Best microVM sandboxes for AI coding agents in 2026

A microVM sandbox is an execution environment inside a lightweight virtual machine with its own guest operating-system kernel. Hardware virtualization separates that guest from the host, giving agent-generated code a different boundary from an ordinary container sharing the host kernel.

The container image and the isolation mechanism are separate choices. A service can accept your Docker image and run it inside a microVM. Conversely, a container running inside a shared host VM does not necessarily get a separate VM boundary of its own.

gVisor takes another approach: it implements an application kernel that mediates system calls. It is a sandboxing technology, but it is not a microVM. The microVM versus gVisor comparison explains that architecture decision in more detail.

A sandbox also serves a different role from the agent harness. The harness coordinates the agent's tools and work; the sandbox constrains the environment executing code. You can select them separately.

Compare the configuration that supplies the VM boundary, then evaluate repository compatibility, execution location, retained state, and access to resources outside the sandbox. These recommendations are organized by workload fit rather than a single performance ranking.

Docker also provides cloud sandboxes; its profile here evaluates the local option.

The following platforms support different coding-agent workflows behind a microVM boundary, from API-managed task execution to persistent workspaces and local Docker environments.

Northflank provides isolated sandboxes for coding agents and applications that need to execute generated or untrusted code. Managed CPU sandboxes use Kata Containers with Cloud Hypervisor for microVM isolation; managed GPU sandboxes use gVisor. Teams can run repository builds, test suites, scripts, APIs, and other tool-driven tasks in configured environments, rather than limiting execution to short code snippets.

• Custom coding environments: A coding agent can work with files, execute shell commands, install dependencies, run repository tests, and start a development server. Custom container images let teams supply the language runtimes, system packages, and build tools required by each repository. JavaScript and Python clients support creating sandboxes and executing commands through an application.
• Startup and concurrency: On Northflank Cloud, CPU sandboxes use microVM isolation, boot in under a second, and support thousands of concurrent sessions.
• Runtime and deployment: CPU sandboxes use microVMs; GPU sandboxes use gVisor. With bring your own cloud (BYOC), Northflank manages Kubernetes for sandboxes in your cloud account and VPC. MicroVM execution requires a supported runtime and eligible hardware; installing a runtime alone does not isolate workloads. Orchestration metadata and external model-provider requests are separate from sandbox execution.
• Files and pause: Attached volumes retain files across a pause. Pausing a sandbox stops running processes and discards ephemeral files, so save required repository changes and artifacts to attached storage.

Best for: Coding agents that need custom environments to run repository builds, tests, scripts, and APIs across many concurrent tasks.

E2B provides Firecracker sandboxes that applications can create and manage for agent tasks. Its SDKs and templates fit coding workflows that need a prepared environment for repository changes and tests.

Python and JavaScript/TypeScript SDKs connect applications to the environment. Normal pause preserves filesystem and memory state; filesystem-only pause reboots on resume, retaining files but not processes. Network clients still need to reconnect.

Continuous runtime limits also affect task design: Hobby sessions run for up to one hour, while Pro sessions run for up to 24 hours. Enterprise plans provide additional deployment and session options, including execution in your own VPC.

Best for: Coding-agent applications that need programmatic microVM management and reusable execution state.

Vercel Sandbox runs agent-generated code in Firecracker microVMs. It fits application-building agents that need to install dependencies, build a repository, and expose a development server.

Command and file operations, managed or custom images, outbound firewall policies, and credential brokering support the build-and-preview workflow.

Sandboxes are persistent by default: stopping a session saves the filesystem, and resuming starts a new session from that saved state. Treat this as filesystem continuity when designing recovery for development servers and other processes.

The maximum running session is 45 minutes on Hobby and 24 hours on Pro and Enterprise. Those limits apply to individual sessions, rather than the entire lifetime of the persistent sandbox. Include retained snapshot storage when estimating cost.

Best for: Agents that turn repository changes into working application previews inside a managed microVM environment.

Fly.io Sprites provides persistent Linux environments in dedicated microVMs for agents that return to the same workspace.

Files and installed tools survive idle periods. Each Sprite also has an HTTP URL for services running inside it, with CLI and programmatic access for interactive or application-driven work.

Filesystem checkpoints can restore an earlier workspace, but they do not reverse external actions such as a Git push or database update.

Best for: Coding agents that reuse a Linux workspace and benefit from filesystem recovery between tasks.

Docker Sandboxes gives local coding agents a microVM with its own Linux kernel and Docker daemon, useful when a task needs to build images or run Docker Compose.

Workspace modes range from direct host-directory mounts to private clones or mountless workspaces. Direct mounts expose the selected host files to the agent.

Packages and in-sandbox files persist until removal. A host-side proxy applies network policies. Local execution requires supported hardware virtualization; Docker-managed cloud mode has separate credentials, policies, and lifecycle controls.

Best for: Local repository work where an agent needs both a separate guest kernel and isolated access to Docker.

Run a representative repository task and check that the VM boundary, build tools, and access policies work in your actual configuration.

For example, fix a failing integration test that requires a package install and development server. Check:

1. Isolation allocation: Identify what receives its own VM: the sandbox, task, tenant, or agent. Multiple agents inside one VM still share that guest kernel, even if separate Linux users restrict file access.
2. Build compatibility: Run the actual toolchain. Include native packages, browsers, Docker, or filesystem mounts if your repository needs them. A guest kernel does not guarantee every device or privileged operation is exposed.
3. Boundary crossings: Inspect mounted directories, credentials, reachable services, and preview URLs. Test an allowed connection and one your policy should block. VM isolation does not restrict an intentionally permitted API action.
4. Recovery: Interrupt the task, then return to it. Check the patch, installed dependencies, test results, and server state separately. Saved files and saved memory support different recovery paths.

Northflank's sandbox quickstart provides a baseline for environment creation and command execution. Save required artifacts on attached storage before testing pause and resume.

For a deeper review of credentials, outbound access, and tenant separation, use the AI-agent sandbox security checklist.

Start with the coding tasks and state your workflow needs. Northflank sandboxes are the default recommendation for teams running repository builds, tests, scripts, and APIs across many concurrent tasks in custom environments.

E2B is a fit when reusable execution state drives the integration. Vercel Sandbox fits application builds and previews. Fly.io Sprites suits an ongoing Linux workspace, while Docker Sandboxes suits local work requiring an isolated Docker daemon.

If your team needs a persistent remote workspace for the coding agent itself, Northflank Cloud Harnesses provide that environment. A Harness is distinct from the sandbox API, which creates isolated execution environments for code and tasks; it is not included in this microVM comparison.

These answers distinguish the VM boundary from the rest of the agent workflow.

No. Firecracker is a virtual machine monitor, not a complete coding-agent service. Products such as E2B and Vercel Sandbox add environment, network, storage, and lifecycle management around Firecracker-based execution.

An ordinary Docker container shares its host kernel. Docker Sandboxes is a separate product that places local agents inside microVMs. Docker image support alone does not establish a VM boundary.

No. gVisor mediates system calls through an application kernel; it does not provide a separate guest kernel in a microVM.

Not on its own. A microVM constrains execution, but an agent can still misuse files, credentials, or network connections it is permitted to access.

It depends on the trust boundary. Reusing a sandbox can retain a repository and avoid repeated setup. Mutually untrusted tasks that require separate VM boundaries need separate sandboxes, with appropriate storage and credentials. Creating separate processes inside one microVM does not provide that separation.

Use these guides for the adjacent decisions:

• Best sandboxes for coding agents
• Best code execution APIs for AI agents
• Persistent versus ephemeral AI sandboxes
• MicroVM versus gVisor