AI-Powered Recommendation System for Cost-Effective Monitoring and Self-Healing Across Cloud Platforms

Modern cloud and DevOps environments have become increasingly complex, making traditional infrastructure management approaches less effective. Many organizations still rely heavily on manual processes for system configuration, monitoring, and recovery, which can lead to human errors, service downtime, security risks, and increased operational costs. This paper presents an AI-powered recommendation system designed to simplify and improve cloud infrastructure management. The proposed system continuously monitors system performance, analyses infrastructure behavior, and provides intelligent recommendations for optimal resource configuration and system stability. By integrating artificial intelligence techniques such as machine learning, predictive analytics, reinforcement learning, and natural language processing, the system can detect early warning signs, predict potential failures, and initiate automated self-healing actions before issues escalate into major outages. Unlike traditional reactive monitoring approaches, the system focuses on proactive infrastructure management by studying workload patterns and traffic trends to recommend timely resource scaling and security improvements based on historical system behavior. The research highlights how AI-driven solutions can enhance reliability, reduce operational overhead, and improve efficiency in modern cloud and DevOps environments.