WendyOS vs. balena: Fleet Containers, Now Built for Physical AI

Want to compare them hands-on? Install Wendy below, then run wendy run.
If you've researched deploying containers to a fleet of edge devices, you've met balena. It's the platform that popularized the idea: a container-based host OS (balenaOS), a cloud for managing devices (balenaCloud), and a balena push workflow. It's mature, well-documented, and a genuinely good fit for a lot of IoT.
WendyOS shares balena's core thesis - containers + fleets + updates-like-a-phone - but is built for a different center of gravity: robots and Physical AI. If your devices have GPUs, cameras, ROS 2 nodes, and actuators, the differences below matter.
The short version
| Concern | balena | WendyOS |
|---|---|---|
| Container fleet model | Yes - the category creator | Yes - same OCI images |
| Primary focus | General IoT / connected devices | Robotics & Physical AI (GPU, vision, ROS 2) |
| GPU (CUDA / TensorRT) | Works, but not the focus | First-class; declared entitlement, NVIDIA userspace provided |
| ROS 2 / DDS tooling | Not built in | Built in (wendy device ros2 …, Foxglove bridge) |
| Build & deploy loop | balena push builds in the cloud, then pulls | Builds locally, streams only changed layers over USB-C / Wi-Fi |
| Works with no internet | Local mode exists; cloud is the default path | Yes - deploy over a USB-C cable in host mode |
| Hardware access | Commonly privileged containers | Declarative, code-signed entitlements (least privilege) |
| Licensing | balenaOS open; balenaCloud is a paid product | Apache 2.0 open source; Wendy Cloud in preview |
Where the two align
Credit where due: balena got a lot right, and WendyOS agrees with it.
- Containers are the right unit for shipping edge apps. Both use OCI images; both let you bring a
Dockerfile. - Fleets need a management plane and remote updates that don't require SSHing into each box.
- Updates should be atomic and safe, not in-place
apton a device you can't reach.
If your workload is a general IoT app - a gateway, a kiosk, a sensor hub on a Raspberry Pi - balena is a proven, mature choice and you'll be well served.
Where WendyOS diverges - and why
1. It's built for robotics hardware, not general IoT
WendyOS treats the GPU, cameras, and robotics buses as first-class. GPU access is a declared entitlement that provisions the NVIDIA userspace for CUDA/TensorRT workloads; camera (V4L2), audio, I2C, SPI, GPIO, and serial are all declarable. On balena you can reach this hardware, but you're often doing it through privileged containers and manual device configuration - it isn't the platform's focus.
2. ROS 2 and DDS are built in
Physical AI usually means ROS 2. WendyOS ships DDS-aware networking plus CLI introspection - list nodes and topics, echo messages, record and download bags, and bridge a live ROS 2 graph into Foxglove Studio over the device connection. balena has no robotics-specific tooling; that layer is yours to assemble.
3. A local build-and-stream loop, offline-friendly
balena's default path is balena push: your code goes to balenaCloud, gets built there, and the device pulls the result. It's clean, but it's a cloud round-trip. WendyOS builds on your machine and streams only the changed layers to the device over USB-C or Wi-Fi - no cloud build step, and it works with no internet at all (a direct USB-C cable in host mode). On a bench or a field site, that's the difference between iterating and waiting.
4. Least-privilege hardware access
Instead of privileged containers, WendyOS uses declarative, code-signed entitlements:
{
"appId": "yard-inspector",
"entitlements": [
{ "type": "gpu" },
{ "type": "camera" },
{ "type": "network", "mode": "bridge" }
]
}The runtime grants exactly those devices - a deny-all model with explicit allow rules - so an app can't quietly reach the rest of the machine.
5. Apache 2.0, end to end
balenaOS is open, but balenaCloud is a commercial product with usage tiers. WendyOS is Apache 2.0 open source - the OS, the agent, and the CLI - with Wendy Cloud as an optional managed layer (in preview). If avoiding platform lock-in matters to you, that's a real difference.
When balena is the better fit
If you're shipping general-purpose IoT - no GPU, no ROS 2, no robotics I/O - and you want a mature, batteries-included cloud with a large ecosystem today, balena is an excellent, battle-tested choice. WendyOS is the stronger pick when your fleet is robots, cameras, and AI accelerators, when you need the offline local loop, or when you want an Apache-2.0 foundation.
Frequently asked questions
Can I migrate a balena project to WendyOS?
Largely, yes - both run OCI containers, so your Dockerfile carries over. You'd swap balena push for wendy run, and replace privileged device access with declared entitlements in wendy.json.
Does WendyOS have a cloud like balenaCloud?
Wendy Cloud (in preview) provides fleet reach, remote deploys, and telemetry, with devices dialing out over mutual TLS - no inbound ports. The core OS/agent/CLI are usable fully offline without it.
Is WendyOS only for NVIDIA Jetson?
No. It's a flashed image for supported boards (Jetson, Raspberry Pi today), and wendy-agent brings the same workflow to existing x86/ARM64 Linux machines with no OS swap.
Keep comparing: WendyOS vs. Docker + ROS 2 · WendyOS vs. Ubuntu + JetPack · WendyOS vs. Mender / RAUC. Or install the CLI and try it.
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WendyOS is the open-source operating system for Physical AI — deploy your apps to NVIDIA Jetson, Raspberry Pi, and more in seconds, over USB-C, wireless, or the cloud.