Alert Fatigue: When Good Monitoring Starts Working Against You
Nandan Jadhav
One of the biggest misconceptions about observability is that more alerts mean better monitoring.
In reality, I’ve found the opposite to be true.
Over the years, I’ve seen environments with hundreds of carefully configured monitors that still struggle to identify what actually matters. The problem isn’t a lack of monitoring—it’s that engineers gradually become conditioned to ignore alerts because too many of them don’t require meaningful action.
This phenomenon is known as alert fatigue, and it doesn’t happen overnight. It develops slowly as teams lose confidence in the signals they’re receiving.
Story 1: The Disk Space Alert Nobody Wants to Act On
A common example is disk space utilization.
A typical enterprise monitor might be configured with conservative thresholds:
- Warning: 85%
- Critical: 90%
At first glance, this seems perfectly reasonable.
As utilization crosses these thresholds, alerts are generated and the operations team reaches out to the customer. Surprisingly, the response is often not one of urgency.
Sometimes the affected server is a non-production system. Sometimes the customer already knows the disk is filling up but doesn’t consider it important enough to act on immediately. In cloud environments, they may even prefer to postpone expanding storage until it’s absolutely necessary to avoid unnecessary costs.
This creates an awkward situation.
The monitoring team now has to repeatedly follow up with the customer to determine the next step.
Should the disk be expanded?
Or should the monitoring threshold be adjusted?
Neither option is ideal.
Increasing storage increases infrastructure cost and the additional capacity may never actually be used.
Changing the threshold introduces another risk—what if the next genuine disk issue is detected too late because the alert was relaxed?
Over time, these recurring conversations begin to feel routine rather than urgent.
The alert still works technically, but its perceived importance gradually decreases.
Story 2: The CPU Alert That Everyone Ignores
Another pattern I’ve observed is short-lived CPU spikes.
A monitor detects CPU utilization crossing the configured threshold.
Notifications are sent.
Engineers acknowledge them.
Then… nothing happens.
Within a few minutes the CPU returns to normal.
Eventually people begin recognizing the pattern.
“It’ll probably settle down.”
“It’s just the nightly backup.”
“The scheduled job will finish in a few minutes.”
Without realizing it, engineers begin filtering these alerts mentally before even investigating them.
This is dangerous.
The next CPU alert may actually represent a genuine production issue, but because previous alerts rarely required intervention, the response becomes slower.
The monitoring system hasn’t failed.
Human behaviour has adapted to noisy monitoring.
Story 3: The Midnight Server Restart
One situation that creates unnecessary stress is an unexpected server restart.
Monitoring detects:
- System rebooted
- System uptime reset
- Availability interruption
The on-call engineer immediately assumes something serious has happened.
Emails are sent.
Calls are made.
Teams start investigating.
Eventually someone replies:
“This was a planned restart.”
Perhaps application patches were applied over the weekend.
Perhaps infrastructure maintenance was scheduled.
Perhaps someone simply forgot to notify the operations team.
Nothing was actually wrong.
But valuable engineering time was still spent responding to what appeared to be an incident.
Repeated experiences like this slowly reduce trust in monitoring alerts.
The Real Problem Isn’t the Alert
Notice something common across all three examples.
The alerts themselves weren’t incorrect.
The monitoring platform did exactly what it was configured to do.
The problem was that the alerts lacked operational context.
Monitoring systems are excellent at detecting technical conditions.
They are not always capable of determining whether those conditions require immediate human action.
That’s where process, communication, and governance become just as important as technology.
Reducing Alert Fatigue
Over time, I’ve found a few principles that help improve the quality of monitoring.
Every alert should require an action
If an engineer receives an alert, there should be a clear expectation of what to do next.
If nobody intends to act, the alert should be reconsidered.
Review monitors regularly
Infrastructure changes.
Applications evolve.
Thresholds that made sense two years ago may no longer be appropriate today.
Monitoring shouldn’t be treated as “configure once and forget.”
Understand recurring patterns
If CPU spikes occur every night because of scheduled jobs, perhaps the monitoring strategy needs refinement instead of simply accepting constant alerts.
Recurring behaviour deserves investigation rather than normalization.
Improve communication
Many unnecessary incidents can be avoided if planned maintenance, application deployments, and infrastructure changes are communicated to the teams responsible for monitoring.
Technology alone cannot solve communication gaps.
Final Thoughts
Alert fatigue is rarely caused by a single poorly configured monitor.
It is usually the result of many small compromises that accumulate over time.
One unnecessary notification doesn’t seem significant.
Neither do ten.
But after hundreds of alerts that never require meaningful action, engineers naturally begin questioning every notification they receive.
That is when monitoring quietly starts working against the very people it was designed to help.
A monitoring platform should not strive to generate the maximum number of alerts.
It should strive to generate the minimum number of alerts required to ensure the right people take the right action at the right time.
In observability, quality of signal will always matter more than quantity of alerts.