> For the complete documentation index, see [llms.txt](https://stakpak.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://stakpak.gitbook.io/docs/how-it-works/rulebooks/infrastructure-cost-estimation.md).

# Infrastructure Cost Estimation

## Introduction

This rulebook defines a technology-agnostic methodology for creating accurate monthly infrastructure cost estimates, ensuring reliable budgeting through:

* Systematic resource discovery via IaC analysis and live environment enumeration
* Authoritative, up-to-date pricing research
* Credit exclusion for true cost visibility
* Consistent, reproducible calculation practices

## Goal

### Why did we make this rule book?

* To avoid inaccurate cost forecasts caused by credits, discounts, or outdated pricing.
* To ensure consistent, transparent cost estimation across cloud providers.
* To eliminate incomplete analysis by covering all resources and environments.

### What will you achieve?

* Accurate gross and net monthly infrastructure cost estimates.
* Clear, repeatable workflows for cost analysis and validation.
* Better budgeting decisions and reduced financial risk post-credits.

### Who is this for?

Anyone who wants to perform accurate, consistent, and reliable cloud infrastructure cost estimation, regardless of platform or technology.

## Workflow

This methodology follows a standardized process to estimate infrastructure costs accurately and consistently across cloud providers.

1. Exclude Credits First: Remove credits, promotional offers, and discounts to reveal true gross costs.
2. Native Cost Estimation: Use official cloud provider tools with proper filters to calculate gross and net costs.
3. Resource Discovery: Identify all infrastructure components through IaC analysis, live queries, and API enumeration.
4. Pricing Research: Gather region-specific pay-as-you-go rates from official provider pricing pages.
5. Python Calculations: Perform all numeric operations in Python for precision and reproducibility.
6. Validate Results: Compare native tool gross costs with calculated costs, documenting any differences and assumptions.
7. Document Findings: Record the full analysis, resource inventory, pricing sources, and calculation scripts.

## Use Cases

### Cloud Cost Forecasting

Generate accurate cost projections without being skewed by temporary credits or discounts.

### Post**-Credit Budget Planning**

Plan for ongoing operational costs after promotional credits expire.

### **Multi-Cloud Cost Comparison**

Compare true infrastructure costs across AWS, Azure, and Google Cloud using standard pricing.

### FinOps Reporting

Produce transparent, reproducible cost breakdowns for finance, operations, and leadership teams.

### Op**timization Impact Validation**

Measure and confirm savings from infrastructure or workload optimizations using a consistent methodology.

## References

* [AWS Cost Explorer Documentation](https://docs.aws.amazon.com/cost-management/latest/userguide/ce-what-is.html)
* [Azure Cost Management + Billing Documentation](https://learn.microsoft.com/en-us/azure/cost-management-billing/)
* [Google Cloud Billing Documentation](https://cloud.google.com/billing/docs)