AWS Cost Anomaly Detection のアイコン

AWS Cost Anomaly Detection Essential2020年〜

A service that uses machine learning to automatically detect unusual spikes in your AWS spending

About 3 min read

What It Does

AWS Cost Anomaly Detection uses machine learning models to learn your AWS spending patterns and automatically detect unusual cost spikes. You can set up monitors at the service level, account level, or cost allocation tag level, and receive notifications via SNS or email when anomalies are detected. It helps you catch unexpected charges early.

Use Cases

Common use cases include early detection when a developer accidentally launches expensive instances, discovering unauthorized resource usage from security breaches, and catching sudden increases in data transfer costs due to misconfigurations. It is also used to centrally monitor cost anomalies across accounts in multi-account environments.

Everyday Analogy

Think of it like a credit card company's fraud detection system. It learns your usual spending patterns and sends you an alert when an unusually large charge appears, asking 'Was this really you?' This helps you catch charges you don't recognize early on.

What Is AWS Cost Anomaly Detection?

AWS Cost Anomaly Detection is a service that automatically detects and notifies you when unusual patterns appear in your AWS spending. Since AWS uses pay-as-you-go pricing, misconfigurations or forgotten resources can lead to unexpectedly high bills. Cost Anomaly Detection analyzes your historical spending patterns with machine learning and flags cost increases that exceed normal fluctuations. It is available as a feature of AWS Cost Explorer at no additional charge.

Setting Up Monitors and Alerts

In Cost Anomaly Detection, you create "monitors" to define what costs to watch. There are four monitor types: by AWS service, by linked account, by cost allocation tag, and by cost category. By attaching a "subscription" (notification setting) to a monitor, you can send alerts to an SNS topic or email address when anomalies are detected. You can set thresholds based on dollar amounts or percentages to prevent alerts from firing on minor fluctuations. For a comprehensive overview of monitor and alert configuration, books on Amazon can be helpful.

Analyzing and Addressing Anomalies

When an anomaly is detected, you can see a breakdown of which service, account, and region experienced the cost increase. Root cause analysis information is also provided, making it easy to identify the problem. For example, you might see something like 'EC2 m5.xlarge instances surged in us-west-2.' Since some detections may be false positives, you can provide feedback on each anomaly (whether it was a valid or false detection) to improve detection accuracy over time.

Things to Watch Out For

  • Cost Anomaly Detection itself is free to use with no additional charges
  • The machine learning model needs historical cost data to learn from, so detection accuracy may be lower for new accounts
  • Detection is not real-time - there is a lag of several hours for cost data to be reflected. If you need immediate detection, consider using CloudWatch alarms alongside this service
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