This paper presents a comparative analysis of leading cloud platforms for Generative AI applications. We evaluate performance, scalability, cost, and ecosystem support for AI workloads. The rapid evolution of generative artificial intelligence (AI) has significantly increased the demand for scalable and robust cloud infrastructure. This paper presents a comparative analysis of major cloud platforms, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), focusing on their capabilities to support generative AI applications. We examine key aspects such as infrastructure scalability, cost efficiency, and the availability of specialized AI services. Furthermore, we discuss the importance of well-architected frameworks and best practices for deploying scalable AI solutions. The paper also explores the strategic collaborations and advancements in supercomputing infrastructure that are driving the future of generative AI. Generative AI (GenAI) is rapidly transforming various industries, demanding scalable and cost-effective infrastructure. Cloud platforms like Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and Oracle Cloud Infrastructure (OCI) are vying to provide the necessary tools and services. This literature review examines recent publications and articles discussing the capabilities, architectures, and cost considerations of these platforms in the context of GenAI application development and deployment. We categorize these resources based on their focus: (1) comparative analyses of cloud platforms, (2) GenAI infrastructure and application development, (3) Retrieval-Augmented Generation (RAG) solutions, and (4) scalability and cost optimization strategies. This review aims to provide a comprehensive overview of the current state of GenAI in the cloud, highlighting the strengths and weaknesses of each platform and identifying key trends and challenges.