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@article{202548,
        author = {Shreya Agrawal and Harish Honrao and Raj Patil and Pranav Magar and Prof. Dr. Suvarna Bhagwat},
        title = {Pre-Deployment Cost Estimation Accuracy in IaC-Driven Platforms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {9050-9055},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202548},
        abstract = {Cloud cost overruns pose a significant challenge for enterprises, costing billions annually due to the unpredictable nature of cloud billing. While Infrastructure as Code (IaC) tools such as Infracost offer static pre-deployment cost estimations, these often fail to account for dynamic runtime behaviours including auto-scaling and variable data transfer. This paper presents the first empirical benchmark of pre-deployment IaC cost estimation accuracy within a real-world cloud deployment platform. Using the AutoStack platform and four distinct infrastructure blueprint archetypes—Static Website, Web App, Full Stack Application, and Microservices—we compare Infracost-generated estimates against actual incurred AWS costs. Our findings demonstrate that estimation error increases non-linearly with architectural complexity, quantified using Mean Absolute Percentage Error (MAPE). Static Websites achieve MAPE as low as 6.98%, whereas Microservices architectures reach 48.01%. Data transfer and auto-scaling events are identified as the primary deviation sources. We further propose a lightweight correction factor model to improve the reliability of future pre-deployment cost predictions.},
        keywords = {Cloud Cost Estimation, Infrastructure as Code, Terraform, Infracost, AWS, FinOps, MAPE, Auto-Scaling, Cost Optimization, Blueprint Architecture},
        month = {May},
        }