Check for any spend anomaly identified by Amazon Cost Anomaly Detection within your AWS cloud account and analyze and determine the root cause of the anomaly, such as account, service, region, or usage type that is driving the cost increase. Anomaly Detection is a new feature within AWS Cost Explorer that uses custom-built machine learning (ML) models to continuously monitor your AWS cloud cost and usage to detect unusual spends.
This rule can help you work with the AWS Well-Architected Framework
This rule resolution is part of the Cloud Conformity solution
Amazon Cost Anomaly Detection helps detect irregular AWS spend outside regular usage patterns. Through Cost Anomaly Detection findings, you can quickly identify the root cause of cost and usage spikes. This helps you save time in investigating anomalous spend and allows you to better understand your AWS cost drivers based on seasonally-aware patterns (e.g. weekly).
To check your AWS account for Cost Anomaly Detection findings, perform the following operations:
Remediation / Resolution
To access, analyze and solve the Amazon Cost Anomaly Detection findings detected within your AWS cloud account, perform the following operations:Note: As example, this section demonstrates how to analyze an AWS cost anomaly detected in your AWS account because the S3 STANDARD storage usage is much higher than expected due to incomplete multipart uploads in one of your Amazon S3 buckets. To solve the issue that caused the cost anomaly, an S3 bucket lifecycle rule is created and configured to automatically abort any incomplete multipart uploads older than a specified age.
- Google Cloud Platform (GCP) Documentation
- Detecting unusual spend with AWS Cost Anomaly Detection
- Getting started with AWS Cost Anomaly Detection
- Multipart upload overview
- How do I create a lifecycle rule for an S3 bucket?
- GCP Command Line Interface (CLI) Documentation
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You are auditing:
Cost Anomaly Detection Findings
Risk level: Medium