Detection Rules

Cost Anomaly Detection helps you monitor the actual payments of both pay-per-use and yearly/monthly resources.

• Pay-per-use expenditures: AI algorithms are used to intelligently identify unexpected expenditure spikes based on machine learning. If the actual cost in a day exceeds the maximum forecasted cost of that day and the difference is greater than $1 USD, a cost anomaly will be identified.

  Percentage of pay-per-use costs that are impacted = (Actual cost – Maximum forecasted cost)/Maximum forecasted cost

  For example, if the actual cost on July 23 was $105 USD, but the maximum forecasted cost was $100 USD, that will be identified as a cost anomaly.

• Yearly/monthly expenditures: If the actual period-over-period (PoP) growth rate of MTD costs (excluding the cost of the current day) exceeds the threshold you set over the previous billing cycle and the difference is greater than $1 USD, a cost anomaly will be identified.

  PoP growth rate = (Actual cost for the current month – Cost for the previous month)/Cost for the previous month

  For example, if your expenditures from June 1 to 23 were $100 USD and the expenditures from July 1 to 23 (the current day is July 24) were $121 USD, and the threshold was set to 20%, then the actual growth rate (21%) exceeds the threshold, and that will be identified as a cost anomaly.

  

  There are three severity levels for cost anomalies:

  • Minor: > 0% and < 20%
  • Major: ≥ 20% and < 50%
  • Critical: ≥ 50%