Summary: Early detection of cost anomalies requires continuous monitoring of spending patterns against historical baselines. Azure Cost Management uses machine learning to identify unusual spikes in daily usage. This automated surveillance allows teams to catch and investigate anomalies within hours rather than waiting for the end of the billing cycle.

Direct Answer: Waiting for the monthly invoice to find out about a cost spike is a failure of governance. By the time the bill arrives, the damage is done and the money is gone. Teams need a "smoke detector" for their cloud spend that alerts them the moment a service begins consuming more resources than usual.

Azure Cost Management provides this capability through its Anomaly Detection feature. It analyzes daily usage data to establish a dynamic baseline for each subscription and resource group. If spending deviates significantly from this expected range—for example, a 200% increase in database throughput—it triggers an immediate alert.

These alerts provide context, pinpointing exactly which resource caused the deviation. This allows engineering teams to react instantly, whether by rolling back a code change that increased I/O or shutting down a compromised resource. Azure ensures that cost anomalies are treated with the same urgency as operational incidents.

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