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Status Submitted
Workspace Cloudability
Categories Anomaly Detection
Created by Guest
Created on Aug 5, 2026

RELATED IDEAS

Enable anomaly detection to distinguish expected cloud spend changes from genuine anomalies through configurable detection controls and awareness of billing lifecycle events such as prepaid credit exhaustion.

Customers need greater control and context-awareness within anomaly detection to reduce false-positive alerts and improve trust in anomaly insights.
A specific customer use case involves a GitHub Copilot subscription where prepaid AI credits are consumed during the first part of the month and billed consumption begins after credits are exhausted. This transition causes expected spend increases, often ranging from approximately $1,900-$3,000 on weekdays, but these expected billing events are currently flagged as anomalies.
Additionally, customers cannot configure anomaly detection sensitivity, thresholds, or acceptable variance levels to align with known workload and business spending patterns.


Requested enhancements:
• Allow customers to configure anomaly detection sensitivity and threshold settings.
• Support customer-defined acceptable spend variance ranges for specific subscriptions or services.
• Detect prepaid credit exhaustion and transition-to-billed-consumption events as expected billing behaviour.
• Incorporate billing lifecycle context into anomaly detection models.
• Provide options to suppress or automatically de-prioritize anomalies generated from known and expected spend patterns.
 

Idea priority Urgent

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