Cloud Cost Optimizer: An Intelligent Multi-cloud Cost Management System

• Author(s):

  Atharv Kulkarni | Atharv Kulkarni | Sushant Gaikwad | Vaishnavi Kale | Tushar Patil

• Keywords:

  Cloud Cost Optimization, Multi-Cloud Manage-ment, Resource Rightsizing, Idle Resource Detection, FinOps, AWS Cost Explorer, Azure Cost Management, Cloud Analytics

• Abstract:

  Cloud Computing Offers Flexible And Scalable Re-sources, But Uncontrolled Consumption Often Leads To Escalating Costs. Organizations Frequently Face Unexpected Cloud Bills Due To Forgotten Development Instances, Oversized VM Types, Unattached Storage Volumes, And Inefficient Autoscaling Settings. This Paper Presents Cloud Cost Optimizer, A Comprehensive Web-based Platform Designed To Analyze Cloud Usage And Billing Data, Detect Inefficiencies And Idle Resources, And Automatically Recommend Cost-saving Actions While Preserving Performance. The System Integrates Data Collection From Cloud Provider APIs (AWS, Azure, GCP), Preprocessing Modules, Analytical Models Using Rule-based Heuristics And Machine Learning, And A Recommendation Engine With An Interactive Dashboard For Visualization. The Platform Supports Three Primary User Roles: Administrators, Cloud Operators, And Finance Managers. Key Functionalities Include Idle Resource Detection, Rightsizing Recommendations, Storage Cleanup Alerts, And Reserved Instance Suggestions. The System Architecture Incorporates Secure API Integration, Role-based Access Control, And Real-time Data Processing. Expected Outcomes Include Ac-tionable Recommendations With Estimated Savings Of 20–30%. This Work Demonstrates How Modern Web Technologies And Data Analytics Can Effectively Optimize Cloud Expenditure While Maintaining Service Quality.

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Other Details

• Paper id:

  IJSARTV12I6105723

• Published in:

  Volume: 12 Issue: 6 June 2026

• Publication Date:

  2026-06-24

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