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For user-facing reliability paging, alert on whether customers are experiencing failures and how quickly those failures are consuming the service’s error budget. CPU utilization is useful diagnostic evidence—and can justify a narrowly targeted preventive alert near a hard limit—but it is not, by itself, proof that users are being harmed.
Why error-budget alerts are a better paging signal
A CPU threshold describes an internal condition. High utilization may precede a problem, but it does not necessarily mean the service is failing for users; conversely, users can experience failures while CPU remains ordinary. Google’s incident-management guidance puts the distinction plainly: “Alerts should be based on end-to-end measures of customer/client experience, not based on a system’s internal behavior.” Google SRE’s alerting guidance recommends symptom-based alerting because internal metrics may not map reliably to impact and can become fragile as implementation changes.
An SLI-based alert instead measures a user-visible property—such as successful requests or latency against a threshold—and asks whether the service is meeting the objective promised by its SLO. When the SLI shows that failures are consuming the available error budget unusually quickly, the alert conveys both impact and urgency. CPU and other diagnostic metrics still matter; they help responders investigate why the symptom is occurring.
What an error budget and burn rate mean
An error budget is the failure a service can tolerate while still meeting its SLO over its measurement period. Its meaning depends on the SLI: for an availability objective, the budget represents permitted unavailability; for another objective, it corresponds to the allowed shortfall in that measurement. Google’s SRE guidance gives a 99.99% availability target as an example, which permits 0.01% unavailability. Google SRE’s availability table
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Burn rate expresses how quickly the service is spending that budget relative to the SLO. A burn rate of 1 would use the full budget over the SLO window if the observed rate continued. A faster rate exhausts it sooner. For example, Google’s SRE Workbook explains that with a 99.9% SLO over 30 days, a burn rate of 1 corresponds to a 0.1% error rate and 30 days to exhaust the budget; a burn rate of 10 corresponds to a 1% error rate and three days to exhaust it. Google SRE Workbook: Alerting on SLOs
How to set up SLO burn-rate alerts
- Define the promise. Choose a user-facing SLI and an SLO with a stated measurement window. Define which requests or events count; the denominator and the failure condition must reflect the service’s actual promise.
- Calculate budget consumption. Measure the fraction of the SLO’s error budget consumed over a window, then relate that rate to the full SLO period. A burn rate makes clear whether the current error rate would exhaust the budget slowly or rapidly.
- Use more than one window and urgency threshold. A fast window can catch acute incidents; a slower window can identify sustained degradation. Google’s workbook gives example paging baselines of 2% of budget consumed in one hour and 5% in six hours, plus a ticket baseline of 10% in three days. These are starting examples, not universal standards; tune them to traffic, service behavior, and on-call capacity.
- Choose the response route. Page when immediate intervention is warranted, create a ticket when work is needed but can wait days, and retain information in logs when no immediate response is required. The notification should tell the responder which SLI and SLO are affected, the relevant window, and where to inspect the service dashboard.
- Keep impact and diagnosis together. Put SLI and SLO views prominently on the service dashboard so responders can confirm customer impact. Keep CPU, saturation, and other diagnostic metrics available for investigation; a symptom alert identifies that something is wrong, not necessarily why.
Choose pages, tickets, and logs deliberately
| Signal or route | What it tells the team | Typical response | Main caution |
|---|---|---|---|
| SLI/SLO burn-rate page | User-visible reliability is deteriorating and budget is being consumed at a consequential rate. | Immediate investigation and action when the threshold is crossed. | Thresholds and windows need tuning to the service; a copied baseline may not suit its traffic. |
| Slower budget-consumption ticket | Degradation merits work, but is not currently urgent enough to interrupt the on-call responder. | Schedule and address within the team’s response horizon. | Do not route a signal that needs immediate action to a ticket. |
| CPU or other internal-metric alert | An internal condition may be developing; near a hard limit, it may predict abrupt failure. | Investigate or intervene when a specific failure mode is imminent; otherwise use it diagnostically. | Utilization alone does not establish customer harm, so broad threshold paging can be noisy or miss failures elsewhere. |
| Log or passive metric | Useful context or a condition that does not require immediate response. | Review during investigation or routine analysis. | Do not rely on passive records for a condition that needs timely action. |
Google’s alerting guidance allows preventive internal alerts when a hard resource quota is close enough to cause an abrupt failure. That is a targeted exception, not a reason to make every elevated CPU reading page-worthy. Tie such an alert to a concrete risk, such as an imminent capacity ceiling, and decide whether the available response time makes paging necessary.
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Adapt burn-rate alerts for low-traffic services
Short-window error ratios can look severe when request counts are small. Google’s workbook notes that one failed request in an hour with only 10 requests produces a 10% hourly error rate. That ratio may represent a meaningful user failure, but it can also make a copied high-volume threshold behave badly during quiet periods.
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When setting alert logic for a low-volume service, inspect the count behind the ratio as well as the percentage. Account for normal quiet periods and how quickly a small number of failures can move the SLI. Decide whether the resulting signal is sufficiently actionable for a page or better routed to a ticket; do not assume high-traffic thresholds transfer unchanged.
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What a burn-rate alert cannot explain
An SLO dashboard can show that an objective is being violated without identifying the cause. Google’s SRE guidance recommends keeping service data available for diagnosis alongside the SLI view. Google SRE Workbook: Monitoring A CPU graph belongs in that investigation: it can help determine whether resource pressure contributed to the customer-visible symptom, but it should not replace the symptom signal.
Likewise, a budget alert is not a complete monitoring strategy. Teams still need to decide which user journeys the SLI represents, what degree and duration of budget consumption deserves interruption, and which preventive limits could fail abruptly. The alert is useful when its evidence points to a customer-relevant condition and its route matches the time available to respond.
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