Cloud Cost Calculator
AWS/Azure/GCP service cost estimator with comparison views, reserved instance modeling, and monthly spend forecasting.
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Cloud Cost Calculator
A spreadsheet-based cloud cost estimator and comparison model for **AWS, Azure, and
GCP**. Price a real multi-tier application across all three providers, model reserved /
committed-use savings, and forecast 12 months of spend against a budget — entirely in
Google Sheets or Excel, with no add-ons, macros, or API keys.
This workbook is built around a worked example: a production three-tier web application
(web, API, database, cache, storage, networking, monitoring, backup). Every number in
every tab reconciles, so you can trace a single line item all the way from a per-hour
rate to the annual forecast and see exactly how it was calculated. Replace the sample
line items with your own and the whole model recalculates.
Who this is for
- Engineers and architects sizing a new workload before they commit spend
- FinOps / platform teams that need a defensible, auditable cost model in a tool everyone already has
- Founders comparing providers for a migration or a greenfield build
- Anyone who has ever been surprised by a cloud bill and wants a forecast instead
What's included
| Tab | File | What it does |
|---|---|---|
| 1. AWS Estimate | sheets/01-aws-estimate.csv | Line-item AWS cost breakdown (15 components) for the sample three-tier app |
| 2. Azure Estimate | sheets/02-azure-estimate.csv | The same workload priced on Azure |
| 3. GCP Estimate | sheets/03-gcp-estimate.csv | The same workload priced on GCP |
| 4. Provider Comparison | sheets/04-provider-comparison.csv | Workload-by-workload three-way comparison, cheapest provider, spread % |
| 5. Reserved vs On-Demand | sheets/05-reserved-vs-ondemand.csv | Commitment modeling: Savings Plans, Reserved Instances, CUDs |
| 6. Monthly Forecast | sheets/06-monthly-forecast.csv | 12-month compounding spend forecast vs budget with variance flags |
Supporting material:
formulas/FORMULAS.md— every formula, with real cell references and both Google Sheets and Excel syntaxpricing/— illustrative AWS EC2, Azure VM, and GCP Compute rate cards you can use as a starting referenceguides/reserved-instance-strategy.md— how to decide what to commit, which term, and which payment optiondocs/— getting-started, CSV import, and customization guides
The worked example at a glance
The sample workload (one production environment) costs, per month:
| Provider | Monthly | Annual (on-demand) |
|---|---|---|
| AWS | $1,412.90 | $16,954.80 |
| Azure | $1,469.13 | $17,629.56 |
| GCP | $1,394.75 | $16,737.00 |
GCP is cheapest for this particular mix by $74.38/month (5.3%) versus the most
expensive provider — but the comparison tab shows the picture is workload-dependent: AWS
wins the database and API tiers, GCP wins web/cache/storage/backup, and Azure wins
networking. The headline "cheapest provider" number hides those swings, which is exactly
why the per-workload view exists.
Committing the steady-state compute to a 1-year AWS Compute Savings Plan (no upfront)
cuts the committable compute bill from $958.49 → $699.70/month, a saving of
$258.79/month ($3,105.48/year). The forecast tab applies that saving from month 3
onward and shows the budget staying green until usage growth catches up in Q4.
Quick start
... continues with setup instructions, usage examples, and more.
📄 Content Sample guides/reserved-instance-strategy.md
Reserved Instance & Commitment Strategy
A practical guide to cloud commitment discounts — Reserved Instances (RIs), Savings Plans,
and Committed Use Discounts (CUDs) — and how to use the Reserved vs On-Demand tab to
decide what to commit, for how long, and how to pay. The worked numbers below match the
sample data in sheets/05-reserved-vs-ondemand.csv.
The one-sentence version
Reserve your floor, pay on-demand for your peak. Commitments are cheap because you're
trading flexibility for a discount — so only commit to the capacity you are confident you'll
run for the whole term no matter what.
The three commitment models, briefly
| Provider | Mechanism | Commit to… | Flexibility |
|---|---|---|---|
| AWS | Savings Plans | $/hour of compute spend | High — applies across instance families & regions |
| AWS | Standard RIs | a specific instance type/region | Low — but biggest discount |
| Azure | Savings Plan for Compute | $/hour of compute spend | High |
| Azure | Reserved VM Instances | a VM size in a region | Low–medium |
| GCP | Committed Use (flexible) | $/hour of vCPU+RAM spend | High |
| GCP | Committed Use (resource) | specific machine resources | Low–medium |
The trade-off is always the same: more specificity = bigger discount, less wiggle room.
What the sample model shows
The reservable compute in the worked example (the four steady-state components flagged
Reservable = Yes) costs, on-demand:
- AWS: $958.49/month
- Azure: $989.88/month
- GCP: $909.58/month
Apply the commitment plans and the savings stack up fast:
| Plan | Term | Discount | Effective / mo | Saved / mo | Saved / yr |
|---|---|---|---|---|---|
| AWS Compute Savings Plan | 1 yr | 27% | $699.70 | $258.79 | $3,105.48 |
| AWS Compute Savings Plan | 3 yr | 42% | $555.92 | $402.57 | $4,830.84 |
| AWS Standard RI (all-upfront) | 1 yr | 40% | $575.09 | $383.40 | $4,600.80 |
... and much more in the full download.