Our first monitoring implementation provided data we never used. Alerts fired randomly, dashboards showed metrics that did not correlate with actual problems, and we abandoned it after 4 months. The second attempt worked because we changed our evaluation criteria completely.

Positive Changes

Focusing on 8 core metrics instead of tracking everything reduced alert fatigue by 85%. We now get 2 to 3 meaningful alerts weekly instead of 40+ daily noise notifications. Response time tracking tied directly to user complaints gave us a 0.91 correlation coefficient.

Simplified dashboards meant our entire team could interpret data without training. Cost per prediction metrics justified infrastructure upgrades with specific ROI calculations. The platform caught a memory leak that was degrading performance by 15% weekly.

Ongoing Problems

Historical comparison tools struggle with our seasonal traffic patterns. The system flags normal December usage spikes as anomalies every year. Custom model types require manual instrumentation that breaks with framework updates.

Cross-model performance comparison remains misleading because context windows and task complexity vary too much. We spent 60 hours building workarounds for multi-region latency aggregation.

What Made the Difference

We stopped trying to monitor everything and focused on metrics that predicted actual failures. The platform itself matters less than knowing exactly which 6 to 10 signals indicate your specific failure modes. Document those before evaluating any tool.