Optimizing Resource Allocation in Multi-Cloud Environments for Cost Efficiency and Scalability

Single-vendor-cloud minimization is the core to cost-performance equilibrium. Resource sharing among various providers is the aim of this research. With elastic software designs, predictive costing models, and dynamic load balancing, the method ensures optimum cost up to the point it is aligned with performance. Its methodology entails continuously monitoring resources, auto-scaling, and cost estimation tools. The experiment used Amazon Web Services (AWS) and Google Cloud Platform (GCP) data sets and built variables like instance cost, compute utilization rates, and response time. The proposed model uses Kubernetes and Terraform tools for automation to attain scalability in a non-intrusive manner and improve performance management. The experimental setup ensures the system's performance, enhanced cost-effectiveness, and scalability. The results confirm that strategic resource planning can make a 30% cost improvement and 25% performance. The study offers a critical reference model for companies seeking long-term multi-cloud management.