The six most damaging mistakes in cloud economics to avoid

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The cloud has tremendous value potential, but only for businesses that understand—and adapt to—the realities of cloud economics.

Although businesses may be migrating to the cloud, their mentality is still anchored in the legacy world of on-premises computing. With commercial and financial models built on decades-old traditional IT practices that are predicated on “owning” IT instead of “consuming” it, that way of thinking has proven difficult to alter for many companies.

As a result, companies are developing business cases, negotiating contracts, and doing economic calculations without considering the many cloud-specific financial strategies and models. In addition to the value received from the cloud falling significantly short of expectations as a result, this has the potential to endanger cloud programs themselves in some situations, with some companies even considering reversing their course.

The following six cloud economics mistakes that businesses make are the most pervasive and harmful, according to our research.

Creating a business case that combines the economics of the first day and the first year

When presenting a business case for moving to the cloud, an emphasis on the “lift and shift” strategy—that is, on a targeted migration of current applications with little remediation—complicates accurate estimations of cloud value.

With this strategy, businesses can immediately create a cloud footprint and begin developing cloud capabilities from day one. The economic advantages mostly result from reduced hosting, storage, and maintenance expenses. Sadly, these advantages are frequently muted since businesses continue to suffer from the operational inefficiencies and technical debt of the migrated applications, which prevents them from benefiting from the cloud’s ability to provide dynamic infrastructure.

One benefit, notably speed to market, access to advanced capabilities, and innovation, today pales in contrast to those that businesses could realize in year one. The economics of year one, made possible by an effective financial operations (FinOps) deployment, typically represent a 15–25% gain over the advantages of day one. For example, more effort must be spent on foundation development, automation, and app remediation if these economic gains are to be realized.

Companies can create a business case that focuses on the true value of the cloud and develop a migration strategy to capture it if they have a clear understanding of year-one economics.

Also Read : How To Make Your Cloud Transformation Recession-Proof

Using incremental cost operating expense economics rather than “average cost” capital expenditure economics

Traditional IT operates under a capital-expenditure model, in which businesses engage in episodic, long-range demand-planning exercises, followed by capital outlays and ongoing depreciation. The marginal cost of utilizing more infrastructure capacity is low in this model, data-center capacity is built out years in advance, and businesses gauge their cost effectiveness by examining their average costs and infrastructure utilization levels.

Cloud service providers (CSPs) have shifted the paradigm to an operating-expenditure model, where businesses pay for what they use, by making it easy to dynamically add nearly limitless capacity. The ability to accurately assess capacity demand—and the accompanying incremental or marginal costs—at any given time is now crucial for the most efficient cloud economics.

In essence, this is about not paying for capacity you don’t use, but rather paying for capacity only when you actually need it. Instead, businesses must create a dynamic operational expense strategy for cloud economics that continuously reduces incremental costs by selecting the cloud services that best meet their current workload needs.

One media organization, for instance, dynamically increases its computing capacity prior to significant consumer promotions to meet increased user traffic and decreases it after the campaign expires to prevent irrational cloud expenditure.

Only using historical data to forecast cloud spending

History becomes a much less accurate indicator of the future when businesses transition from the capital-expenditure world of traditional IT to the operating-expenditure world of cloud. This becomes a significant problem when businesses must calculate their cloud spending in order to create budgets or allocate funds to support new cloud-based products. Old habits are hard to overcome, and forecasting often still strongly relies on the capital-expenditure model. Despite this, businesses make allowances based on the operating-expenditure model that is currently dominant in the cloud. This frequently causes a greater than 20% difference between projected and actual spending, which leads to bad allocation choices and laborious rebudgeting.

Cloud forecasting and budget planning can be improved by more closely aligning them with business priorities. For instance, if a business is preparing a significant promotion for Black Friday, it is likely to experience a spike in customer interest. In a similar vein, plans to switch to a subscription pricing model will influence new consumer habits. These kinds of business decisions will affect the expenses of the cloud because they change depending on usage.

Organizations should develop unit economics for their key applications, such as compute cost per customer, in order to get forecasting right. In order to assist application owners in understanding the business drivers behind their cloud spending and the related impact of cloud spending on unit economics, this strategy necessitates a shift in perspective toward a consumption model and a competent FinOps capability.

