Alert Noise Score Calculator
Score how noisy an alerting setup is from false-positive and repeat-alert ratios.
About Alert Noise Score Calculator
Noise score = (false_positives + auto_resolved) / total_alerts × 100. Both categories represent alerts that didn't require the human response an alert implies: false positives never reflected a real problem, and auto-resolved alerts fixed themselves before anyone acted. A high noise score is the leading driver of alert fatigue — responders start ignoring or delaying response to a channel that's frequently wrong or self-resolving.
Formula
noise_score = (non_actionable_alerts / total_alerts) × 100
- non_actionable_alerts
- Alerts that required no action (false positives, auto-resolved)
- total_alerts
- Total alerts fired in the period
Worked example
With the default values below, Alert Noise Score Calculator returns Alert Noise Score of 40.0%. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Total Alerts Firedaccepts 1–10,000,000 alerts
- 500 alerts
- False Positivesaccepts 0–10,000,000 alerts
- 80 alerts
- Auto-Resolved Without Actionaccepts 0–10,000,000 alerts
- 120 alerts
Results
- Alert Noise Score
- 40.0%
- Rating
- High noise — significant alert fatigue risk
- Actionable Alert Rate
- 60.0%
- Noise Alerts
- 200 alerts
Step by step
- 1Noise alerts: false positives + auto-resolved= 200 alerts
- 2Noise score: noise / total × 100= 40.0%
- 3Rating= High noise — significant alert fatigue risk
Assumptions and limits
- The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax or fees or charges into account.
- Figures are rounded for display to 1 decimal place, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
Is a 0% noise score achievable or even desirable?
It's rarely achievable in practice — some legitimate transient issues will always auto-resolve before action is needed. Most well-tuned alerting systems land in the 5-15% range rather than exactly zero.
What's the difference between a false positive and an auto-resolved alert?
A false positive never corresponded to a real problem at all (a monitoring bug, a bad threshold, a flapping check), while an auto-resolved alert reflected a real but transient condition that fixed itself — both are noise from a responder's perspective, but they call for different fixes.
How do I reduce auto-resolved alert volume?
Increase the `for:` duration so transient conditions don't cross the firing threshold, or add a secondary condition requiring sustained severity, so only conditions that would matter to a human actually notify one.
How do I reduce false positives specifically?
Audit alert rules against actual incident postmortems — if an alert rule has fired many times with zero corresponding incidents, either its threshold is miscalibrated or it's not actually measuring something that matters.