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Finance needs to perform month over month forecasting by extrapolating amortized costs based on historical rates. Having this data enables Finance to create amortized cost models.
SELECT MONTH(BillingPeriodStart), ProviderName, ServiceCategory, ServiceName, ChargeCategory, SUM(EffectiveCost) AS TotalEffectiveCost FROM focus_data_table WHERE BillingPeriodStart >= ? AND BillingPeriodEnd < ? GROUP BY MONTH(BillingPeriodStart), ProviderName, ServiceCategory, ServiceName, ChargeCategory
Evaluates how cloud infrastructure forecasts change over time due to various factors such as priority shifts, workload variations, rightsizing, and CUD impacts.
Though percentage variance is prioritized, measuring in dollars is also valuable. Organizations set their own standards for acceptable variances.
((New Forecasted Cloud Infrastructure Spend – Previous Historic Forecasted Cloud Infrastructure Spend) / Previous Forecasted Cloud Infrastructure Spend)
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Compare forecast vs. actual cloud usage (serviceName, serviceCategory, serviceSubCategory, etc) over a specific period (e.g., day, month, quarter).
This should be specific to service types (ex, measure by a specific serviceName, SKU, serviceCategory, serviceSubCategory etc). Percent variance is recommended, although the scale of usage should also be considered. Each organization defines its acceptable variance
Formula : For specific ServiceName or ServiceCategory or SKU
((Forecasted Resource Utilization – Actual Resource Utilization) / Forecasted Resource Utilization)
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Exploring Cloud Cost Forecasting
A paper that provides guidance around cloud forecasting, including key terms, themes, KPIs, and user insights from our community.
Accurate Cloud Forecasts
A guide that walks through how finance, operations, and executives build models to forecast cloud spend and allocate budgets to business units.
How to Forecast AI Services Costs in Cloud
Cloud-based Artificial Intelligence services add new complexity to forecasting. Learn new FinOps best practices to better forecast these costs.
This metric compares forecasted vs. actual cloud spend over a specific period (e.g., day, month, quarter).
While percentage variance is the primary metric, dollar differences can also be informative. Each organization defines its acceptable variance.
((Forecasted Public Cloud Spend – Actual Public Cloud Spend) / Forecasted Public Cloud Spend)
Data Sources:
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Finance
Forecasting
Cloud Sustainability