The transition from monolithic architectures to cloud-native microservices has revolutionized software deployment, yet it introduces significant challenges regarding resource orchestration, specifically concerning dynamic scaling and cost governance. While Kubernetes has emerged as the de facto standard for container orchestration, native reactive scaling mechanisms often suffer from "cold-start" latency during high-velocity traffic spikes, such as those experienced during industrial refinery turnarounds or large-scale e-commerce events. This paper proposes a novel, hybrid orchestration framework that integrates Ansible’s configuration management capabilities with Kubernetes’ Horizontal Pod Autoscaler (HPA) to optimize the trade-off between performance latency and operational cost on Azure PaaS. By employing a comprehensive experimental approach, we simulate massive small-file storage workloads and volatile request patterns to evaluate the efficacy of the proposed model against traditional static and purely reactive scaling methods. Our methodology involves a rigorous mathematical modeling of cost functions and system latency, supported by real-time telemetry data. The results indicate that the Ansible-integrated approach reduces cold-start latency by approximately 22% compared to standard Kubernetes configurations while maintaining a 15% reduction in cloud resource costs through intelligent down-scaling policies. Furthermore, the study highlights the critical role of network virtualization and metadata optimization in maintaining throughput. We conclude that enhancing the orchestration layer with predictive configuration management significantly improves the elasticity of cloud-native systems, offering a viable blueprint for cost-efficient, high-availability enterprise applications.