Skip to main content
Now that we’ve covered preparation, let’s focus on one of the most critical tools during incidents: alerts. Not all alerts are equal. Some wake you up at 3 AM for no reason; others only trigger once customers are already furious. Effective alerts wake you up for a real problem — not noise. In this article we’ll cover what makes an alert actionable, how to prioritize alert types, using SLOs as an alerting foundation, routing and escalation, and a practical implementation checklist. What makes an alert effective? Good alerts share several key attributes:
  • They require human intervention — if no one needs to act, they shouldn’t page.
  • They focus on specific, user-impacting problems rather than only background metrics.
  • They are concise and include enough context for an on-call engineer to triage quickly.
  • They are routed to the proper owner and include links to dashboards and playbooks.
A presentation slide titled "Alert Design Principles" with a target and several arrows on the left. On the right are four actionable alerting criteria: requires human intervention, specific and precise, contains clear context, and has a defined owner.
An alert that only says “CPU usage is high on server prod-api-03” is noisy and non-actionable. An actionable alert names the service impact, quantifies the problem, and points to remediation resources.
A slide titled "Alert Design Principles" showing two example alerts: a purple "Non-Actionable" alert saying "CPU usage high on server prod-api-03" and an orange "Actionable" alert describing "Order Processing API latency exceeding 2s SLO (current 5.2s) — affecting checkout flow; see dashboard and playbook [links]."
Actionable example summary: “Order Processing API latency is exceeding the 2 s SLO; current p95 = 5.2 s; checkout flow affected; see dashboard and playbook.” This tells the on-call engineer what is broken, who is affected, and where to look. Alert types: symptom-based vs cause-based Alerts generally fall into two categories:
  • Symptom-based alerts: measure user-facing behavior — latency, error rates, availability. These map directly to business impact and tell you what is broken for users.
  • Cause-based alerts: track implementation or infrastructure signals such as CPU, memory, or disk. They suggest why something might fail but don’t necessarily indicate immediate user impact.
Both have a role, but they serve different purposes and should be used intentionally.
A presentation slide titled "Symptom-Based vs Cause-Based Alerting" that compares two alert types side-by-side. The left column describes symptom-based alerts (user-facing, focus on errors/latency and business impact) and the right column describes cause-based alerts (internal metrics like CPU/memory, suggesting why something broke).
Comparison table: symptom vs cause
Alert TypePrimary GoalExampleWhen to use
Symptom-basedDetect user impact”Users cannot play songs”Always preferred for paging and prioritization
Cause-basedSurface root-cause signals”Disk usage trending above 95%“Use for predictive or diagnostic value before symptoms surface
YAML-style mapping (illustrative): 
# Symptom (user-facing)
music_playback_failures:
  metric: 'streaming_errors / streaming_requests'
  threshold: '0.001'  # 0.1% error rate
  impact: 'Users cannot play songs'

# Cause (infrastructure)
disk_space_trending:
  metric: 'disk_usage_trend_24h'
  threshold: '> 95% in 4 hours'
  justification: 'Prevents user impact before it occurs'
Prioritize symptom-based alerts. Google’s SRE practice recommends symptom-first alerting because it reduces unnecessary noise, keeps attention focused on user impact, and remains meaningful even when underlying failure modes are unfamiliar.
Prioritize alerts that reflect user-visible symptoms. Use cause-based alerts sparingly and only when they provide predictive value or critical diagnostics.
Cause-based alerts still matter when they predict imminent user impact (e.g., disk filling to 100%), provide critical troubleshooting signals during incidents, or detect security/resource-exhaustion conditions that could quickly degrade service. The key is to limit these to actionable, high-value signals. SLOs as the foundation for alerting Service Level Objectives (SLOs) tie alerting to the promises you make customers. Anchoring alerts to SLOs avoids arbitrary thresholds (e.g., “why CPU > 80%?”) and aligns operational effort with business risk. Typical SLO-based workflow:
  1. Define meaningful SLOs for each service (example: 99.9% availability).
  2. Create error-budget and burn-rate alerts that measure how quickly your error budget is being consumed.
  3. Establish tiered thresholds for different burn rates (fast burn vs slow burn) with appropriate actions.
The slide titled "SLO-Based Alert Implementation" outlines a basic three-step structure for alert design: 1) define meaningful SLOs (e.g., 99.9% availability), 2) create "burn rate" alerts based on error budget consumption, and 3) establish varied thresholds for different consumption rates. It appears to be a presentation slide from KodeKloud about using Service-Level Objectives for alerting.
Example context: if the Checkout API has a 99.9% success SLO, that implies ~0.1% failure budget (~43.2 minutes of errors per 30 days). Alerts should focus on how quickly that budget is being consumed so you act when risk to users becomes unacceptable.
A presentation slide titled "SLO-Based Alert Implementation" explaining that Service Level Objectives provide a foundation for alert design. The example shows a Checkout API with a 99.9% SLO and a 0.1% error budget (43.2 minutes of errors per 30 days).
Tiered SLO-based alerts Use tiered alerting to balance urgency and noise. Example tiers:
SeverityError Budget ConsumptionTypical Action
Urgent100% consumed in 1 hourPage on-call immediately
High25% consumed in 6 hoursPage on-call
Medium50% consumed in 1 dayEmail team
Low / Load75% consumed in 3 daysCreate a ticket / backlog item
A presentation slide titled "SLO-Based Alert Implementation" showing three alert levels (Urgent, High, Medium) with columns for error budget consumption and action triggered. Urgent = 100% in 1 hour (page on‑call); High = 25% in 6 hours (page on‑call); Medium = 50% in 1 day (email team).
Example Prometheus-style alerting rules (simplified) that show cause vs symptom and an SLO burn-rate pattern: 
groups:
- name: example-alerts
  rules:
  - alert: HighCPU
    expr: |
      1 - avg by(instance) (irate(node_cpu_seconds_total{mode="idle"}[5m])) > 0.8
    for: 5m
    labels:
      severity: warning
    annotations:
      summary: "High CPU usage on instance"

