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A Comparative Study of Cloud GPU Offerings for AI/ML Engineers
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Author(s): Sanjay P. Ahuja (University of North Florida, USA), Madhuri Golanakonda (University of North Florida, USA)and Sandeep Reddivari (University of North Florida, USA)
Copyright: 2026
Volume: 16
Issue: 1
Pages: 15
Source title:
International Journal of Cloud Applications and Computing (IJCAC)
Editor(s)-in-Chief: B. B. Gupta (Asia University, Taichung City, Taiwan)
DOI: 10.4018/IJCAC.406737
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Abstract
The expanding role of machine learning (ML) and artificial intelligence has become a primary reason behind the demand for high-performance GPUs. Cloud platforms such as Google Cloud Platform, Amazon Web Services, and Microsoft Azure provide scalable access to NVIDIA accelerators. However, variations in GPUs, pricing, usability, and deployment pipelines can be challenging for ML engineers. This paper presents a comprehensive study of GPU platforms across the major cloud providers, covering hardware families (T4, L4, A100, H100, H200, and emerging B200), virtual machine configurations, interconnect technologies, and pricing, including on-demand, spot, and reserved options. Various ecosystem factors were evaluated, including documentation quality, community support, ease of provisioning, and managed ML operations services, such as Vertex AI, SageMaker, and Azure ML. A literature review is provided of MLPerf results and benchmarks to analyze cost, performance, and scalability. The findings provide practical recommendations to guide ML engineers in selecting a suitable cloud for training, inference, and production.
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