My main use case for DoiT is at an MSP level, focusing on cost reporting across multiple customer accounts. Second is anomaly detection, cost allocation by business unit or environment or product, and final would be commitment management such as savings plans or reserve instances. I can give you an example around the commitment management and saving part relating to how I use DoiT. Our reserve instances and saving plans had commitment coverage gaps and some overcommitment risks present in the environment. We use the Flexsave model of DoiT to analyze historical utilization trends and automate commitment purchasing instead of focusing on manual forecasting processes. We also use it to compare versus commitment spend scenarios and reduce the risk of very long-term reserve instances if they are not required. As an outcome of all this activity, we increased our commitment coverage from 40% to 70%. We achieved approximately 15% of additional compute savings beyond just right-sizing.
Cloud Analytics transforms raw data into actionable insights, enhancing decision-making capabilities. It leverages scalable cloud resources for data collection, processing, and analysis, providing businesses with a competitive edge.Cloud Analytics offers a flexible approach to handling vast datasets, integrating seamlessly with existing business tools to optimize data analysis. Companies utilize this technology to gain deeper insights into their operations, customer behavior, and market...
My main use case for DoiT is at an MSP level, focusing on cost reporting across multiple customer accounts. Second is anomaly detection, cost allocation by business unit or environment or product, and final would be commitment management such as savings plans or reserve instances. I can give you an example around the commitment management and saving part relating to how I use DoiT. Our reserve instances and saving plans had commitment coverage gaps and some overcommitment risks present in the environment. We use the Flexsave model of DoiT to analyze historical utilization trends and automate commitment purchasing instead of focusing on manual forecasting processes. We also use it to compare versus commitment spend scenarios and reduce the risk of very long-term reserve instances if they are not required. As an outcome of all this activity, we increased our commitment coverage from 40% to 70%. We achieved approximately 15% of additional compute savings beyond just right-sizing.