The Truth About Cloud Computing and Cost: Why Long-Running Data Workloads Are Often Better Run On-Premises

The allure of cloud computing is undeniable. It offers companies the ability to scale up their operations quickly, provision resources easily and affordably, and take advantage of a breadth of tools that may not be available on-premises. However, one of the common myths about cloud computing is that it is always a cost-effective solution for long-running data workloads. In this article, we will examine this myth and explain why, in certain scenarios, on-premises solutions may be the more cost-effective choice.

One of the factors that make data analytics workloads difficult to predict is their extensive time requirements. Training machine learning models takes a considerable investment in time, and if that time is extended even more so when the data is stored on the cloud servers. These models often have unique characteristics that can quickly become expensive. Luke Roquet, senior vice president of product marketing at Cloudera, recently spoke to a customer who received a $700,000 bill for a single data science workload running in the cloud. Such factors can deter companies from using the cloud as their primary environment.

Furthermore, local applications often need to be refactored or rebuilt for a specific cloud platform, which can lead to unforeseen expenses. David Dichmann, the senior director of product management at Cloudera, said there is no guarantee that the workload is going to be improved and that a company can end up being locked into one cloud or another.

While working in the cloud can be costly in some instances, the scalability and variety of tooling options make it a desirable target environment for many data analytics projects. Companies also believe that cloud computing saves them money because they don't have to purchase on-premises hardware, software, networking, or storage. Here's what is often missed though: most executives I've talked to say that moving an equivalent workload from on-premises to the cloud often results in about a 30 percent cost increase. That is just one factor to be considered when comparing on-premises and cloud deployments.

It's essential to consider all the factors before deciding whether to move your long-running data workloads to the cloud or keep them on-premises. This includes not only cloud computing costs but also workforce and training costs, data transmission and storage costs, software licensing fees, local interface costs, and costs of the time to commission and maintain new infrastructure.

Cloud computing is a useful and versatile tool, but it's not always the most cost-effective solution for long-running data workloads. In some cases, on-premises solutions may be the more cost-effective option. It's crucial, then, to consider all the factors before making a move to the cloud. Ultimately, the best decision depends on your organization’s specific needs, IT budget, and the details of your data. You can contact @Teddy Wagnac for expert insight into cloud computing solutions.

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