Skip to content

📈 Spending Drift Detection

What is Drift Detection?

Drift Detection is an intelligent monitoring feature that automatically identifies unusual changes in your AWS spending patterns. Instead of discovering cost surprises at the end of the month, unusd.cloud proactively alerts you when your spending deviates from normal patterns.

Why It Matters

The Problem

AWS costs can spike unexpectedly due to various reasons:

  • Accidental resource creation – A developer spins up expensive resources and forgets to terminate them
  • Auto-scaling gone wrong – Scaling policies trigger more aggressively than expected
  • Security incidents – Compromised credentials used for crypto mining or other malicious activities
  • Configuration drift – Infrastructure changes that weren't budgeted for
  • Forgotten experiments – Test environments left running over weekends or holidays

By the time you receive your monthly bill, thousands of dollars may have already been wasted.

The Solution

unusd.cloud continuously monitors your AWS spending and compares it against your historical patterns. When something unusual happens, you're notified immediately through your preferred channels (Email, Slack, or Microsoft Teams).

Key Features

🔍 Intelligent Pattern Analysis

Our drift detection goes beyond simple percentage comparisons. We analyze your spending patterns over time to understand what's "normal" for your specific AWS environment. This means:

  • Adaptive thresholds – An account with variable spending won't trigger false alarms, while even small deviations in a stable account are flagged
  • Historical context – We consider your past spending history, not just the previous data point
  • Trend awareness – Gradual planned growth is distinguished from sudden unexpected spikes

⚡ Spike Detection

Sudden, dramatic increases in spending are immediately flagged. This is particularly important for:

  • Detecting potential security breaches (crypto mining attacks can increase costs by 1000%+ overnight)
  • Identifying runaway auto-scaling or misconfigured resources
  • Catching accidental deployments to production environments

📉 Focus on Spending Increases

Our drift detection is specifically designed for AWS cost monitoring, so it focuses on spending increases rather than decreases:

  • Spending increases are flagged with appropriate severity (Low → Critical)
  • Spending decreases are treated as informational – after all, reduced costs are typically good news!

If your spending drops significantly, you'll see an informational notice confirming the decrease. This helps you verify the change was expected (e.g., successful cost optimization) rather than unexpected (e.g., resources accidentally terminated).

đŸŽ¯ Severity Classification

Not all spending changes are equally concerning. Our system classifies anomalies by severity:

Level What It Means
✅ None Spending is within normal range
â„šī¸ Low Minor deviation – worth monitoring
âš ī¸ Moderate Notable change – review recommended
🔴 High Significant anomaly – investigate promptly
🚨 Critical Major deviation – immediate action required

📊 Confidence Scoring

Each drift alert includes a confidence score based on how much historical data is available. As unusd.cloud collects more data about your account, detection accuracy improves automatically.

💡 Actionable Recommendations

When drift is detected, you receive specific, actionable recommendations based on the type of anomaly detected. This helps you:

  • Know exactly what to investigate first
  • Understand the potential causes
  • Take corrective action quickly

How It Works

  1. Continuous Monitoring – Every scan analyzes your current AWS spending
  2. Pattern Learning – We build a profile of your normal spending behavior over time
  3. Anomaly Detection – Current spending is compared against your historical baseline
  4. Smart Alerting – Only meaningful deviations trigger notifications
  5. Clear Reporting – Drift status is included in every report and dashboard view

Benefits

💰 Cost Protection

Catch spending anomalies before they become expensive problems. Early detection means early remediation, potentially saving thousands of dollars.

🔒 Security Enhancement

Unusual spending patterns can indicate security breaches. Drift detection acts as an additional security layer, alerting you to potential compromised resources.

😌 Peace of Mind

No more end-of-month bill shock. Know that your AWS spending is being monitored 24/7 with intelligent alerting.

📉 Reduced Noise

Unlike simple threshold alerts that trigger on any change, our intelligent detection reduces false positives by understanding your specific spending patterns.

Viewing Drift Information

In Reports

Every unusd.cloud report includes a drift indicator showing whether your spending has deviated from normal patterns.

In Dashboard

The Analytics dashboard displays:

  • Current drift status and severity
  • Historical drift trends over time
  • Visual alerts when anomalies are detected

In Notifications

Slack, Microsoft Teams, and Email notifications include drift status with appropriate severity indicators to help you prioritize your attention.

Getting Started

Drift detection is automatically enabled for all unusd.cloud users. No additional configuration is required.

As you run more scans, the system builds a more accurate profile of your normal spending patterns, improving detection accuracy over time. We recommend running at least 3-5 scans before expecting optimal drift detection performance.

Best Practices

  1. Run regular scans – More data points mean better pattern detection
  2. Review moderate alerts – Don't ignore low-severity drift; it may indicate emerging trends
  3. Investigate high/critical alerts promptly – These often indicate issues requiring immediate attention
  4. Use multiple notification channels – Ensure critical alerts reach the right people quickly

💡 Pro Tip: Combine drift detection with scheduled scans to get proactive alerts before your daily standup or weekly reviews. This way, you can address spending anomalies as part of your regular operational workflow.