AI-Augmented Cloud Cost Optimization: Automating FinOps with Predictive Intelligence
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I2P110Keywords:
Cloud Cost Optimization, FinOps, Artificial Intelligence, Machine Learning, Predictive Analytics, Cloud Computing, Cost Engineering, Cloud Economics, Anomaly DetectionAbstract
Cloud computing has transformed IT of enterprises and is characterized by scalability, flexibility, and speed. Nevertheless, it also brings about challenges to cost control (with the dynamics of the models of pricing, elastic resource allocation, and the concept of multi-clouds). Financial operations practice FinOps has formed as a response to these issues as a way to collaborate between finance, operations, and engineering. However, legacy FinOps approaches are usually not sufficient, especially in dynamic clouds, where time is of the essence because insights and immediate reaction to them are necessities. The proposed paper provides an AI-augmented solution to cloud cost optimization, a combination of machine learning, predictive analytics, and automation as a FinOps practice. The framework we suggest uses historical cloud usage information, live telemetry, and business KPIs to predict the costs, find anomalies, and suggest intelligent scalability/rightsizing. The system automates costs assignment, budgeting and governance making it less, during cost assignment, budgeting and governance, the system automates cost assignment, budgeting and governance which reduces overheads done manually and makes it more continuous in accordance to the financial objective. Experimental experience using real-world e-commerce datasets shows the usefulness of this method, comparing the AI-enhanced FinOps with the legacy approaches. The outcomes indicate that there was a marked increase in cost savings, anomaly detection accuracy, and operational efficiency. The implementation challenges such like data quality, the model scalability, and the organizational readiness are also discussed in the paper. Lastly, we draw some conclusions on the ways forward in multi-cloud optimization, cost engineering in real time, and sustainability-conscious FinOps. This study highlights the paradigm shift in the field of cloud cost management that can be done through AI since it is an intelligent and proactive domain
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