End-to-end latency of serving jobs in distributed and shared environments, such as a Cloud, is an important metric for jobs' owners and infrastructure providers. Yet it is notoriously challenging to model precisely, since it is affected by a large collection of unrelated moving pieces, from the software design to the job schedulers strategies. In this work we present a novel approach to modeling latency, by tracking how it varies with CPU usage. We train a classifier to automatically assign the latency behavior of methods in three classes: constant latency regardless of CPU, uncorrelated latency and CPU, and predictable latency as a function of CPU. We use our model on a random sample of serving jobs running on the Google infrastructure. We illustrate unexpected and insightful patterns of latency variations with CPU. The visualization of latency-CPU variations and the corresponding class may be used by both jobs' owners and infrastructure providers, for a variety of applications, such as smarter latency alerting, latency-aware configuration of jobs, and automated detection of changes in behavior, either over time, during pre-release testing, or across data centers.