Why
Cloud cost is driven by business activity. Known user growth that isn’t modelled creates massive mid-cycle surprise cost spikes. A product expecting 40% user growth next quarter will drive proportional cloud cost increases — if nobody captures this, the forecast is wrong before it’s published.
What
Collect business growth assumptions (user growth, transaction volumes, data volumes) from product teams regularly and translate each into projected cost impact. This data often already exists in product planning documents — the effort is in connecting it to cloud cost projections.
How
Identify Key Business Drivers per Workload
For each major workload, identify the business metric that drives cloud cost:
| Workload | Business Driver | Cloud Cost Relationship |
|---|---|---|
| Customer-facing API | Monthly active users (MAU) | Compute + data transfer scales |
| Data pipeline | GB ingested per month | Storage + processing scales |
| E-commerce platform | Transactions per second (TPS) | Compute + database scales |
| ML inference | Predictions per day | GPU compute scales |
Collect Growth Assumptions from Product Teams
Add a recurring step to the product planning cycle: request projected growth rates for each driver over the next 1–3 quarters. Translate into cost impact using historical cost-per-unit ratios.
Example: if current cost is $0.002 per transaction and product expects TPS to grow 30% next quarter, projected cost increase = current spend × 30%.
Feed into Planning Summary
Add running business growth alongside new project demand in the planning summary. This ensures the forecast captures both step-changes (new projects) and gradual growth (business scaling).
Deliverable Checklist
- Key business drivers identified per major workload
- Growth assumptions collected from product teams
- Cost-per-unit ratios calculated for major workloads
- Growth projections added to quarterly planning summary
- Monthly refresh cadence aligned with product planning