Reliability measurement for LLM inference
When your application depends on a hosted model API, that provider’s reliability under load is a production dependency. Percentes measures it the hard way: open-loop load with coordinated-omission-correct timing, every scheduled request accounted for as completed, errored, or censored, Kaplan–Meier completion curves for the requests that never finish, and a client that must prove it wasn’t the bottleneck before any number counts.
The instrument is open source and the methodology is pre-registered — every published number cites the exact commit and configuration that produced it, so you can re-run it rather than trust it.
- Writing — measurement notes and teardowns, published as they happen
- Methodology — what the numbers mean and what would invalidate them
- The instrument — Go, Apache-2.0