This project analyzes a $170,870 (48.01%) budget variance in monthly cloud spend. Acting as a Senior FinOps Analyst, I developed a financial "Bridge" model to decompose a total spend of $526,770 against a baseline budget of $355,900.
The objective was to categorize spending drivers into Organic Growth, Operational Rate Changes, and Strategic Product Shifts, while isolating $1,380 in automated efficiency gains.
This visualization provides the "Source to Settlement" story for leadership, documenting how specific business decisions impacted the bottom line.
Using my proprietary decomposition engine, I visualized the relative weight of each driver to identify the primary cost catalysts.
Using a custom Python engine, I isolated five distinct drivers. This analysis proves that the majority of the variance was a Strategic Shift, not an operational failure.
| Driver | Impact ($) | Classification | Strategic Context |
|---|---|---|---|
| Mix Shift | +$120,250 | Strategic | Migration to P4d GPU instances to support AI/ML scaling. |
| Traffic Spike | +$22,050 | Organic | Unexpected egress surge in us-east-1 due to user growth. |
| Usage Growth | +$16,200 | Organic | Standard expansion of EC2 and Block Storage footprint. |
| Rate Change | +$13,750 | Operational | Unit price increase in Snowflake data warehousing. |
| Efficiency Gain | -$1,380 | Optimization | Favorable Variance via Azure storage lifecycle automation. |
- Proprietary Python Engine (
variance_engine.py): Automated Price-Volume-Mix (PVM) decomposition logic. - FOCUS 1.3 Specification: Normalized multi-cloud billing data across AWS, Azure, and Snowflake.
- Financial Modeling: Applied bridge analysis to separate price, volume, and mix variances.
- AI Cost Governance: 70% of the variance is driven by the GPU "Mix Shift." I recommend a Savings Plan commitment for the P4d instance family to reduce the effective hourly rate by ~30%.
- Egress Optimization: Investigate the us-east-1 traffic spike to determine if a CDN (CloudFront) optimization can mitigate future variable egress costs.
- Efficiency Scaling: The $1,380 Azure storage saving should be audited and replicated across AWS S3 buckets to maximize favorable variances.