AWS Cost Anomaly Detection: When machine learning meets your billing

Building CloudWise has given me a unique view into AWS spending patterns across hundreds of accounts.

The Problem

Many teams struggle to detect anomalous spending, leading to unexpected bills.

What the Data Shows

After analyzing $10M in AWS spend, here's what we discovered:

Key Findings

• 30% of overspend due to unused EBS volumes
• 25% variance linked to idle EC2 instances
• 20% from overlooked S3 storage classes

The Most Common Mistake

Teams often prioritize compute resources but neglect storage costs, which can accumulate rapidly.

Quick Fix That Works

Implement alerts for spending thresholds in AWS Budgets.

Implementation Steps

1. Audit Phase: Use AWS Cost Explorer to identify spending spikes.
2. Quick Wins: Start with the easiest savings first, like deleting idle resources.
3. Monitor: Set up alerts to catch anomalies before they escalate.
4. Scale: Apply learnings across all accounts and environments.

Your Experience?

What patterns have you noticed in your AWS bills? Drop a comment - I love learning from other developers' experiences.

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I'm building CloudWise to help developers get clarity on AWS costs. Always happy to share insights from our data analysis.