Which should you use, Kata Containers or Firecracker?
It depends on whether your unit is a Kubernetes pod or a whole machine. If you want Firecracker sandboxes for agents without building the platform, Runtime gives each sandbox its own microVM from one API call. Pick Kata Containers if you already run Kubernetes and want pods in VMs, with little more than a RuntimeClass. Use QEMU under it for GPUs or confidential computing. Pick Firecracker directly to build and run your own platform of machines.
Different layers of the same stack
The two are not rivals so much as layers. Firecracker's own README lists Kata Containers as one of the container runtimes it has been integrated into.
Firecracker is the machine. It is a Rust virtual machine monitor that uses Linux KVM to run microVMs, one process per microVM, with what its site counts as "only 5 emulated devices". You drive it through a RESTful API. Give it a kernel, a root filesystem and boot arguments, and it starts the guest. It knows nothing about container images or Kubernetes.
Kata Containers is the container runtime. It describes itself as "lightweight virtual machines that seamlessly plug into the containers ecosystem." Its shim, containerd-shim-kata-v2, speaks the containerd and CRI-O interfaces, so Kubernetes schedules pods as usual, selected with a RuntimeClass. For each pod, Kata starts a VM with its own guest kernel. It runs a kata-agent inside the VM to manage the containers, and it shares the container's root filesystem into the VM with virtio-fs. The VM itself comes from a hypervisor Kata supports: Cloud Hypervisor, Firecracker, QEMU, Dragonball or StratoVirt.
Side by side
| Kata Containers | Firecracker | |
|---|---|---|
| What it is | An OCI container runtime that runs pods in lightweight VMs | A virtual machine monitor on KVM |
| Isolation boundary | Hardware virtualization, from the hypervisor it is configured with | Hardware virtualization through KVM |
| Kernel | A guest kernel per pod | A guest kernel per microVM |
| Unit | A Kubernetes pod or container | A microVM from a kernel and a root filesystem |
| Interface | containerd shim v2, CRI-O, Kubernetes RuntimeClass | A RESTful API per Firecracker process |
| Hypervisors | Cloud Hypervisor, Firecracker, QEMU, Dragonball, StratoVirt | Is the hypervisor |
| Start time | No figure published; depends on the hypervisor | Up to 125 ms to guest init, in the project's specification |
| Memory overhead | No figure published; depends on the hypervisor | Up to 5 MiB for the VMM on a 1 vCPU, 128 MiB guest |
| GPUs | Through QEMU, which Kata calls best supported for NVIDIA GPUs | Not supported, per Kata's hypervisor table |
| Architectures | x86_64, aarch64, ppc64le, s390x | x86_64, aarch64 |
| Language and licence | Rust and Go, Apache 2.0 | Rust, Apache 2.0 |
| Who uses it | Baidu, for function computing and container instances | AWS Lambda and Fargate; E2B, Vercel Sandbox, Runtime |
Firecracker's figures come from its specification, measured on AWS metal hosts with a minimal guest. They cover the monitor alone. Kata says it delivers "consistent performance as standard Linux containers," but it publishes no single start or memory figure on its site.
When each fits
- Pick Kata Containers when you already run Kubernetes and want some pods, or all of them, in VMs without changing images or manifests beyond a RuntimeClass. Choose QEMU under it when you need GPUs or confidential computing (Intel TDX, AMD SEV-SNP), which Kata's table lists only for QEMU.
- Pick Firecracker directly when you are building a platform whose unit is the machine, not the pod: functions, sandboxes, per-user environments. You get the smallest device model and a monitor built for many small tenants. You write the orchestration yourself.
- Pick Kata with Firecracker for Kubernetes pods on Firecracker's minimal device model, if its feature set covers your workloads.
Runtime Cloud
Runtime gives you the result without the cluster. Every sandbox is its own Firecracker microVM, created by one API call.
A sandbox is a whole machine an agent can install into, pause and fork, not a pod in a cluster. Runtime runs one Firecracker microVM per sandbox on dedicated servers it operates:
- Its own kernel and disk. Ubuntu 24.04, Python 3.12, Node.js 24, Bun, git
and gcc are installed.
sudoworks, and Docker runs inside aftersudo enable-docker. - Controls outside the guest. Network rules, CPU, memory and cost are enforced on the host, so root in the sandbox cannot change them (security).
- Whole-machine state. A paused sandbox keeps files, memory and running processes while you have credit. A fork makes 1 to 100 running copies with memory and processes.
- Images from your Dockerfile. Any Dockerfile or container image becomes a sandbox image, built in its own Firecracker guest (images).
Runtime sandboxes run on CPUs. For GPU work, Kata with QEMU or a GPU provider is the better fit.
TypeScriptimport { Sandbox } from "withruntime";await using sbx = await Sandbox.create();const run = await sbx.exec("uname -r"); // this sandbox's own kernelconsole.log(run.stdout);Pythonfrom withruntime import Sandboxwith Sandbox.create() as sbx: run = sbx.exec("uname -r") # this sandbox's own kernel print(run.stdout)A new account gets 100 free hours of a 1 GB machine in its first 30 days, with no card:
Terminalnpx withruntime sandboxes run -- uname -rMore: what is Firecracker?, what is a microVM?, Firecracker vs Docker, Firecracker vs gVisor.
Run npx withruntime login to start. See the migration guide for moving code from another sandbox provider.
Sources
Checked 26 October 2026.
- Kata Containers, repository, architecture, hypervisors
- Firecracker, README, specification
- Provider isolation from Runtime's comparisons, checked 25 October 2026: E2B, Vercel Sandbox
Facts on this page were checked on 26 October 2026.