Cloud Platforms for Developing Generative AI Solutions: A Scoping Review of Tools and Services
Dhavalkumar Patel, Ganesh Raut, Satya Narayan Cheetirala, Girish N Nadkarni, Robert Freeman, Benjamin S. Glicksberg, Eyal Klang, Prem Timsina
TL;DR
This scoping review maps the cloud platforms landscape for developing generative AI (GENAI) solutions across AWS, Azure, GCP, IBM Cloud, Oracle Cloud, and Alibaba Cloud, with expansions to Databricks and Snowflake. It analyzes compute, storage, edge, serverless, and API-service ecosystems, and assesses security, governance, cost, and scalability, supported by case studies in healthcare, finance, and entertainment. The work highlights how HPC, edge computing, data lakes/warehousing, and MLOps tooling enable end-to-end GENAI development, while also flagging challenges such as vendor lock-in, data governance, and sustainability. It provides practical guidance and future directions, including multi-cloud strategies, responsible AI, and the integration of emerging technologies like quantum computing and federated learning. Overall, the paper offers a comprehensive guide for practitioners and researchers to navigate cloud-based GENAI deployment, optimization, and governance in a rapidly evolving field.
Abstract
Generative AI is transforming enterprise application development by enabling machines to create content, code, and designs. These models, however, demand substantial computational power and data management. Cloud computing addresses these needs by offering infrastructure to train, deploy, and scale generative AI models. This review examines cloud services for generative AI, focusing on key providers like Amazon Web Services (AWS), Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud, and Alibaba Cloud. It compares their strengths, weaknesses, and impact on enterprise growth. We explore the role of high-performance computing (HPC), serverless architectures, edge computing, and storage in supporting generative AI. We also highlight the significance of data management, networking, and AI-specific tools in building and deploying these models. Additionally, the review addresses security concerns, including data privacy, compliance, and AI model protection. It assesses the performance and cost efficiency of various cloud providers and presents case studies from healthcare, finance, and entertainment. We conclude by discussing challenges and future directions, such as technical hurdles, vendor lock-in, sustainability, and regulatory issues. Put together, this work can serve as a guide for practitioners and researchers looking to adopt cloud-based generative AI solutions, serving as a valuable guide to navigating the intricacies of this evolving field.