Anomaly Detection
Spot unexpected cost spikes
before it hurts budget
Machine-learned baselines flag unusual Azure spend the day it happens and
route the alert to the owner who can act.
From baseline to alert, the day spend moves
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Learn your normal spend
Machine-learned baselines are built from your historical usage, per subscription, resource group, and service.
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Watch spend in real time
Actual cost is compared against the baseline every day, so an unusual spike is caught the day it happens.
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Route the alert to an owner
When spend breaks the expected band, the alert goes straight to the team that owns the resource, with the dollar impact attached.
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Act before the invoice
Drill into the resource, see exactly what changed, and fix it before the cost lands on the bill.
From billing spike to verified root cause
Why finance and engineering rely on it
No more bill shock
Catch runaway spend the day it starts instead of discovering it at month-end.
Alerts reach the right person
Anomalies route to the resource owner, so the team that act hears about it first.
Hours, not weeks
Find the cause in minutes, with the change and its impact shown in context.
Cost Analyzer
More features in this module
AI agents
Embrace AI in FinOps
Rightsizing
Stop over-provisioning
Allocation & visibility
Make every cost accountable
Cost optimization
Reduce cloud waste
Budget planning
Forecast and control spend
Azure showback
Drive cost accountability
Azure reservations
Commit with confidence
Reduce technical debt & risk
Mitigate infrastructure vulnerabilities
Scheduling
Run workloads only when needed