Case studies
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.
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.
Performance method and numbersEnrollment, cordon, drain with real container relocation and explicit placement pinning were run with two real agent processes, each with its own Docker daemon.
Multi-node quickstartLevelrail 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.
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.
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](/performance) 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.
Run it, measure it, and tell us what you find.
