Tip: Start with your average month. Then adjust reduction, discounts, PUE, and renewable coverage to compare greener options.
- Current monthly cost = (ComputeHours × ComputeRate) + (StorageGB × StorageRate) + (TransferGB × TransferRate)
- Optimized usage = Baseline × (1 − Reduction%/100) for compute, storage, and transfer
- Optimized prices = BaselineRate × (1 − Discount%/100) for each cost component
- Total cost = MonthlyCost × PeriodMonths
- Energy (kWh) = (ComputeHours × kWh/ComputeHour + StorageGB × kWh/GB-Month + TransferGB × kWh/GB) × PUE
- Efficiency gains reduce kWh intensities: kWhNew = kWhBase × (1 − EfficiencyGain%/100)
- Effective emissions factor (simplified) = GridEF × (1 − RenewableCoverage%/100)
- Emissions (kg CO2e) = Energy × EffectiveEF × PeriodMonths
- Breakeven months = ceil(OneTimeCost / MonthlySavings), when MonthlySavings > 0
- Enter a realistic average month for compute hours, storage, and transfer.
- Add your blended rates from invoices or your pricing dashboard.
- Set reduction percentages for right-sizing, scheduling, and cleanup.
- Apply discounts that reflect commitments, spot usage, or negotiation.
- Update PUE, renewable coverage, and grid intensity for greener options.
- Click Calculate, then download CSV or PDF for reporting.
Sample scenarios to illustrate typical inputs and outcomes.
| Scenario | Compute hours/mo | Storage GB/mo | Transfer GB/mo | Compute reduction | Renewable target | Typical focus |
|---|---|---|---|---|---|---|
| Dev/Test consolidation | 900 | 700 | 120 | 35% | 70% | Schedules, auto-stop, cleanup |
| Production right-sizing | 3800 | 2600 | 900 | 18% | 80% | Instance fit, commitments, tuning |
| Data-heavy workloads | 2400 | 12000 | 4200 | 12% | 90% | Tiering, caching, egress control |
Baseline footprint from workload mix
Most cloud spend starts with three drivers: compute-hours, stored gigabytes, and transferred gigabytes. For example, 2,500 compute-hours/month at $0.06/hour equals $150/month. Add 2,000 GB storage at $0.023/GB-month ($46) and 800 GB transfer at $0.09/GB ($72) to reach $268/month. Multiply by 12 months for an annual view. The calculator mirrors this structure, then converts the same activity into electricity using kWh intensities and an overhead factor.
Rightsizing and scheduling compound savings
Rightsizing and scheduling reduce both cost and energy because fewer hours are consumed. A 20% compute reduction turns 2,500 hours into 2,000 hours, and the savings scale every month of the period. If you also secure a 15% price improvement through commitments or spot usage, the compute line item becomes 2,000 × $0.051 = $102/month. Adding a 5% efficiency gain further lowers kWh per compute-hour, improving emissions when prices stay flat.
Storage lifecycle and data transfer control
Storage improvements are low-risk and measurable. A 10% storage reduction on 2,000 GB frees 200 GB; at $0.023/GB-month that is $4.60/month, before any tier discounts. Larger programs use retention rules, cold tiers, and object lifecycle policies to reach 20–50% reductions over time. Network transfer can also drop with caching and locality; even a 5% reduction on 800 GB prevents 40 GB of egress each month and trims network energy use.
PUE and renewables drive emissions outcomes
Emissions are calculated from energy (kWh) multiplied by an effective emissions factor. Energy is workload kWh plus overhead: PUE 1.50 means 1.5 kWh are used for each 1 kWh of IT load. Moving from 1.50 to 1.20 cuts overhead by 20% on the same IT energy. Renewable coverage reduces the effective emissions factor: with grid intensity 0.45 kg/kWh, shifting renewables from 30% to 80% lowers the factor from 0.315 to 0.090 kg/kWh. Pair with efficiency gains for deeper reductions.
Breakeven and reporting for stakeholders
Financial teams often need a payback view when migration has a one-time cost. If transition work is $6,000 and modeled monthly savings are $250, breakeven is ceil(6000/250) = 24 months. Sustainability reporting can also monetize avoided emissions using an internal carbon price; 12 tCO2e avoided at $50/t suggests $600 of value. Use the savings %, avoided kg, and avoided tons outputs to prioritize actions. Export buttons generate CSV and PDF summaries for approvals and tracking.
1) What should I enter for compute hours per month?
Use your billing export or monitoring totals for instance-hours or vCPU-hours. If you only know monthly compute cost, divide by your blended hourly rate to approximate hours. Keep the method consistent across comparisons.
2) How do I choose kWh per compute hour?
Start with 0.3–0.8 kWh per compute-hour for general workloads, then calibrate using measured power, provider sustainability reports, or internal estimates. Adjust downward when you expect newer hardware or higher utilization.
3) Why is PUE included?
PUE accounts for cooling, power delivery, and facility overhead. An IT load of 1,000 kWh with PUE 1.5 implies 1,500 kWh total. Lower PUE reduces emissions without changing workload demand.
4) What does renewable coverage mean here?
It represents the share of electricity matched with renewable energy in your chosen location or contract. The model reduces the grid emissions factor by that percentage. Use conservative values if claims are uncertain.
5) How is avoided carbon value calculated?
Avoided emissions in metric tons are multiplied by your carbon price per ton. Set the price to an internal shadow price or a compliance value. This is an optional decision-support metric, not revenue.
6) Can I compare multiple scenarios?
Yes. Run the calculator for each scenario and download CSV/PDF outputs. Keep the same baseline inputs, change only one lever at a time, and save files with clear names for auditability.