MicroVMs vs gVisor vs containers for AI agents
For untrusted, model-generated code from many users, microVMs (Firecracker, Cloud Hypervisor, Kata) offer the strongest common boundary because each sandbox gets its own kernel. gVisor is a strong middle ground that keeps container ergonomics. Plain containers are fastest and simplest but share the host kernel, so they are best reserved for trusted code or as one layer among several.
At a glance
| Approach | Boundary | Startup | Compatibility | Typical use |
|---|---|---|---|---|
| MicroVM (Firecracker, Cloud Hypervisor) | Hardware virtualization; separate guest kernel | Sub-second, faster with snapshots | Full Linux | Multi-tenant agent sandboxes |
| Kata Containers | VM per pod behind container APIs | Sub-second to seconds | Full Linux | Self-hosted on Kubernetes |
| gVisor | User-space kernel intercepts syscalls | Container-like | Most Linux software; some syscall gaps | Hardened containers, Kubernetes |
| Containers (runc) | Namespaces, cgroups, seccomp; shared kernel | Fastest | Full Linux | Trusted code, dev environments |
| V8 isolates / Wasm | Language runtime | Milliseconds | JS/Wasm only | Small tool calls and snippets |
MicroVMs
A microVM runs a real guest kernel inside a minimal virtual machine. An attacker who gains root inside the guest still has to break out of the hypervisor — a much smaller and better-audited surface than the full Linux system-call interface. Firecracker, built by AWS for Lambda, is the reference design; it supports snapshot/restore, which providers use to deliver fast starts, pause/resume and forking.
Trade-offs: microVMs need hardware virtualization (bare metal or nested virtualization), which constrains where you can self-host them, and GPU passthrough is harder than with containers.
gVisor
gVisor implements Linux system calls in a memory-safe user-space kernel, so container workloads talk to gVisor instead of the host kernel. It runs anywhere containers run — no nested virtualization required — and integrates with Docker and Kubernetes through the runsc runtime.
Trade-offs: extra overhead on system-call- and I/O-heavy workloads, and occasional compatibility gaps for low-level tools.
Plain containers
Containers are the default packaging format for software, and every sandbox ultimately runs some image. As an isolation boundary on their own, though, they rely on the shared host kernel; real-world escapes such as runc CVE-2019-5736 and CVE-2024-21626 show why most multi-tenant platforms add a layer. Containers remain a reasonable choice for single-tenant, trusted workloads — for example a coding agent on your own laptop, ideally combined with a restricted network.
Isolates and WebAssembly
V8 isolates and WebAssembly runtimes start in milliseconds and are excellent for running small functions or tool calls. They are not a general-purpose Linux machine: there is no shell, apt or arbitrary binary. Use them when your agent emits short JavaScript or Python (via Pyodide) snippets, not when it needs to build and test a real project.
Which should you pick?
- Serving untrusted users at scale → microVM-based provider or self-hosted Firecracker/Kata.
- Already on Kubernetes, need stronger-than-container isolation without nested virt → gVisor.
- Single developer, trusted repository, local agent → containers or OS-level sandboxing with network restrictions.
- Tiny, frequent snippets → isolates or Wasm.
The directory lists the isolation technology each provider documents.
Frequently asked questions
Is Firecracker more secure than gVisor?
Why are microVM sandboxes so fast now?
Sources
Cite this page
Agent Sandboxes. (2026). MicroVMs vs gVisor vs containers for AI agents. agentsandboxes.org. Last reviewed November 8, 2026. https://agentsandboxes.org/guides/microvm-vs-gvisor-vs-containers/
Found an error or a missing sandbox? Submit a correction — every entry links to the sources it relies on.