Machine Learning-Driven Finops Strategies: Adaptive Scaling Models For Balancing Reliability And Cost In Multi-Cloud Data Platforms

Abstract

The complexity of multi-cloud ecosystems is pushing businesses to utilize vendor-agnostic financial operations (FinOps) to maximize cost savings while scaling with audits and reliability in mind. This paper presents a learning-based FinOps framework that emulates the human processes of supervised learning and reinforcement learning to automate adaptive scaling of workloads across heterogeneous vendor-neutral environments. The method demonstrates the capabilities of a predictive demand forecasting process augmented by a dynamic policy optimization that escapes traditional rule-based or vendor-specific scaling in a way that businesses can evaluate cost savings while achieving service-level objectives (SLA, RTO, RPO) and meeting compliance. Large-scale simulations of enterprise workloads provide significant operational and finance-oriented opportunities including a 28-35% decrease in cloud spending, and on average, the project also achieved a 22% increase in the SLA time dimension relative to baseline autoscaling projects. The reinforcement learning process is responsive to workloads that fluctuate and are cross-cloud dependent as the costs of cloud service change in real-time, achieving sustainable savings while not sacrificing availability, RTO, or governance/ audit. This remains true beyond enterprise costs, and could be an automation-leading opportunity for cost and resilience scale across other critical industries like banking, insurance, and healthcare. By integrating supervised with reinforcement learning, the proposed open-source, vendor-independent framework enables a replicable process for intelligent multi-cloud FinOps rather than just a technical optimization.