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How It Works
A cloud cost model translates expected workload activity into projected spend. Finance and engineering teams combine historical cloud usage data with variables such as resource growth rates, pricing tiers, commitment discounts, and planned product changes. The model is then used to produce a forward-looking view of cloud spend, typically on a monthly or quarterly basis. On AWS, this involves pricing constructs like On-Demand rates, Savings Plans, and Reserved Instances. On Azure, the equivalents are Pay-As-You-Go rates, Azure Savings Plans, and Reservations. On GCP, the equivalents are On-Demand rates and Committed Use Discounts. Each provider’s pricing model has distinct inputs, so a multi-cloud model must account for all three separately before combining them into a total spend forecast.
Why It Matters for Cloud Cost
Without a cost model, cloud budgets are largely guesswork. A McKinsey study found that 67% of companies cannot accurately forecast cloud spend, which means budget overruns are discovered after the fact rather than anticipated. A well-maintained cost model lets finance teams set realistic budgets, gives engineering managers visibility into the cost impact of architectural decisions, and provides a baseline against which actual spend can be compared each month. It also supports commitment planning: knowing how much compute capacity will be needed over the next 12 months makes it possible to evaluate whether Reserved Instances or Savings Plans will reduce total cost. Without that forward-looking view, teams default to On-Demand pricing and forgo available discounts of up to 72% on AWS, up to 72% on Azure, and up to 57% on GCP.
CoPilot surfaces projected savings based on actual usage patterns, giving teams the data needed to validate commitment assumptions inside their cost model before any purchase is approved.