CloudOptx: AI-Based Cloud Cost Optimization & Waste Detection System

Cloud computing costs have become a critical concern for organizations deploying workloads on public cloud platforms. Manual cost optimization approaches are inherently limited by the combinatorial complexity of resource allocation decisions across multiple services and time periods. This paper presents a research-grade Cloud Cost Optimization System built for Microsoft Azure that employs two nature-inspired evolutionary algorithms — the Genetic Algorithm (GA) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) — to automate and mathematically validate cost reduction strategies while preserving service performance. The system is implemented as a full-stack Python Flask web application that ingests real-world Azure billing export data, performs statistical anomaly detection using Z-score analysis, conducts linear regression trend forecasting, generates rule-based recommendations, and runs both single-objective (GA) and multi-objective (NSGA-II) evolutionary optimization. Experimental results on a 12-month dataset of 489 records across 10 Azure services demonstrate consistent cost reductions of 20–50% while maintaining service performance above user-specified thresholds. NSGA-II further produces a Pareto-optimal front of 20–50 non-dominated solutions per run, enabling decision-makers to select cost-performance trade-offs aligned with their specific business priorities. The system introduces a dynamic optimization engine that allows users to compare algorithm outputs on both the original dataset and custom-defined service costs simultaneously, enabling real-world deployment scenarios beyond the training data. This work demonstrates that evolutionary computation techniques are highly effective for cloud financial management (FinOps) and provides a reproducible, open-architecture framework for researchers and practitioners to further extend and validate cloud cost optimization techniques.