The Real Difference Between 'Monitoring' and 'Alerting'

Monitoring and alerting are not the same thing. Teams conflate them constantly: "We need monitoring" actually means "We need alerts." Or worse: "We have monitoring" when they mean "We have dashboards no one looks at."

Understanding the difference prevents alert fatigue, improves incident response, and helps you build a system that actually works.

Definitions

Monitoring

Continuous observation of system state.

Monitoring is always running, always collecting:

• CPU usage every 30 seconds
• HTTP response time on every check
• Error rates from logs
• Disk space percentage

Monitoring = Data collection + Storage + Visualization

Monitoring answers: "What is the current state?" and "What was the state at 3am last Tuesday?"

Alerting

Notification when observed state requires action.

Alerting triggers based on conditions:

• CPU > 90% for 5 minutes → Alert
• HTTP status = 503 → Alert
• Error rate > 5% → Alert
• Disk > 85% → Alert

Alerting = Condition evaluation + Notification

Alerting answers: "Does someone need to do something right now?"

The Relationship

Monitoring (continuous)
    │
    └── Collects data → Stores → Visualizes
                            │
                            ↓
                        Evaluates conditions
                            │
                            ↓
                     Condition met?
                        │     │
                       Yes    No
                        │     │
                        ↓     └→ Continue monitoring
                    Alerting
                        │
                        ↓
                  Notification sent

Monitoring is the foundation. Alerting is built on top.

Why the Distinction Matters

Problem 1: Alert Fatigue

Teams often think: "Monitor everything important → Alert on everything monitored"

Result:

6:00 AM - Alert: CPU at 75%
6:05 AM - Alert: Memory at 70%
6:10 AM - Alert: Disk I/O spike
6:15 AM - Alert: Response time 500ms
6:20 AM - Alert: CPU back to 45%
... 50 more alerts ...

All of these: Normal operations, no action needed

The fix: Monitor everything. Alert only on what requires human action.

Problem 2: Missing Context

Alert without monitoring context:

Alert: Website down
├── What happened? Unknown
├── When did it start? Unknown
├── What else is affected? Unknown
└── Is it recovering? Unknown

Alert with monitoring context:

Alert: Website down
├── Timeline: Started 6:42 AM
├── Server metrics: CPU spike at 6:40 AM
├── Related: Database also showing errors
├── Trend: Response time degrading for 2 minutes before failure
└── Status: Still down (6:45 AM)

The fix: Alerting triggers response. Monitoring provides context.

Problem 3: No Historical Insight

Alerting-only approach:

Q: "How often does the API timeout?"
A: "We get alerts sometimes. Maybe weekly?"

Q: "What's our actual uptime?"
A: "We haven't tracked outages since we alert."

Q: "Is performance getting worse over time?"
A: "Unknown. We only know when it crosses alert threshold."

Monitoring-first approach:

Q: "How often does the API timeout?"
A: "3 times in the last 90 days, averaging 12 minutes each."

Q: "What's our actual uptime?"
A: "99.94% over the last quarter."

Q: "Is performance getting worse over time?"
A: "P95 latency increased from 120ms to 180ms over 6 months."

The Right Balance

Monitor (Continuous, No Notification)

Everything useful for understanding system behavior:

• All server metrics (CPU, memory, disk, network)
• All response times
• All error rates
• All service states
• All dependency health

This data goes to dashboards and is stored for analysis.

Alert (Conditional, Notification)

Only conditions requiring human action:

The Threshold Question

For each metric, ask:

1. At what value would I take action?
2. How long should it persist before alerting?
3. Who should be notified?

If you wouldn't take action, don't alert.

Alerting Anti-Patterns

1. Alert on Everything

CPU > 50%: Alert
Memory > 40%: Alert
Response time > 100ms: Alert

Result: 500 alerts/day, all ignored

2. No Severity Levels

Alert: Server on fire
Alert: CPU at 51%
Alert: SSL expires in 30 days

All sent to: #alerts channel with same priority

3. Alert Without Context

Alert: Website timeout

Missing:
- Which server?
- Current metrics?
- Related issues?
- Previous occurrences?

4. Duplicate Alerts

Alert: API down (from monitor A)
Alert: API down (from monitor B)
Alert: API down (from synthetic check)
Alert: Database errors (caused by API)
Alert: Error rate spike (symptom of API)

One incident, five alerts.

5. No Alert Ownership

Alert: Database slow

Sent to:
- #alerts (30 people)
- Email (entire team)
- SMS (everyone)

Result: Bystander effect. No one responds.

Building a Good System

Step 1: Monitor First

Set up comprehensive monitoring without alerts:

• Server metrics (CPU, memory, disk)
• Service health checks
• Response times
• Error rates

Let it run. Observe patterns. Understand normal.

Step 2: Identify Actionable Conditions

From monitoring data, determine:

• What values indicate actual problems?
• What duration makes it significant?
• What's normal variance vs. concern?

Step 3: Create Tiered Alerts

Critical (Wake someone up):
- Site completely down
- Database unreachable
- Payment processing failed

Warning (Respond during business hours):
- Disk > 85%
- Error rate > 2%
- Response time > 2s

Info (FYI, no action needed):
- Deployment completed
- SSL expires in 30 days
- Memory higher than usual

Step 4: Route Appropriately

Step 5: Review and Tune

Regularly ask:

• Which alerts led to action? (Keep)
• Which alerts were ignored? (Tune or remove)
• What incidents had no alert? (Add monitoring)

Wakestack's Approach

Monitoring Layer

• HTTP/HTTPS endpoint checks (continuous)
• Server metrics via agent (30-second intervals)
• Response time tracking (every check)
• SSL certificate expiration (daily)
• DNS resolution (continuous)

All data stored, visible in dashboards, available for analysis.

Alerting Layer

Configurable per monitor:

• Threshold conditions
• Duration requirements
• Severity levels
• Notification channels

Example configuration:
Monitor: api.example.com/health
├── Check every: 1 minute
├── From: 3 regions
├── Alert when: 2+ regions fail
├── For: 2 consecutive checks
├── Severity: Critical
└── Notify: PagerDuty + Slack

Separation in Practice

Dashboard shows:
├── All checks (200+ endpoints)
├── All server metrics (15 servers)
├── Historical trends (90 days)
└── No noise in your inbox

Alerts fire for:
├── Actual outages
├── Approaching thresholds
└── Only what you configured

Set up smart monitoring — Monitor everything, alert on what matters.

Practical Guidelines

When to Monitor (But Not Alert)

• Normal operational metrics
• Development/staging environments
• Non-critical internal tools
• Metrics for capacity planning
• Data for post-mortems

When to Alert

• User-facing services down
• Critical infrastructure failing
• Security-related events
• Thresholds requiring immediate action
• Approaching capacity limits

Questions to Ask Before Adding an Alert

1. If this fires at 3 AM, would I get out of bed?
2. What would I actually do when this fires?
3. Is there a clear remediation step?
4. Could this wait until business hours?
5. Will this alert fire frequently in normal operation?

If you answer "no" to #1 and #3, it might be monitoring-only.

Key Takeaways

• Monitoring is observation; alerting is notification
• Monitor everything useful; alert only on actionable conditions
• Alert fatigue comes from conflating the two
• Monitoring provides context; alerting triggers response
• Historical data (monitoring) enables improvement
• Tuning alerts is ongoing, not one-time

Related Resources

• Uptime Monitoring Guide
• Status Page Best Practices
• What Causes False Downtime Alerts