Determining the Right Threshold for Alerts in AWS Cost Anomaly Detection

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Content level: Intermediate
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Leveraging AWS Cost Anomaly Detection's machine learning capabilities, organizations can gain comprehensive visibility into anomalous spending patterns, implement tailored monitoring strategies, and take timely actions to control and optimize cloud costs across their AWS accounts

Introduction

AWS Cost Anomaly Detection is a service that uses machine learning to monitor usage in your accounts to detect anomalous spending. As of 3/27/23, all new AWS Cost Explorer users with a Payer or Regular account automatically get a default configuration of AWS Cost Anomaly Detection. While AWS Cost Anomaly Detection offers a default configuration out-of-the-box, FinOps managers, Cloud Finance professionals, and Site Relayability engineers can derive greater value by strategically configuring the service to align with their organization's unique cost optimization goals, resource utilization patterns, and operational workflows. This article explores insights to help organizations transform cloud cost management through strategic monitoring approaches.

Customization

Organization stakeholders may have different needs and preferences for cost management. The configuration of multiple alert subscriptions and cost monitors allows businesses to tailor alerts and cost monitoring to meet the specific needs of multiple teams or departments.

  • IT or Application teams require detailed service cost breakdowns
  • Finance teams focus on overall infrastructure expenses
  • Leadership needs high-level spending insight

Granularity & Accuracy

By leveraging multiple alert subscriptions and cost monitors, organizations can monitor cloud usage at a granular level. This heightened visibility enables quicker and more accurate detection of anomalies, allowing businesses to address potential issues proactively before they escalate and cause significant financial impact.

When configuring these alert subscriptions and cost monitors, it's crucial to consider the organization's unique needs and objectives. The alert frequency and threshold settings should be carefully evaluated to strike the right balance between timely anomaly detection and avoiding excessive noise or false positives.

Determining Thresholds

Different teams within an organization often require unique cost monitoring approaches tailored to their specific needs and responsibilities. To address this, AWS Cost Anomaly Detection offers the capability to create role-specific alert subscriptions. By leveraging this feature, organizations can ensure that each team receives precisely targeted financial insights and cost anomaly notifications relevant to their role.

Application Teams: Granular Cost Visibility

Use Case: Customer gaming application team is preparing for the rollout of a new feature that is expected to significantly increase application usage due to user interactions and data storage requirements. To proactively manage the potential impact on costs, the team has established a 20% deviation threshold for their application accounts.This threshold was strategically determined to strike an optimal balance between historical analysis, forward-looking projections, and reasonable buffers for usage spikes. This approach helped the team ensure they can identify and address meaningful anomalies that may affect application costs, while filtering out minor fluctuations that are expected during the feature rollout period and organic growth of the application.

Project Teams: Accountability for controlled Spending

Use Case:Customer research team is working on a machine learning project using resource-intensive analytics workloads for training their machine learning models.To maintain accountability, based on their project projections and anticipated month-over-month growth in resource requirements.They set a $1,000 threshold for monitoring analytics service spending. One month in, they received an alert indicating costs exceeded the threshold. Investigation revealed a data scientist had inadvertently left resource-intensive instances running overnight, causing a cost spike. Receiving the alert enabled the team to quickly terminate unnecessary instances, implement stricter resource management, prevent further budget overruns, and ensure responsible resource allocation within their project budget.

FinOps Teams: Comprehensive Monitoring

Use Case: The FinOps team sets a dual-layered threshold combining an absolute $1,000 limit and a 30% relative threshold with a daily summary. Additionally, they set a $10,000 alert threshold to capture critical anomalies that could indicate large-scale overspending or operational issues with individual alerts. This comprehensive approach ensures that no significant cost deviation, whether a large unexpected jump or a proportionally significant increase, goes unnoticed, enabling consistent identification of optimization opportunities across the organization's cloud usage.

Leadership Teams: Strategic Oversight for Informed Decision-Making

Use Case: Leadership sets a $10,000 threshold to monitor the organization's overall cloud cost trends. This high-level threshold filters out operational details, focusing on strategic anomalies. Weekly summaries consolidate data, providing leadership with summarized insights to make informed decisions and aid in long-term planning without information overload. Enter image description here Fig1 : Multiple Alert Subscriptions

Effective Implementation

Implementing a dynamic threshold approach transforms Cost Anomaly Detection from a passive tracking mechanism to a proactive financial management strategy. By evolving with your organization's unique spending patterns, you can achieve comprehensive cost visibility while preventing alert fatigue.

  • Start conservative: Begin with lower thresholds.
  • Gradually refine based on actual usage patterns.
  • Customize alerts for different organizational needs.

Conclusion

In this article, we have discussed different approaches to tailoring AWS Cost Anomaly Detection to you environment, that can help you gain better visibility into anomalous spending patterns and take timely and informed actions to control cost.Cost Anomaly Detection now support faster anomaly resolution with enhanced root cause analysis. Explore and get started with Cost Anomaly Detection workshop