  - alert: CheckoutLatencySLOViolation
    expr: |
      histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket{job="checkout"}[5m])) by (le))
      > 0.5
    for: 2m
    labels:
      severity: critical
    annotations:
      summary: "Checkout p95 latency > 500ms — conversion impact likely"
      runbook: "https://example.com/runbooks/checkout-high-latency"
Example error-budget burn-rate rules for the KodeKloud Record Store: 
groups:
- name: slo-error-budget
  rules:
  # Fast burn — pages immediately
  - alert: StreamingErrorBudgetBurnFast
    expr: |
      (
        sum(rate(streaming_request_errors_total[5m]))
        /
        sum(rate(streaming_requests_total[5m]))
      ) > (0.001 * 15)  # SLO error rate * 15 -> ~budget gone in 2 days (30d/2d = 15)
    for: 2m
    labels:
      severity: critical
    annotations:
      summary: "Streaming error budget burning ~15x too fast"
      action: "Page @streaming-team immediately; see runbook"

  # Slow burn — warns but does not page
  - alert: StreamingErrorBudgetBurnSlow
    expr: |
      (
        sum(rate(streaming_request_errors_total[5m]))
        /
        sum(rate(streaming_requests_total[5m]))
      ) > (0.001 * 2)  # SLO error rate * 2 -> ~budget gone in 15 days
    for: 30m
    labels:
      severity: warning
    annotations:
      summary: "Streaming error budget elevated consumption"
      action: "Review in next standup"
Alert routing and escalation Routing and escalation decide who gets notified and how:
  • Map severity to notification channel: pages/SMS for urgent, chat/email for non-urgent.
  • Route to the team with the access and expertise to fix the issue.
  • Define an escalation path for unacknowledged alerts and keep on-call rotations documented.
  • Consider follow-the-sun models to balance load across time zones.
  • Use deduplication, grouping, and correlation to avoid alert storms.
A slide titled "Alert Routing and Escalation" outlining routing principles for notifications. It shows five colored cards recommending aligning alert severity with notification methods, routing to the right team, clear escalation paths, time-zone-aware follow-the-sun support, and deduplication to prevent alert storms.
Practical checklist for implementing alerts
  1. Define the user impact: explicitly state what user behavior or business metric the alert detects.
  2. Select metrics that indicate that impact (prefer service-level metrics like latency, error rates, availability).
  3. Establish thresholds with historical telemetry — avoid arbitrary numbers.
  4. Test sensitivity: simulate incidents and verify alerts trigger reliably and are resilient to noise.
  5. Document actions: attach runbooks/playbooks that tell responders exactly what to do.
  6. Review and tune regularly: remove false positives/negatives and adjust thresholds as the system evolves.
A presentation slide titled "Practical Alert Implementation" showing an "Alert Implementation checklist." It features a horizontal timeline with six colorful pin icons listing: Define User Impact, Select Metrics, Establish Thresholds, Test Sensitivity, Document Analysis, and Review Regularly.
Summary Design alerts to reflect user impact first, use SLOs and burn rates to make alerting objective and measurable, and keep cause-based signals focused and actionable. Route alerts to the right responder, and continuously validate and refine thresholds with real telemetry. That’s it for the Designing Alerts lesson. We will cover incident response structures and roles next, including a model useful for incident response preparation. Further reading and references
  • Google SRE – guidance on SLOs and alerting best practices
  • Prometheus – alerting rule examples and metrics collection

Watch Video