International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Cloud computing has become an essential part of modern software systems, but managing the cost of cloud services is often difficult for organizations. Companies typically receive their cloud billing information as large CSV reports that contain detailed usage and cost data. While these reports contain useful information, understanding them manually can be confusing and time-consuming. It becomes especially difficult to identify sudden cost increases or understand which services are responsible for higher spending.

CloudWise is an intelligent cloud expense analysis system designed to simplify the process of understanding cloud billing data. Instead of manually examining spreadsheets, users can upload their billing dataset directly into the system. The application automatically processes the data and converts it into meaningful insights such as monthly cost trends, service-wise spending distribution, and abnormal cost spikes.

The main feature of CloudWise is its ability to detect unusual cloud spending patterns using statistical anomaly detection. When the system identifies a sudden increase in cost for a specific month, it highlights that period and analyzes which cloud service contributed most to the spike. The system also predicts future cloud expenses using a regression-based forecasting model, allowing organizations to plan budgets more effectively.

Developed using Python Flask, Pandas, NumPy, and SQLite, the system presents its analysis through an interactive dashboard with charts, cost breakdowns, and automated insights. By transforming complex billing reports into simple visual information, CloudWise helps organizations monitor cloud spending more efficiently and respond quickly to unusual cost behavior.

Keywords: Cloud Cost Optimization, Anomaly Detection, Z-Score, Linear Regression Forecasting, Python Flask, SQLite, Interactive Dashboard, Statistical Analysis

Downloads: | DOI: 10.17148/IJARCCE.2026.15250