AWS vs. Azure vs. Google Cloud for Data Science
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I2P101Keywords:
AWS, Azure, GCP, Cloud Computing, Data Science, Machine Learning, Artificial Intelligence, Big Data, Cloud PlatformsAbstract
This paper analyzes and compares three of the largest cloud computing platforms Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) based on their data science features. Scalability, flexibility, and performance are essential factors in determining the most effective cloud platform, especially as organizations increasingly depend on data-driven decision-making. The paper examines the data science tools, machine learning services, data storage systems, integration capacities, and pricing models of each platform. AWS offers a comprehensive ecosystem with advanced services such as SageMaker and Redshift. Azure provides seamless integration with Microsoft’s enterprise tools and robust AI capabilities through Azure Machine Learning. GCP, known for its open-source nature and support for TensorFlow, excels in big data analytics and model deployment. This comparative study aims to guide data scientists and businesses in selecting the most suitable platform for their data science workflows and organizational objectives by assessing each platform’s capabilities, constraints, and application areas.
References
1. Svitla Systems. (n.d.). Cloud platforms comparison: AWS vs Azure vs Google Cloud. Retrieved from https://svitla.com/blog/cloud-platforms-comparison
2. PlainEnglish. (n.d.). AWS SageMaker vs Google Vertex AI vs Azure ML. Retrieved from https://aws.plainenglish.io/aws-sagemaker-vs-google-vertex-ai-vs-azure-ml-cloud-ml-platform-reality-918c76059904
3. Tharwani, J., & Purkayastha, A. A. (2024). Cost-performance evaluation of general compute instances: AWS, Azure, GCP, and OCI. arXiv. https://arxiv.org/abs/2412.03037
4. Medium. (n.d.). Exploring AI Platforms: A comparative analysis of Azure, Google, and AWS. Retrieved from https://ip-specialist.medium.com/exploring-ai-platforms-a-comparative-analysis-of-azure-google-and-aws-d6c032ad970b
5. TechTarget. (n.d.). Compare Google Vertex AI vs Amazon SageMaker vs Azure ML. Retrieved from https://www.techtarget.com/searchenterpriseai/tip/Compare-Google-Vertex-AI-vs-Amazon-SageMaker-vs-Azure-ML
6. Amazon Web Services. (n.d.). Amazon SageMaker unified studio connecting with EMR and Redshift. Retrieved from https://docs.aws.amazon.com/sagemaker/latest/dg/studio-notebooks-emr-cluster.html
7. Google Cloud. (n.d.). Vertex AI: Training and deploying models. Retrieved from https://cloud.google.com/vertex-ai/docs/start/training-methods
8. Microsoft Learn. (n.d.). Google Cloud to Azure services comparison. Retrieved from https://learn.microsoft.com/en-us/azure/architecture/gcp-professional/services
9. AWS Documentation. (n.d.). Machine learning on AWS: SageMaker overview. Retrieved from https://aws.amazon.com/sagemaker/
10. Google Cloud. (n.d.). BigQuery overview. Retrieved from https://cloud.google.com/bigquery
11. Microsoft Learn. (n.d.). Azure Machine Learning overview. Retrieved from https://learn.microsoft.com/en-us/azure/machine-learning/overview
12. TechTarget. (n.d.). Cloud cost and performance comparison among AWS, Azure, and GCP. Retrieved from https://www.techtarget.com/searchcloudcomputing/feature/Cloud-cost-comparison-AWS-vs-Azure-vs-Google-Cloud
13. Svitla Systems. (n.d.). Data science services in major cloud platforms. Retrieved from https://svitla.com/blog/cloud-platforms-comparison
14. Medium. (n.d.). AI and data science in cloud computing: A review. Retrieved from https://ip-specialist.medium.com
15. Fujiyanti, V., Suranegara, G., & Ichsan, I. (2024). Comparative Analysis of Server-Based and Serverless Service Performance on Google Cloud Platform (GCP) (Case Study: Machine Learning Model Deployment). Journal of Information Systems and Informatics, 6(2), 1172-1194. https://doi.org/10.51519/journalisi.v6i2.773
16. Amazon Web Services. (2020). Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. arXiv. https://arxiv.org/abs/2012.08489
17. Tharwani, J., & Purkayastha, A. A. (2024). Cost-Performance Evaluation of General Compute Instances: AWS, Azure, GCP, and OCI. arXiv. https://arxiv.org/abs/2412.03037
18. Microsoft. (n.d.). Low-code Machine Learning with Azure. Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/shows/azure-videos/low-code-machine-learning-with-azure
19. Microsoft. (n.d.). Build an Architecture with Real-Time Machine Learning Inference and Low-Code Web Application UI on Azure. Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/azure/architecture/example-scenario/ai/deploy-real-time-machine-learning-model-application-ui
20. Google Cloud. (n.d.). BigQuery Overview. Retrieved from https://cloud.google.com/bigquery/docs/introduction
21. Google Cloud. (n.d.). Vertex AI: Unifying Machine Learning Development. Retrieved from https://cloud.google.com/vertex-ai/docs/start/introduction-unified-platform
22. Pamisetty, A. (2022). A Comparative Study of AWS, Azure, and GCP for Scalable Big Data Solutions in Wholesale Product Distribution. International Journal of Scientific Research and Modern Technology, 1(12), 71-88. https://doi.org/10.38124/ijsrmt.v1i12.466
23. Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98–115.
24. Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing the business perspective. Decision Support Systems, 51(1), 176–189.
25. Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7–18.
26. Amazon Web Services. (2024). AWS Cloud Overview: Official Documentation. Retrieved from https://aws.amazon.com/documentation/
27. Microsoft Azure. (2024). Azure Architecture and Data Science Services Overview. Retrieved from https://learn.microsoft.com/azure/
28. Google Cloud Platform. (2024). Google Cloud Documentation: Data and AI Services. Retrieved from https://cloud.google.com/docs
29. Patel, P., Ranabahu, A. H., & Sheth, A. P. (2009). Service level agreement in cloud computing. Cloud Workshops on Cloud Computing, IEEE.
30. Zeng, W., Zhao, Y., Ou, K., & Song, W. (2009). Research on cloud storage architecture and key technologies. Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, 1044–1048.
31. Li, M., Smirni, E., & Riska, A. (2019). Understanding cloud performance interference using workload decomposition. IEEE Transactions on Cloud Computing, 7(4), 1130–1143.
32. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
33. Li, A., Yang, X., Kandula, S., & Zhang, M. (2010). CloudCmp: Comparing public cloud providers. Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, 1–14.
34. Ghorbani, A., & Khatibi, V. (2021). Comparative analysis of AWS, Azure, and GCP cloud computing platforms. International Journal of Computer Applications, 183(15), 10–18.
35. Sultan, N. (2014). Cloud computing: Past, present, and future. International Journal of Information Management, 34(5), 748–758.
36. Alharthi, A., Yahya, F., Walters, R., & Wills, G. (2017). An overview of cloud services adoption challenges in higher education institutions. Proceedings of the IEEE 5th International Conference on Cloud Computing Technology and Science, 98–103.
37. Hossain, M. S., & Muhammad, G. (2020). Cloud-assisted industrial Internet of Things (IIoT) – enabled framework for health monitoring. IEEE Internet of Things Journal, 7(8), 7495–7503.
38. Prasanth Tirumalasetty, (2025). System and Method for Generating Privacy-Preserving Synthetic Health Data Using a Generative Adversarial Machine Learning Mode.
