account and VPC.

Northflank targets sub-second microVM boot times and supports thousands of concurrent agent sessions. Boot time is one part of readiness: your image, dependencies, resource requirements, and available capacity also affect when a task can begin.

Run short-lived sandboxes for individual tasks or retain files on persistent volumes for work that continues across sessions. Both can run on Northflank’s managed cloud or inside your own cloud account and VPC.

Best for: Teams and developers running AI agents, code execution, isolated APIs, or browser automation, from individual tasks to concurrent sandbox fleets.

Pricing and limits: Free, Pay-as-you-go, and Enterprise plans cover evaluation through production, including an always-on free tier for development and testing. Northflank Cloud compute is billed per second, with CPU at $0.01667/vCPU-hour and memory at $0.00833/GB-hour; GPUs, storage, and transfer are additional. Enterprise options include volume discounts and 24/7 support.

Trade-off: GPU types and capacity depend on region. Choose the runtime and location together when your agent needs both GPU access and a particular isolation boundary.

E2B provides isolated environments that applications can create and control through SDKs. Its template-based workflow suits agents that repeatedly need the same tools and dependencies for code execution.

Capabilities include:

• Firecracker isolation for executing untrusted code.
• Templates for preparing the environment before an agent task starts.
• Programmatic command execution and filesystem access.

Best for: Teams adding code execution to an agent product and building around an SDK-driven sandbox lifecycle.

Pricing and limits: Hobby includes trial credit and supports 20 concurrent sandboxes with one-hour sessions. Pro costs $150/month plus usage, includes 100 concurrent sandboxes, and supports 24-hour sessions; concurrency add-ons increase the limit. Enterprise offers custom capacity and longer-lived environments.

Trade-off: Long-running or highly concurrent tasks can require a different plan or additional capacity. Design retries and task checkpoints around the limits of the plan you actually use.

Modal Sandboxes runs dynamically defined environments for code execution and agent tasks. It fits teams that want these environments alongside Modal's broader machine learning and GPU workflows.

Capabilities include:

• Environments built from custom images, including images pulled from registries.
• VM and gVisor runtime options, with GPU support on gVisor sandboxes.
• Network blocking and outbound domain or IP allowlists.

Best for: Teams combining agent code execution with Python services, data processing, or GPU workloads.

Pricing and limits: Sandbox CPU costs $0.00003942 per physical-core-second, where one physical core represents two vCPUs. Memory costs $0.00000667/GiB-second. Billing uses the greater of requested and actual resource usage. GPU resources and other applicable usage add to the bill.

Trade-off: The runtime affects hardware support: a GPU sandbox requires gVisor. Also account for Modal's physical-core billing unit when comparing it with providers quoting per-vCPU rates.

Daytona provides programmable development environments for agents that need files, commands, dependencies, and running applications. Its sandbox types extend beyond Linux containers to VM and GPU environments, including Windows VM options.

Capabilities include:

• APIs and SDKs for creating environments and executing development tasks.
• Multiple sandbox types for different operating-system and hardware requirements.
• Network access controls and lifecycle options for stopping, pausing, or archiving eligible environments.

Best for: Teams building coding agents that need development environments with different runtimes or operating systems.

Pricing and limits: Pay-as-you-go CPU is $0.0504/vCPU-hour and memory is $0.0162/GiB-hour, with storage and specialised resources charged separately. Trial credits and custom Enterprise options are available.

Trade-off: Lifecycle behaviour varies by sandbox type. VM pause, container archive, and GPU stop are different operations; for example, stopping a GPU sandbox deletes it. Build cleanup and recovery around the type you select.

Together Code Sandbox provides environments for agent tool calls, generated code, and reinforcement learning rollouts. Its current together-sandbox workflow creates repeatable environments from Docker images or Dockerfiles.

Capabilities include:

• Image snapshots built through a remote image builder.
• Python and TypeScript SDKs, plus a CLI.
• Command execution and file operations inside running sandboxes.

Best for: Teams integrating code execution into Together AI applications and workflows.

Pricing and limits: Listed Code Sandbox rates are $0.0446/vCPU-hour and $0.0149/GiB-hour. Confirm the applicable terms when enabling the current SDK for your organisation; Code Interpreter's separate per-session pricing is for a different product.

Trade-off: Access to the current SDK and CLI requires organisation enablement. Together also retains a legacy SDK, so choose the integration before building around its lifecycle APIs.

Vercel Sandbox gives agents isolated Linux environments for executing commands, modifying files, and running applications. It connects naturally to Vercel projects while also supporting access from external systems.

Capabilities include:

• Firecracker microVMs and custom OCI images stored in Vercel Container Registry.
• Persistent sandboxes that save state on stop, plus snapshot support.
• CLI, JavaScript, and Python interfaces for automating agent tasks and previews.

Best for: Teams building coding-agent workflows and application previews alongside Vercel deployments.

Pricing and limits: Hobby includes a limited allowance with 45-minute sessions and 10 concurrent sandboxes. Pro and Enterprise support sessions up to 24 hours with a default quota of 10,000 concurrent sandboxes. Usage includes active CPU, allocated memory, and applicable storage, creation, and transfer charges.

Trade-off: A persistent environment does not remove the maximum running-session duration. Long tasks still need to fit within that limit or resume their work across sessions.

AI sandbox costs depend on allocated resources, billable runtime, storage, and platform fees. Compare the total cost of completing a task, including time spent waiting for a model or external service.

Prices are in USD, checked on 9 October 2026, before applicable taxes. These are resource-rate examples, not equivalent complete sandbox configurations. GB and GiB, physical cores and vCPUs, allocated resources and active CPU billing are different units and models. Plan fees, minimum allocations, GPUs, storage, and transfer can change the total.

For a useful estimate, record your average task duration, peak concurrency, memory allocation, CPU activity, and retained data. Then include idle environments and failed attempts. A sandbox that waits for model responses can have a different bill from one compiling code continuously, even when both exist for the same amount of time.

Test how quickly your own workload becomes usable at your expected concurrency, rather than treating a vendor's fastest startup as a universal result.

Use the same image, region, resource allocation, and readiness check for each candidate. Run separate tests for a new image, a cached image, and a resumed environment. Record median, P95, and P99 readiness times, successful starts, timeouts, and quota errors.

Also distinguish creating many sandboxes sequentially from requesting a large burst. A fast single-environment result does not establish how the platform behaves when thousands of users start tasks together. Test cleanup too: environments should stop consuming compute when the application is finished with them.

The right sandbox depends on the code being executed and the access it needs. These questions help narrow that choice.

Choose a sandbox that runs your repository's tools, isolates each task, and preserves the state your workflow needs. Northflank supports concurrent agent tasks, custom runtime images, and CPU/GPU sandboxes; E2B fits SDK-based execution; Modal, Daytona, Together, and Vercel offer other runtime and development workflows. Test a real build and test suite before selecting one.

A standard container shares the host kernel, so it is not the same isolation boundary as a microVM. For untrusted code, evaluate a hardened runtime such as gVisor or a VM-based sandbox alongside network restrictions, scoped credentials, and resource limits. The appropriate boundary depends on what the code can access and the consequences of an escape.

Northflank, Modal Sandboxes, and Daytona offer GPU sandbox options. GPU availability, isolation, regions, and lifecycle differ: Northflank GPU sandboxes use gVisor, Modal requires its gVisor runtime for GPUs, and Daytona distinguishes GPU environments from its other sandbox types.

Northflank supports bring your own cloud (BYOC): Northflank acts as the control plane managing Kubernetes and deployments, while sandboxes run inside your cloud account and VPC. Select the supported runtime and network configuration for that cluster. A provider offering private connectivity to your VPC is not necessarily placing its sandbox compute inside it.

Only if the chosen lifecycle and storage configuration preserve them. Persistent volumes, filesystem snapshots, and memory snapshots serve different purposes. On Northflank, use persistent volumes for files that must survive a sandbox pause or restart; do not rely on an ephemeral filesystem remaining intact.

Choose Northflank for isolated code execution, coding agents, APIs, and browser automation that need configurable environments and concurrent CPU or GPU execution. Choose another option where its SDK, runtime, lifecycle, or existing ecosystem fits your agent more closely. Validate the decision with one representative task, then test concurrent execution and recovery before increasing traffic.

Use these guides for a closer look at isolation and code execution:

• Best microVM sandboxes for AI coding agents
• Best code execution APIs for AI agents
• Persistent vs ephemeral AI sandboxes