Mitigating the Cost Amplification of Platform Defaults in Lean Cloud Data Deployments Through Configuration Hygiene - Manuel O. Diaz (Department of Informatics, Faculty of Science and Technology, UIN Prof. K.H. Saifuddin Zuhri, Purbalingga, Indonesia, 2025)
Cloud data platforms offer flexibility, but their default configurations are implicitly optimized for enterprise-scale environments, leading to hidden, disproportionate costs for small-scale deployments. This paper introduces the concept of a silent tax, where platform defaults, such as minimum billing durations and over-provisioned column types, disproportionately inflate costs for small business organisations with modest compute budgets, exemplified by a 10 IPU per month scenario. These defaults act as regressive cost drivers, penalizing users unable to amortize inefficiencies. We employ an analytical cost-modelling and Monte Carlo simulation approach, using Snowflake and Informatica Intelligent Data Management Cloud (IDMC) as reference platforms, and validate it through a paired, parametric Monte Carlo experiment using truncated normal and lognormal distributions. Our findings indicate that fixed-cost components embedded in these defaults can consume approximately 65% of a minimal budget. Key mechanisms include 60-second warehouse billing minimums, schema-driven memory over-allocation, and CI/CD pipeline reconfiguration costs. The study demonstrates that cost waste in minimal deployments is predominantly structural. An optimization pathway focusing on configuration hygiene—including explicit data types, aggressive warehouse tuning, and schema-as-code—can reclaim over 80% of this overhead, aligning with FinOps guidance for small estates. This research addresses a gap in cloud cost governance, empowering small teams to leverage cost intelligence for sustainability, and suggests transparent disclosure of default cost impacts by providers.