This is a cross-post. Original on our site: stellarbytecapital.com/blog/gvisor-firecracker-docker-ai-sandbox
Once you've decided to run AI-generated code in a sandbox, the next question is which sandbox. The three names you'll keep hitting are Docker, gVisor, and Firecracker. They're not competing products so much as three different strengths of boundary — and picking the wrong one either leaves you exposed or costs you performance you didn't need to spend.
Here's how they actually differ, and how to choose.
The one thing that separates them: where the boundary is
All three isolate code, but at different layers — and that layer determines how hard the boundary is to break:
- Docker (runc) — standard containers. Isolation comes from Linux namespaces, cgroups, and seccomp. The container shares the host kernel. Fast and universal, but a kernel exploit crosses the boundary.
- gVisor (runsc) — a user-space kernel from Google. It intercepts the container's syscalls and services them in a sandboxed process instead of passing them straight to the host kernel. That's a second wall between the code and the real kernel.
- Firecracker — a microVM built on KVM hardware virtualization. Each sandbox is a real (tiny) virtual machine with its own guest kernel, booting in ~125ms. This is the strongest boundary of the three — it's what AWS Lambda and Fargate use to isolate tenants.
Comparison at a glance
| Docker (runc) | gVisor (runsc) | Firecracker | |
|---|---|---|---|
| Boundary | Shared host kernel | User-space kernel intercepts syscalls | Hardware-virtualized microVM |
| Escape resistance | Weakest | Strong | Strongest |
| Startup | Fastest (tens of ms) | Fast | ~125ms boot |
| Runtime overhead | Near-zero | Syscall-heavy workloads pay a cost | Low, plus a small memory floor per VM |
| Compatibility | Full | Some syscalls unimplemented | Full (real kernel in guest) |
| Complexity | Lowest | Low (drop-in runtime) | Higher (VM lifecycle, images) |
| Used by | ~Everyone | Google Cloud Run, GKE Sandbox | AWS Lambda, Fargate |
Docker: the baseline, not the answer for untrusted code
A hardened one-shot Docker container — non-root, dropped capabilities, seccomp on, read-only filesystem, no network by default — is a perfectly reasonable sandbox for internal tools where the blast radius is contained. It's the fastest and simplest option.
Its weakness is structural: the container shares the host kernel. One kernel vulnerability, and "isolated" code is on the host. If you're running genuinely untrusted or multi-tenant code, Docker alone is a bet on the kernel being flawless.
gVisor: a second kernel wall, at a compatibility cost
gVisor slots in as a container runtime (runsc), so it's close to a drop-in upgrade. Instead of your code's syscalls hitting the host kernel directly, they hit gVisor's user-space kernel first. An attacker now has to break gVisor and the host kernel.
The tradeoffs: gVisor doesn't implement every syscall, so some workloads break, and syscall-heavy programs pay a performance tax. For sandboxed Python doing data analysis or API calls, it's usually fine. For exotic low-level code, test compatibility first.
Firecracker: real isolation, real VM lifecycle
Firecracker gives each execution its own microVM with a separate guest kernel. Because the boundary is hardware virtualization, it's the strongest of the three — an escape has to break out of a VM, not just a namespace. It boots in ~125ms and strips the device model down to almost nothing.
The cost is operational: you're managing VMs, guest images, and a memory floor per instance. If you're multi-tenant, handling other people's data or money, or running adversarial code, that cost buys you isolation you can actually defend.
So which should you use?
- Internal tool, your own code/data → hardened one-shot Docker. Fast, simple, good enough.
- Untrusted code, moderate risk → gVisor. A big jump in isolation for little operational change; just verify syscall compatibility.
- Multi-tenant, real money/data, adversarial input → Firecracker (or Kata Containers). Pay the VM tax; you'll want the hardware boundary.
The isolation tech is one decision. The policies around it — disposable per-run, default-deny egress, read-only mounts, quotas — matter just as much, whichever runtime you pick.
We're Xingyao Byte — building secure AI-execution layers, quant trading systems, and payment platforms. Remote, async-first. More at stellarbytecapital.com
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