Wading through thousands of alerts every day? It’s like trying to spot a flare in the middle of a fireworks show.
For DevOps teams and SREs, the constant flood of alerts—most of them noise—leads to something called observability fatigue. You stop noticing the important stuff. Your brain tunes it all out. And that’s when things break.
The real kicker? Monitoring was supposed to help us avoid downtime. Not drown us in distractions.
The Problem: It’s Bigger Than You Think
Let’s talk scale.
Some large orgs report more than 100,000 alerts a day. That’s about:
- 4,100 alerts every hour
- 69 alerts every minute
- More than one per second
Buried in that mess? One critical alert that gets ignored. The result? Not just slower response times—but real burnout. Engineers stop trusting the system. They tune out, mute channels, or worse—miss the stuff that actually matters.
When Too Much Monitoring Goes Sideways
Example 1: The Alert Avalanche
One global e-commerce company set up redundant alerts for every microservice. Their goal? Catch issues early.
Instead, they triggered 200,000 alerts in a single day. A postmortem showed 98% were false positives—mostly low-priority stuff like CPU spikes or container restarts.
The cost? Engineers spent that week triaging garbage instead of shipping features. Productivity dropped 60%. Just like that.
Example 2: The Tool Soup
A fintech startup thought more tools meant better coverage. So they added Grafana, Datadog, CloudWatch, Prometheus… all at once.
Each tool sent alerts to email, Slack, and SMS. Within three weeks, they were neck-deep in 50,000 redundant alerts. Many were duplicates. Some had unclear severity. Most weren’t helpful.
Eventually, the devs started muting channels or disabling tools altogether. Which defeats the entire point of observability.
From Chaos to Clarity: A 4-Step Framework
So how do you fix this? Here’s a straightforward approach that actually works.
1. Sort Your Alerts by Priority
Not everything deserves a 3 a.m. page. Separate the noise from the must-know-now stuff.
A simple Bash example:
#!/bin/bash
# Categorize alerts by type
for alert in $(cat alerts.txt); do
case $alert in
*critical*) echo "Critical: $alert" >> critical_alerts.log ;;
*warning*) echo "Warning: $alert" >> warning_alerts.log ;;
*info*) echo "Info: $alert" >> info_alerts.log ;;
*) echo "Uncategorized: $alert" >> uncategorized_alerts.log ;;
esac
done
Of course, in production you’ll want real tooling—Datadog monitors, Prometheus rules, or Alertmanager routing. But the logic’s the same: keep the high-priority stuff loud and clear.
2. Know What Actually Matters
Start with service-level objectives (SLOs). Use the Four Golden Signals:
- Latency
- Traffic
- Errors
- Saturation
Don’t alert on everything. Alert on symptoms users will notice. A high CPU spike? Maybe not a big deal. But increased page load time? That’s worth waking up for.
3. Group the Noise, Kill the Clones
Use tools that can group alerts or suppress known false alarms. Saves your brainpower—and your on-call engineer’s sanity.
Examples:
- PagerDuty can deduplicate alerts and manage escalation.
- Prometheus + Alertmanager lets you group alerts with labels and silence flapping ones.
4. Make Alerts Smarter
Alerts should come with context. Not just “Something broke”, but:
- What broke
- Where it happened
- Maybe even why
Here’s a Terraform example that tells you when an EC2 instance is running hot:
resource "aws_cloudwatch_metric_alarm" "high_cpu" {
alarm_name = "High CPU Alarm"
comparison_operator = "GreaterThanThreshold"
evaluation_periods = 1
metric_name = "CPUUtilization"
namespace = "AWS/EC2"
period = 60
statistic = "Average"
threshold = 80
alarm_description = "Triggers when CPU exceeds 80% on EC2 instance."
dimensions = {
InstanceId = aws_instance.my_instance.id
}
alarm_actions = [aws_sns_topic.my_alert_topic.arn]
}
No more guessing games during incidents.
Use the Right Tools—But Not All of Them
You don’t need every tool under the sun. You need the right ones, set up the right way.
Here’s a smart combo:
- Prometheus + Alertmanager – Custom metric alerts with routing logic
- Grafana – Dashboards that actually help you monitor SLOs
- Datadog – Great for anomaly detection and finding patterns
- PagerDuty – Keeps your incident process tight and tidy
- Slack – But only for the alerts you actually need to see
The trick? Don’t let your tools multiply alerts. Integrate them wisely.
Final Thought: Clarity Over Quantity
Observability isn’t about seeing everything. It’s about seeing the right things at the right time.
When your alerts make sense, your team responds faster. Trust in the system comes back. Burnout goes down. And real issues get fixed quicker.
Because let’s be honest—too much noise is just as dangerous as silence.