AWS Cost Anomaly Detection
A tool that leverages machine learning to identify and alert on anomalous cloud spending patterns, helping manage unexpected expenses.
| Category | Cost Security & FinOps |
|---|---|
| Last Commit | 1 year ago |
| Last page update | 19 days ago |
| Pricing Details | Pricing is based on the number of monitors and alerts configured, with a free tier available for new users. |
| Target Audience | AWS users and organizations looking to manage and optimize their cloud spending. |
AWS Cost Anomaly Detection manages unexpected and unmanaged cloud expenses by leveraging advanced machine learning technologies to identify and alert on anomalous spend patterns. This tool integrates with AWS Cost Explorer and the AWS Billing and Cost Management console, allowing for the creation of contextualized monitors that evaluate spend across various dimensions such as AWS services, member accounts, cost allocation tags, and cost categories.
The technical architecture of AWS Cost Anomaly Detection involves machine learning models that analyze historical spend data to establish baseline patterns, including seasonally-aware trends to minimize false positives. These models run approximately three times a day to monitor net unblended cost data, with a potential delay of up to 24 hours due to the processing time of billing data. Once an anomaly is detected, the system ranks potential root causes by their cost impact, surfacing up to the top 10 root causes and providing detailed insights into the factors driving the anomaly, such as specific combinations of linked accounts, regions, and usage types.
Operational considerations include setting up custom anomaly thresholds and alert subscriptions, which can be configured to notify via email or Amazon SNS topics. This allows for flexible alerting preferences, including daily or weekly summaries, and integration with collaboration tools like Slack or Amazon Chime. Access control is managed through AWS Identity and Access Management (IAM), enabling granular permissions for users to view and manage billing data.
Key technical details include the requirement of 10 days of historical service usage data for new service subscriptions before anomalies can be detected. The system also allows for detailed root cause analysis, with the ability to view time series graphs filtered by specific root causes and cost impacts. While the system is highly effective, it is important to note that it may take up to 24 hours for new monitors to begin detecting anomalies after setup.