Managing cloud costs shouldn’t feel like guesswork, but for small teams without FinOps tools or dedicated analysts, it often does.
One company was juggling cloud invoices and CSV exports in Excel, manually reviewing usage and trying to spot patterns on their own. No alerts. No forecasting. Just… reacting.
They knew they were overspending — they just didn’t know where, why, or how to fix it.
The Opportunity
That’s when I stepped in. My goal: Replace manual cost analysis with a smart, automated, Python-powered FinOps solution.
I wanted to build something lightweight, visual, and actually useful — not just another dashboard, but a tool that helped:
Detect unusual cloud spend spikes
Forecast future AWS costs
Identify top services and underused resources
Give teams something to act on, not just stare at
The Build – Tools & Tech
Language: Python
Libraries: Pandas, Matplotlib, Seaborn
Platform: Google Colab / Jupyter Notebook
Data: AWS CUR / Billing Data
Optional Hosting: Streamlit or saved to PDF
The Experience – What It Does
1. Monthly Spend Breakdown
Helps teams understand cost fluctuations over time.
2. Top 5 Cost Drivers
Instantly surfaces the most expensive AWS services
3. Department-Level Cost Allocation
Makes cost ownership transparent
4. Cloud Spend Forecasting
Predicts where costs are heading, 6 months in advance.
5. Usage vs. Spend
Flags cases where usage is flat but costs are high.
6. Anomaly Detection Alerts
Automatically highlights cost spikes.
The Outcome
What started as a static spreadsheet problem turned into a dynamic, insight-driven FinOps solution.
📈 Strategy: Forecasting and optimization in one plan
⏱️ Time saved: No more manual Excel wrangling
💰 Costs saved: Identified $30K+ in wasted monthly spend
🔍 Visibility: Clear reports for finance, engineering, and execs
Why It Matters This wasn’t built for data scientists. It was built for cloud users who need answers — not just numbers. Because understanding your cloud bill shouldn’t require a PhD in Excel formulas.
