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Comparative Analysis of Infrastructure-as-Code and Native Cloud Services for Multi-cloud Interoperability

Author

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  • P. R. Shukla
  • V. M. Patil

Abstract

The rapid adoption of multi-cloud computing has enabled organizations to utilize services from multiple cloud providers to improve scalability, availability, and operational flexibility. However, differences in cloud architectures, proprietary application programming interfaces (APIs), and service management mechanisms continue to impede seamless interoperability across heterogeneous cloud environments. To overcome these challenges, two major approaches have emerged: vendor-neutral Infrastructure-as-Code (IaC) technologies, such as Docker and Terraform, and provider-specific native cloud interoperability services. Despite their growing adoption, a comprehensive comparison of these approaches remains limited. This paper presents a comparative analysis of Infrastructure-as-Code and native cloud services for achieving multi-cloud interoperability. The study evaluates both approaches using key criteria, including vendor independence, deployment portability, infrastructure automation, scalability, deployment complexity, cost efficiency, and vendor lock-in. The analysis shows that Docker and Terraform provide a cloud-agnostic deployment model that enhances application portability and reduces dependency on specific cloud vendors. In contrast, native cloud services offer optimized integration, simplified management, and improved performance within their respective cloud ecosystems but limit portability across heterogeneous platforms. The findings provide practical insights into the strengths and limitations of both approaches and offer guidance for selecting appropriate interoperability strategies for cloud-native applications deployed in multi-cloud environments.

Suggested Citation

  • P. R. Shukla & V. M. Patil, 2026. "Comparative Analysis of Infrastructure-as-Code and Native Cloud Services for Multi-cloud Interoperability," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 12(4), pages 101-108, July.
  • Handle: RePEc:jbh:ijsrcs:v12:y2026:i4:id:2116
    DOI: 10.32628/CSEIT2612414
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2612414
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