This blog is the first part of the series “FinOps Simplified.” Learn more about the entire blog series here.
We are all aware that businesses must adopt the cloud in order to accelerate digital transformation and the development of new business models. There is, however, a caveat. An increase in cloud adoption introduces new challenges, and each business model’s computation requirements are unique and dynamic. The initial wave of organizations rushing to adopt cloud technology is tapering off. The vast majority of organizations, if not all, are now well into their digital transformation process. They are now considering how to manage the elevated costs associated with cloud migration.
So, if you’re wondering what would be the best way to handle these complexities, Cloud FinOps (Financial Operations) is the answer. FinOps is an emerging cloud financial management discipline and cultural practice that enables engineering, finance, technology, and business teams to work together on data-driven purchasing choices. According to the State of FinOps 2023 report, FinOps is still in its infancy, with a 75% growth rate in the previous year and a 50% growth rate anticipated for the following year.
Datametica has worked with many large organizations in their migration and FinOps journeys and identified five pillars of FinOps for the best utilization of the cloud. These pillars are:
- Planning and budgeting
- Visibility
- Risk management
- Cost optimization
- Accountability
This blog will focus on one of these five pillars, cloud cost optimization.
Top five best practices for cloud cost optimization
To optimize cloud costs, it is necessary to standardize the availability and deployment of enterprise-wide resources. Optimal ROI (Return on Investment) is the primary objective of complex cloud system construction. Here are the top five best practices recommended by Datametica to maximize cloud cost optimization:
Best Practice #5
Avoiding Wastage: Everything is metered and billed on the cloud! The quickest way to reduce cloud costs is to identify and turn off/delete unused resources, such as storage, compute, etc.
Best Practice #4
Resizing: It entails right-sizing the over-provisioned instances and matching the cloud resources with the workload. By resizing resources, organizations can ensure that their cloud workloads are running efficiently and cost-effectively in a balanced manner, without paying for more capacity or features than necessary.
Best Practice #3
Avail available discounts: Cloud providers often offer various discounts, such as reserved instances, spot instances, and volume discounts, that can significantly reduce the cost of using their services. Organizations can take advantage of these discounts by analyzing their cloud usage patterns and choosing the appropriate pricing model.
Best Practice #2
Elastic scaling: Elastic scaling refers to the ability of cloud workloads to automatically adjust their computing resources based on changes in demand. With elastic scaling, cloud workloads can dynamically scale up or down in response to dynamic usage patterns, which can again help organizations avoid paying for unused or underutilized resources.
Best Practice #1
Workload Optimization: The last and most nuanced approach is to optimize your workload itself. It involves analyzing and optimizing and aligning the code, data, queries, tables, partitions, and more as per application usage. Datametica provides a unique offering and industry-leading solution for workload optimization on the cloud. While most of the industry focuses on infrastructure optimization (evaluate the size of resources, optimize storage and subscription, enable a chargeback model, monitor software licenses, and align the budgets to goals), Datametica has pushed the boundary further to optimize workloads and make them truly cloud-first and cloud-native.
Workload optimization can be achieved through three key strategies: optimizing data models, optimizing code, and optimizing design and architecture.
Optimizing data models involves analyzing data structure and legacy code to reduce storage requirements, reduce full data scans and improve performance. Optimizing code involves analyzing the code used by the workload and identifying areas to improve performance and reduce resource consumption. Optimizing design and architecture involves identifying opportunities for improvement to reduce bottlenecks, choose the right services and features, improve fault tolerance, and reduce manual intervention through automation and orchestration.
If you’re intrigued, stay tuned for the next article in our series that delves into our unique offering, or get in touch with us to learn more.
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About Datametica
A Global Leader in Data Warehouse Modernization & Migration. We empower businesses by migrating their Data/Workload/ETL/Analytics to the Cloud by leveraging Automation.
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FAQs
Q: What is cloud optimization?
A: Cloud optimization is the process of analyzing and improving the use of cloud resources to achieve better efficiency and cost savings. The goal is to ensure that the resources being used are being utilized in the most effective and cost-efficient way possible.
Q: What is cloud cost optimization?
A: Cloud cost optimization refers to the process of reducing cloud-related costs by identifying inefficiencies, eliminating waste, and identifying areas where cost savings can be achieved while still meeting business requirements. The objective is to minimize costs without compromising the performance, reliability, and scalability of cloud resources. Cloud cost optimization can help organizations achieve greater efficiency and cost savings in their cloud infrastructure.
Q: Why is cloud cost optimization important?
A: Cloud cost optimization is important because it allows organizations to use cloud resources more efficiently and effectively while minimizing unnecessary expenses. In short, cloud cost optimization helps organizations achieve a better return on investment for their cloud resources and achieve greater business agility and efficiency.
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