Optimizing solution design for $450K+ cost savings

Inconsistent resource tags, naming guidelines, and a lack of a thoughtful tagging strategy were a few of many challenges. Our experts found significant gaps in the design within their data engineering solution that was causing a continuous increase in the cloud spends linearly as they scaled, thus affecting business growth.

We did a Prove Value Workshop benchmarking the existing spend & performance metrics. We also did a FinOps maturity assessment to understand the current and the three-year vision state. After identifying potential saving opportunities, we rightsized the over-provisioned resources and identified cloud waste, set up cost dashboards to create better visibility as well as set up budgets and alerts for continuous monitoring & control. For optimized cost allocation and improved visibility, we developed a standardized tagging strategy wherein we created intelligent Azure PowerShell scripts for resource tag backup, report generation, and bulk modification.

We established a backlog for easy decision-making that was tagged with a 2x2 priority matrix with Low Savings, High Effort, High Savings, High Effort, Low Savings, Low Effort, and High Savings, Low Effort. We reduced the cost by 32% with Azure Data Factory (ADF) short-term recommendations and 46% with ADF long-term re-design. The cost dashboards, custom cost policies, budgets & cost alerts helped our customer with enhanced visibility into their cloud spend, enabled better decision-making and above all better control with cost governance.