For the last ten years, Flexera has published our annual State of the Cloud Report. And consistently across those reports, more than 75 percent of respondents have indicated that a lack of cloud expertise is one of their top challenges. With the proliferation of multi-cloud environments (now up to 92 percent in the most recent report), this challenge will continue to grow as teams will need to manage and monitor each of the cloud environments in their IT estates. It’s for this very reason that Cloud Management Platform was born more than a dozen years ago, and for the continued expansion of multi-cloud tools such as Terraform, Ansible and the like. 

Similar expertise challenges exist in the cloud cost optimization space. Services such as AWS Cost Explorer, Azure Advisor and the Google Cost Management tools are helpful in managing and optimizing cloud costs within their respective environments. But their cross-cloud capabilities are extremely limited or nonexistent, which makes sense—how much time and effort would you want to spend helping your customers save money with one of your competitors? For a multi-cloud team to be effective in identifying and optimizing costs across disparate cloud environments, there are essentially two options: the folks responsible for cost optimization in your organization can become well-versed in the specific tools for each of the cloud environments you utilize, or you can look for cross-cloud tools that implement desired functionality across your multi-cloud estate. These multi-cloud tools abstract the provider-specific details associated with cost-optimization tasks and enable your team to focus their efforts on learning and using a single tool, as opposed to expending the time and effort to master multiple tools that do the same thing, just in different ways (and places). 

For example, the concept of rightsizing an instance (finding the most cost-efficient instance type/size that meets the needs of an application) is basically the same regardless of provider: You look at your historical usage of an instance over a specified period of time and determine if you can reduce the size (and therefore the cost) of that instance and still meet your performance targets. (Upsizing, or moving to a larger instance size, is also a possibility in cases where performance bottlenecks are discovered, but this tends to be the exception and not the rule.) Each of the provider tools mentioned previously allow for the implementation of rightsizing, but they may be prescriptive in how they do it—they may specify a time period that cannot be changed, or their definition of underutilized may not align with how you need to define it. For example, the AWS Rightsizing Recommendations use data from the last 14 days to determine if an instance is underutilized (or idle). What if your use case has a much shorter (or longer) cadence? If your workload follows a predictable pattern on a daily basis, then you will only need a few days to determine if you are over- or under-provisioned. Using 14 days to make that determination could result in wasted spend while you wait to get to the answer. Conversely, Azure Advisor uses seven days of historical data to make assessments and recommendations. Using these two provider tools to accomplish the same task for a same/similar workload in the respective cloud environments will require not just additional training on how these tools work, but extra effort to decipher the recommendations as they are based on different time scales (and different definitions of underutilized). By using a single configurable tool, you can set your own rightsizing rules with regard to the evaluation time period, as well as what constitutes underutilized in your particular use case.  

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The rightsizing illustration above is just one example of how and where the providers’ cost optimization tools differ in their implementation and configuration, but these differences permeate the other cost optimization functionalities as well.   

As cloud provider offerings continue to expand and mature, the need for skilled resources will continue to expand as well. Finding ways to circumvent the need to continually learn new tools, or to learn new functionality added to existing tools, will shorten the learning curve for the members of your cloud team and allow them to focus on your core business activities. By adopting a single, multi-cloud tool that can address your cost optimization needs in all the environments you utilize, you’ll be able to reduce your training requirements, as well as be able to utilize a consistent implementation of cost optimization functionality across your multi-cloud estate. 

Learn more about Flexera One’s Cloud Cost Optimization.