Anomaly Management
Anomaly Management focuses on finding unexpected changes in spend or usage so you can react quickly to misconfigurations, runaway workloads, or unplanned usage. This section explains how anomaly detection fits into Cloudaware FinOps and how to use it together with detectors, triage processes, budgets, and other optimization features.
Use these guides together:
Detectors & Signals – how anomalies are identified and tuned.
Triage Playbook – a repeatable process for validating and responding to anomalies.
Why Use Anomaly Detection
Use anomaly detection for:
Catching sudden cost spikes in specific accounts, services, or applications.
Spotting unusual usage patterns (for example, large increases in a storage class, data transfer, or API call volume).
Complementing waste detection policies and rightsizing recommendations with real‑time signals.
Anomalies are for unexpected behavior; for planned changes such as migrations or product launches, use budgets, forecasts, and normal reporting instead.
Typical Anomaly Scenarios
Examples include:
A forgotten test environment generating production‑level load.
A misconfigured autoscaling group or function that scales out unexpectedly.
A data export or logging configuration that writes far more data than intended.
A new region or service enabled without appropriate controls.
In each case, anomaly alerts should route to owners who can quickly confirm whether the behavior is expected and take action if not.
Cost Anomaly Detection Dashboards
Cloudaware provides analytics dashboards on cost anomalies, for example, Azure Cost Anomaly Detection.
Relationship to Other FinOps Features
Budgets & Forecasting – anomalies may trigger before a scope exceeds its budget; use both together for robust guardrails.
Waste Detection & Optimization – anomalies can indicate new sources of waste or highlight where optimization is most urgent.
Showback & Chargeback – anomaly history provides context when explaining unusual spikes on internal statements or invoices.