/ Case Studies / Concurrency Helps Global Manufacturer with cloud deployment and scaling & cost tracking using Cloudyn Case Studies Concurrency Helps Global Manufacturer with cloud deployment and scaling & cost tracking using CloudynOverviewOne of the practical challenges for any company migrating workloads to Azure is ensuring the costs associated with doing so are accurately tracked, charged to the correct business unit—and minimized.Our client, a global manufacturing firm, requested our assistance with a project to ensure appropriate scaling and business cost tracking. Representatives from our client’s IT team and corporate finance and accounting department rightly recognized the thorny business problems that can emerge from:Subscription costs not tied correctly (or at all) to a specific business unit’s budgetCloud resources sized too large—and therefore costing more than necessaryTo ensure our client avoided these common problems, we provided strategic and technical counsel on these topics as part of a broader Azure engagement around establishing a fully complete “minimum viable product” deployment. We also deployed Cloudyn, a Microsoft “right-sizing” tool for Azure resources.SolutionA good starting point for ensuring appropriate cloud resource usage is examining the existing cost-charging model for on-premises resources. In this case, our client’s existing model provided a solid basis to translate to the cloud. However, as is usually the case, the relationship wasn’t “apples to apples,” so we helped our client work through how to best accomplish the translation details.In that process, our client recognized opportunities to improve alignment of cost charges in general, and so the migration process led to even broader process improvements than those focused on the Azure workloads.The Cloudyn tool helps solve the problem of Azure resources getting initiated and then paid too little attention to. This can lead to finance departments receiving a large bill with no one able to explain just what the charges are for.Cloudyn addresses this problem by monitoring virtual machines in use in Azure to gather real-time data about their use and the subscriptions relating to them. We created a framework rule set to govern Cloudyn’s deployment, ensuring that the right data is gathered and reported on. With those rule sets in place, Cloudyn can generate suggestions such as:Downsizing from a more expensive to a less expensive virtual machine for a particular workloadTurning off a workload during overnight hours or other periods of regular inactivityWe helped our client set up a process to review Cloudyn reports on an ongoing basis—weekly, monthly or quarterly, depending on the workload—and then, over time, identify patterns. We advised our client on how to approach reports over time, taking into consideration that in any given day or week, inconsistencies can occur; these need to be correlated with overarching results.Once suggestions were clear, our client’s accounting representative could take recommendations to business unit managers to discuss making cost-saving changes. We then worked with our client’s the global director of architecture to make the actual configuration changes at opportune times, ensuring that the resulting short outages wouldn’t negatively impact business functions.The result of this project was confidence that Azure resources could be wisely applied. We helped our client save time, money and resources—both now and into the future.