FISO: A Real-Time Multi-Cloud Financial Intelligence Platform with Adaptive Machine Learning for Predictive Cost Optimization

Abstract

For companies with a multi-cloud strategy, cost management has turned into a real challenge. It is not just a matter of tracking costs; existing tools provide a retrospective view of costs, not a prospective one, which makes planning difficult. In this paper, the FinOps-based Intelligent Serverless Orchestrator (FISO) is described as a real-time predictive system based on machine learning and streaming data processing, which helps mitigate cost overruns before the financial run rate is impacted. FISO achieves a forecast accuracy of 93.4% (MAPE: 8.3%) and detects cost anomalies within 4 minutes, compared to 24–48 hours with conventional tools. It combines forecasting (using the Facebook Prophet algorithm) and cost anomaly detection (Isolation Forest) with a natural language interface, enabling users to query costs conversationally. FISO was evaluated on real production data spanning 1–2 months across AWS, Azure, and Google Cloud, with the system ingesting data every 2 minutes. API calls remained under 200 ms while processing thousands of metrics per minute. Most importantly, organizations using FISO reduced budget surprises by 64% and improved forecast accuracy from 69.8% to 93.4%. The main contributions are: (1) a practical framework for normalizing cost data across disparate cloud providers; (2) a machine learning pipeline that self-adjusts to incorporate shifting usage trends; (3) a natural language dialogue interface for financial cost queries; and (4) empirical evidence of production-level performance confirming reliability in operational settings.