A Comparative Analysis on the Services Offered by Amazon Web Services and Microsoft Azure Article Swipe
Shahid Hameed
,
Syed Hashim Raza Bukhari
,
Syeda Umm E Abiha Rizvi
,
Muhammad Tahir
,
Farhan Aadil
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/cpe.70215
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/cpe.70215
Different small or large‐scale enterprises came to the conclusion that moving data to the cloud is more convenient. In recent decades, AWS and Azure have tried their best to provide comparatively affordable and instant solutions. Both cloud platforms are extensive to understand, so it is difficult for users to make the right choice because of data confidentiality, better storage capacity, and, most importantly, to select a reasonable platform. This paper is an in‐depth analysis of the comparable capabilities and services offered by Azure and AWS. The strengths and weaknesses of both platforms are highlighted considering security, storage, pricing, and machine learning services. It critically examines the implementation of the linear regression model and its performance. Furthermore, a survey was conducted to ensure that the users know which cloud service provider is preferred. On the basis of machine learning services, people with more experience in the cloud preferred Azure Machine Learning Studio over Amazon SageMaker. Azure outperformed AWS, achieving an accuracy of 70%, whereas AWS managed only 60%. Similarly, in cases of security, storage, and pricing, Azure was preferred because of its flexible, easy‐to‐use services. Therefore, from the methodology, it is concluded that Azure was preferred over AWS. However, the right choice can only be made by considering the business needs.
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- Landing Page
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- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cpe.70215
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All OpenAlex metadata
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https://openalex.org/W4412847122Canonical identifier for this work in OpenAlex
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https://doi.org/10.1002/cpe.70215Digital Object Identifier
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A Comparative Analysis on the Services Offered by Amazon Web Services and Microsoft AzureWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-07-31Full publication date if available
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Shahid Hameed, Syed Hashim Raza Bukhari, Syeda Umm E Abiha Rizvi, Muhammad Tahir, Farhan AadilList of authors in order
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https://doi.org/10.1002/cpe.70215Publisher landing page
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hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cpe.70215Direct OA link when available
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Amazon rainforest, Computer science, World Wide Web, Web service, Database, Biology, EcologyTop concepts (fields/topics) attached by OpenAlex
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| abstract_inverted_index.Amazon | 153 |
| abstract_inverted_index.Studio | 151 |
| abstract_inverted_index.better | 58 |
| abstract_inverted_index.choice | 53, 201 |
| abstract_inverted_index.ensure | 122 |
| abstract_inverted_index.linear | 110 |
| abstract_inverted_index.moving | 11 |
| abstract_inverted_index.needs. | 210 |
| abstract_inverted_index.people | 140 |
| abstract_inverted_index.recent | 20 |
| abstract_inverted_index.select | 65 |
| abstract_inverted_index.survey | 118 |
| abstract_inverted_index.Machine | 149 |
| abstract_inverted_index.because | 54, 179 |
| abstract_inverted_index.instant | 34 |
| abstract_inverted_index.machine | 100, 137 |
| abstract_inverted_index.managed | 165 |
| abstract_inverted_index.offered | 81 |
| abstract_inverted_index.provide | 30 |
| abstract_inverted_index.service | 129 |
| abstract_inverted_index.storage | 59 |
| abstract_inverted_index.whereas | 163 |
| abstract_inverted_index.ABSTRACT | 0 |
| abstract_inverted_index.However, | 198 |
| abstract_inverted_index.Learning | 150 |
| abstract_inverted_index.accuracy | 160 |
| abstract_inverted_index.analysis | 74 |
| abstract_inverted_index.business | 209 |
| abstract_inverted_index.decades, | 21 |
| abstract_inverted_index.examines | 105 |
| abstract_inverted_index.learning | 101, 138 |
| abstract_inverted_index.pricing, | 98, 175 |
| abstract_inverted_index.provider | 130 |
| abstract_inverted_index.services | 80 |
| abstract_inverted_index.storage, | 97, 173 |
| abstract_inverted_index.Different | 1 |
| abstract_inverted_index.achieving | 158 |
| abstract_inverted_index.capacity, | 60 |
| abstract_inverted_index.concluded | 191 |
| abstract_inverted_index.conducted | 120 |
| abstract_inverted_index.difficult | 46 |
| abstract_inverted_index.extensive | 40 |
| abstract_inverted_index.flexible, | 182 |
| abstract_inverted_index.platform. | 68 |
| abstract_inverted_index.platforms | 38, 92 |
| abstract_inverted_index.preferred | 147, 178, 195 |
| abstract_inverted_index.security, | 96, 172 |
| abstract_inverted_index.services, | 139 |
| abstract_inverted_index.services. | 102, 184 |
| abstract_inverted_index.strengths | 87 |
| abstract_inverted_index.SageMaker. | 154 |
| abstract_inverted_index.Similarly, | 168 |
| abstract_inverted_index.Therefore, | 185 |
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| abstract_inverted_index.comparable | 77 |
| abstract_inverted_index.conclusion | 9 |
| abstract_inverted_index.critically | 104 |
| abstract_inverted_index.experience | 143 |
| abstract_inverted_index.in‐depth | 73 |
| abstract_inverted_index.preferred. | 132 |
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| abstract_inverted_index.considering | 95, 207 |
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| abstract_inverted_index.enterprises | 5 |
| abstract_inverted_index.highlighted | 94 |
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| abstract_inverted_index.importantly, | 63 |
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| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5058076434, https://openalex.org/A5081911060 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I4210107944, https://openalex.org/I4387154028, https://openalex.org/I4626487 |
| citation_normalized_percentile.value | 0.42307565 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |