Automated FinOps Optimization Frameworks for Cost-Aware Cloud-Native Applications

Abstract

Cloud-native application deployments increase cost management complexity but enhance flexibility and scalability due to dynamic resource utilization and alternative pricing models. FinOps approaches that involve static budgeting and manual monitoring cannot optimize elastic cloud expenditures dynamically. Task information, predictive cost modeling, and dynamic policy enforcement automate FinOps optimization for cost-aware resource orchestration in this research. Using advanced analytics, machine learning predictions, and real-time telemetry, the platform maintains application performance and compliance at low cost. Continuous cost feedback loop design, adaptive cost rules, and empirical resource efficiency validation across cloud platforms contribute. Architectural trade-offs, scalability challenges, and operational restrictions are addressed in our cloud-native application lifetime cost-awareness research. Optimization of cloud infrastructure and financial governance are affected.