Intelligent Cost Optimization System for Multi-Cloud Experience Platforms
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I2P121Keywords:
Multi-Cloud Cost Optimization, FinOps, Cloud Automation, AI-Driven Optimization, Experience Platforms, Cloud Governance, Cost Anomaly Detection, Resource Rightsizing, Predictive Analytics, Cloud Workload ManagementAbstract
The rapid expansion of multi-cloud adoption has been largely responsible for the significant changes in how agility, resilience, and best-of-breed services requirements are met. In fact, it has brought a lot of flexibility to the modern digital experience platforms but at the same time, it has created cost-management problems that manual or rule-based traditional approaches can no longer solve efficiently. Organizations that spread their activities over AWS, Azure, Google Cloud, and edge environments are regularly confronted with fragmented billing models, underutilized workloads, unpredictable consumption patterns, and a lack of unified visibility, which in total result in operational inefficiencies and more spending than necessary. This document introduces an Intelligent Cost Optimization System intended to solve these kinds of issues by utilizing AI-driven forecasting, actual time analytics, autonomous policy enforcement, and workload-aware automation. The method involves the use of cloud-native telemetry, machine-learning models for anomaly detection, and demand prediction along with automated remediation actions like rightsizing, scheduling, dynamic provisioning, and spend-limit governance. The system, through different case study scenarios such as a retail experience platform scaling for seasonal demand, a healthcare provider adopting hybrid multi-cloud for compliance and a media company optimizing data-intensive streaming workloads, was consistently able to demonstrate improved cost transparency reduced operational overhead and sustained cost savings without performance or user experience compromise. Some of the essential findings make continuous optimization, context-aware decision-making and proactive anomaly detection in dynamic cloud environments crucial. The insights also show that organizations are better off if the optimization is integrated into CI/CD processes, operational playbooks and cross-cloud governance frameworks rather than being treated as a one-time initiative.
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