Prometheus usually feels simple at the beginning.
Scrape metrics.
Build dashboards.
Create alerts.
Everything works.
But as infrastructure grows, observability problems often start showing up in places teams don’t expect.
High-cardinality metrics.
Inefficient queries.
Poor alerting practices.
Uncontrolled metric growth.
The frustrating part is that most of these problems stay invisible until the monitoring system starts slowing down.
In many cases, Prometheus problems aren’t caused by the platform itself.
They’re usually caused by small decisions that accumulate as monitoring systems grow.
Good observability usually comes from metric design and alert quality, not from adding more dashboards.
I found this breakdown useful because it highlights several common Prometheus mistakes and explains how they impact observability as systems scale:
<a href="https://www.kubeblogs.com/6-things-you-are-doing-wrong-in-prometheus/" target="_blank" rel="noopener nofollow">https://www.kubeblogs.com/6-things-you-are-doing-wrong-in-prometheus/</a>

Divyansh

Prometheus monitoring problems rarely stem from the platform itself but from small design decisions that accumulate over time. Common pitfalls include high-cardinality metrics, inefficient queries, poor alerting practices, and uncontrolled metric growth. These issues stay invisible until the monitoring system degrades. Good observability depends on thoughtful metric design and alert quality rather than adding more dashboards.

Most Prometheus Problems Start Long Before Dashboards Break