Optimizing Cloud Commitment Coverage Against Real Workload Shape
An engineering organization holds reserved instance and savings plan commitments purchased two years ago against a workload that has since changed substantially.
Run on Sahay · Software Delivery & Quality
The challenge
Commitment coverage no longer matches the instance families and regions actually in use.
Finance renews commitments on historic spend because nobody can model the alternative.
Unused commitment and on-demand overspend coexist in the same monthly bill.
How Sahay solves it
Coverage Analysis
Current commitments are compared against actual consumption by family, region, and hour.
Waste Identification
Unused commitment and uncovered on-demand usage are quantified separately rather than netted off.
Workload Attribution
Consumption is attributed to the services and teams driving it, so change is actionable.
Commitment Modelling
Renewal options are modelled against projected rather than historic workload shape.
Ongoing Rebalancing
Coverage is re-evaluated as workload shape changes instead of at renewal only.
The outcome
Unused commitment and on-demand overspend quantified separately.
Renewal modelled against projected rather than historic consumption.
Consumption attributed to the teams and services that drive it.
Representative scenario: a modelled industry situation showing how this accelerator applies. Figures are projections, not a named client result.
Capabilities used
Every accelerator is assembled from the same capability layer, so a workflow you adopt here shares its foundation with the rest of the portfolio.
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