Automatically extending the advantages of compute’s elasticity to other cloud services

The elasticity and scalability of cloud is economically ideal for workloads with variable cloud-consumption patterns. Based on statistical analysis, a video-streaming company was able to develop a unit-cost relationship between the price of cloud computing services and the corresponding business demand drivers (like compute cost per subscriber). As a result, the company was able to estimate cloud consumption with an accuracy of more than 95% and match its compute requirements to its business demand patterns. The business was able to more effectively allocate funds because of its capacity to precisely match demand with need.

Unfortunately, businesses frequently fail to distinguish between workloads that have an economic benefit from on-demand scaling and those that don’t, which drives up expenses. For instance, storage use climbed gradually as member numbers expanded at the same video-streaming business. Although the corporation was able to distance itself from the mechanics of setting up storage infrastructure thanks to the cloud, even though subscriber activity fluctuated, the constant expansion in subscriber data meant constant increases in storage expenses.

With this in mind, companies need to examine their workloads individually to assess whether their elasticity patterns would lead to savings on the cloud.

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Dissociating the cloud architecture road map from the cloud economics road map

Businesses frequently make optimistic assumptions about cloud utilisation when developing the cloud business case. In spite of the promise of dynamically scalable cloud capacity that can be adjusted to match application demand, most businesses actually experience lower cloud-resource utilization than they had planned for, which inflates predicted savings. While some businesses with cutting-edge cloud-native architecture experience resource utilization rates of more than 60%, the majority of businesses experience rates that are below 30%, and in some circumstances, even below 10%.

High utilization rates are at least partly dependent on an architecture capable of supporting them. The consumption of computing resources, for instance, can be considerably improved with autoscaling, but only if the application architecture is modernized. Unfortunately, the company’s cloud-economics and “architecture road maps” are often developed in relative isolation from each other, leading to business cases focused on utilization rates that cannot be supported. Companies must therefore closely tie the cloud business case to the evolution of their cloud architecture.

That doesn’t mean, however, that every workload should be migrated to the cloud. The recent cases of companies repatriating major workloads, especially storage services, from cloud to their own custom-designed on-premises infrastructure are a case in point. The scale and homogeneity of these workloads may create on-premises economics that are equivalent to or better than those offered by cloud providers. For this reason, companies that have an environment with a small number of massively scaled workloads need to be selective about adopting cloud.

In addition, workloads that are core to the competitive advantage of the company warrant the investment and focus required to make them best of breed. This is especially true when the company’s workloads compete with products offered by CSPs, such as storage as a service.

Migrating all workloads, regardless of size or type, to the cloud

The economies of scale have made it possible for hyperscalers to provide better returns than many organizations could achieve on-premises, whether in the form of cost savings or improved business outcomes.

However, this does not imply that all workloads ought to be migrated to the cloud. A case in point is the recent instances of businesses relocating important workloads, particularly storage services, from the cloud to their own custom-designed on-premises infrastructure. These workloads’ size and uniformity may produce on-premises economics that are on par with or superior to those provided by cloud providers. Due to this, businesses that operate in environments with a limited number of highly scalable workloads must be picky in their cloud adoption.

Additionally, workloads that are core to the competitive advantage of the company warrant the investment and focus required to make them best of breed. This is particularly true when the company’s workloads compete with CSPs’ products, like storage as a service, that they offer.

Call for action: Building up a true cloud FinOps capability

The cloud is a rapidly evolving space that demands close attention to shifts in financial modeling. Businesses need a strong FinOps capability to make wise business decisions and regularly manage consumption based on a fundamental understanding of cloud economics if they are to realise the promised benefit. Companies with cloud aspirations will be distinguished from those that have discovered cloud value by their capacity to manage cloud economics efficiently as cloud use grows rapidly and becomes increasingly more central to the business.

And this is where PeoplActive can help you. We will develop a dedicated FinOps team after understanding your existing cloud architecture, whose mission will be to make sound business decisions and manage cloud economics on an ongoing basis.

This team would be responsible for pulling together real-world business cases; recalibrating financial models based on evolving needs; updating models as new services and pricing structures are introduced; and focusing investments on areas of cloud’s greatest value potential. Bringing together technical, finance, and sourcing talent, the cloud FinOps team ensures the effectiveness of cloud consumption and business decisions on a continuous basis. 

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