As cloud adoption rises, managing operational costs has become a critical challenge for organizations. Amazon Web Services (AWS), the leading cloud provider, offers various services with flexible pricing models. However, without effective cost optimization strategies, cloud expenses can escalate quickly. This paper presents a comprehensive approach to cost optimization in AWS, combining traditional BEST PRACTICES with cutting-edge automation techniques using Large Language Models (LLM). The paper explores strategies such as right-sizing compute resources, leveraging Savings Plans, utilizing Spot Instances, optimizing storage, minimizing data transfer costs, and cleaning up unused resources. In addition to AWS-native services, the paper delves into advanced LLM-based solutions for anomaly detection, resource management, and predictive scaling. These AI-driven tools provide real-time insights and automate cost-saving measures, offering organizations intelligent ways to reduce their cloud footprint. The paper also introduces lesser-known AWS services, such as Instance Scheduler, S3 Select, VPC Endpoints, and Glacier Deep Archive, which can be utilized for further cost savings. By employing these strategies in tandem with automated solutions, organizations can significantly reduce their AWS spending while maintaining the scalability and performance of their cloud infrastructure. The findings suggest that organizations using AI-powered optimization tools and AWS BEST PRACTICES can cut costs by up to 60% while improving resource utilization. This paper serves as a detailed guide for businesses looking to optimize their AWS infrastructure and prepare for the future of AI-driven cloud management.

Disaster recovery (DR) is a critical component of any organization's cloud strategy, ensuring business continuity during catastrophic events. This paper provides an in-depth exploration of various DR strategies available within Amazon Web Services (AWS), including Backup and Restore, Pilot Light, Warm Standby, and Multi-Site Active/Active. We focus on optimizing Recovery Time Objective (RTO) and Recovery Point Objective (RPO) for critical workloads while balancing cost and complexity. The paper also explores integrating AWS-native services with cutting-edge automation techniques to ensure a robust disaster recovery solution that aligns with business continuity plans.

This paper presents a novel approach to intelligent log management, using large language models (LLMs) to detect and resolve anomalies in real-time. By extending the capabilities of a model like GPT for log analysis, we can enable the automatic detection of anomalies with actionable recommendations. The model was trained on a dataset of system logs with hyperparameters optimized for anomaly detection accuracy. The evaluation shows that the model successfully identified 85% of anomalies with a false positive rate of 5%, significantly outperforming traditional log analysis tools. Further, the model provides actionable recommendations in 90% of cases, which reduce the Mean Time to Recovery (MTTR) by 40%.