EconPapers    
Economics at your fingertips  
 

Terraform-Based AWS Infrastructure Automation and Cost Optimization: An Experimental Evaluation

Gargi Verma

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2026, vol. 12, issue 3, 929-937

Abstract: Cloud computing has become an essential component of modern information technology infrastructure; however, manually managing cloud resources can result in deployment delays, configuration inconsistencies, resource underutilization, and unnecessary expenditure. This study evaluates Terraform-based Infrastructure as Code (IaC) for automating AWS infrastructure deployment and optimizing cloud resource utilization and cost. A comparative evaluation was conducted between manual deployment and Terraform-based deployment. The evaluation considered deployment time, manual deployment steps, configuration errors, AWS monthly cost, EC2 resource utilization, idle resources, and resource tagging compliance. The implementation results show that Terraform reduced deployment time from 26 minutes to 8 minutes, representing an approximately 69.2% reduction, while configuration errors decreased from four to zero. Monthly AWS expenditure decreased from ₹2,000 to ₹1,400, resulting in an estimated 30% cost reduction. Average EC2 CPU utilization increased from 18% to 47%, idle resources decreased from 14 to 2, and resource tagging compliance improved from 65% to 100%.

Keywords: AWS; Terraform; Infrastructure as Code; Cloud Computing; Cloud Cost Optimization; EC2; Resource Utilization; Infrastructure Automation; DevOps (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT26124222
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT26124222 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT26124222/CSEIT26124222 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v12:y2026:i3:id:2126

DOI: 10.32628/CSEIT26124222

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma ().

 
Page updated 2026-09-29
Handle: RePEc:jbh:ijsrcs:v12:y2026:i3:id:2126