Skip to main content
Measured results, stated honestly Levelrail is pre-release and has no customer logos yet, so instead of testimonials this page shows what has actually been measured and verified, and what has not. Run your own test | View on GitHub

​
What has been verified

Footprint as apps grow

In the idle benchmark, going from 0 to 500 suspended apps moved resident memory from about 68 MB to about 91 MB and CPU from 0.03% to 1.6%. It ran on an Apple M4 Max with a development build and no containers running, so expect different numbers on a Linux VPS. The method is reproducible.

Multi-node against real daemons

Enrollment, cordon, drain with real container relocation and explicit placement pinning were run with two real agent processes, each with its own Docker daemon.

​
What is not proven yet

Levelrail has no stable release, and single node is the best tested path. The WireGuard mesh and cross-host remote transport beyond the join flow are still unverified, there is no Linux release-build idle measurement yet, and public ACME has one live run with renewal and wildcards unverified. The feature status page lists the evidence behind each area. Fewer real-world deployments also means less community knowledge than projects that have existed for years.

​
Be the first case study

If you run Levelrail on a real workload, we would like to hear what broke and what worked. Open a discussion or an issue on GitHub, and if you are happy to be named, we will write it up with your numbers.

​
At a glance

  • 68 MB: RSS at 0 apps, dev build
  • 91 MB: RSS at 500 suspended apps
  • 1.6%: CPU at 500 suspended apps
  • 500: apps in the idle benchmark

​
Frequently asked questions

Because the project is pre-release and has no public production customers yet. This page only shows measurements and verifications that exist.
From a documented benchmark script (scripts/bench-idle.sh) you can run yourself. The performance page lists the method, the machine and the caveats.
They were not measured against other platforms here, so this page makes no comparative claim. Measure your own setup with the same method if you want a comparison.

​
Add your results

Run it, measure it, and tell us what you find. Run your own test | View on GitHub
Last modified on October 12, 2026