AI and Observability in Multi-Cloud Database Migrations: Enhancing Performance, Reliability, and Cost Efficiency

Abstract Multi-cloud architectures are transforming how databases are managed due to their ease of use, scalability, and flexibility. On the other hand, migrating fountain databases across different cloud environments comes with challenges such as performance, reliability, and cost. Many migration strategies lack system behavior telemetry, leading to failures and inefficiencies. Using AI techniques in observability allows for sophisticated machine and deep learning models to monitor, audit, and analyze complex systems such as databases to optimize the migration process. This research aims to explore the AI-powered observability framework for multi-cloud-powered database migration to improve performance, reliability, and cost. We propose an innovative AI framework that utilizes supervised learning for cost prediction, unsupervised learning for outlier detection, and reinforcement learning for resource management. The Proof of Concept (PoC) phase exhibits how artificial intelligence detects problems to deploy cloud resources automatically while shortening system failures beforehand. Experimental implementation with actual data yielded vital improvements that enhanced success rates alongside cost reduction and system reliability. The study creates an analytical path fororganizations, enabling them to conduct effortless intelligent cloud transitions with reduced expensesthrough AI and cloud database observability connections. The research provides foundational knowledge to develop innovations concerning autonomous AI-powered database migration with self-healing capabilities.