This paper investigates the design, implementation, and evaluation of a cloud-based dynamic content caching system aimed at optimizing performance and minimizing costs in cloud environments. The primary objective is to enhance the efficiency of content delivery in cloud computing platforms, focusing on elastic scaling, monitoring, and cost management. Using Amazon Web Services (AWS), the system leverages auto-scaling techniques, load balancing, and real-time monitoring to adjust resource allocation dynamically based on varying traffic loads. The methodology involves implementing the system on AWS, testing it under different load conditions using JMeter for performance evaluation, and analyzing the cost implications of various auto-scaling strategies. The paper includes a detailed comparison of different instance types and the impact of instance monitoring on system reliability. The results of the evaluation demonstrate the system’s ability to effectively manage elasticity, ensuring optimal performance with minimal resource wastage. Key contributions of the paper include a performance-driven cost analysis, an assessment of auto-scaling behavior, and insights into the integration of real-time monitoring for dynamic resource allocation. The study also discusses potential limitations, such as the lag in monitoring data, and suggests directions for future work, including improved integration with cloud storage and messaging services for better operational efficiency.