Since the advent of mainframes in the 1960s, IBM pricing models strategies have taken on have evolved to address a variety of customer needs.

It took nearly 40 years of trial and error for pricing models to arrive at a sub-capacity model where users only pay for what they use, and then with the introduction of the rolling-4-hour-average, users finally received a dynamic pricing solution that aligned expenses with mainframe workload. With the introduction of R4HA, users paid according to peak LPAR activity, and this was a major step toward making mainframe pricing more adaptable and affordable for IT teams.

But, there is always room for improvement.

This year, IBM is motivating users to adapt a new pricing model. Tailored Fit Pricing is a consumption-based model that helps streamline mainframe billing by assigning an exact dollar amount to every MSU the mainframe uses. Tailored Fit Pricing eliminates dependency on rolling average utilization windows, which helps reduce the need to cap or restrict system resources. When implemented properly, this pricing model helps increase IT agility at a lower comparative price point.

IBM plans to move 50 percent of customers to Tailored Fit Pricing within the next five years starting with its largest customers. It’s coming, and sooner or later this new pricing model will affect your mainframe budget. So, let’s get clear on what, exactly, Tailored Fit Pricing is and how this shift will change the way mainframes are managed.

What is Tailored Fit Pricing?

IBM introduced Tailored Fit Pricing to make the Z platform more attractive for implementing new applications and make it possible to test new types of, potentially volatile, workloads without drastically affecting consumption. Previous pricing models, which relied solely on activity peaks, were susceptible to huge variance in costs due to unpredictable workloads. It has become clear that this issue unfairly bars many z/OS environments from realizing growth potential.

“While once batch activity prevailed, today’s workloads tend to be primarily transaction oriented,” according to an IBM press release. “Real time analytics on data to gain insights, smartphone driven transactions and other market factors are driving workload volatility. This volatility impacts workload costs and has prompted the need for a pricing solution that aligns costs with actual resource usage.”

Put simply: mainframe activity has evolved and so must pricing models. Tailored Fit Pricing is designed to overcome unpredictable expenses associated with the pricing models that put far too much emphasis on activity peaks.

Tailored Fit Pricing is made up of two different models that cater to customer requirements:

  • Enterprise Consumption Solution: A cloud-like solution in which billing is measured on a per-MSU-consumed basis. With this, users can take full advantage of hardware without fear of expensive usage spikes. This solution can help level out expenses associated with seasonal variations. Users commit to an annual consumption, and there is a true-up at the end of the year is the customer exceeded their allotted amount. And, if the customer consumed less than their agreed upon amount, the leftover rolls over to be used for the following year.
  • Enterprise Capacity Solution: A full-capacity license that offers improved cost prediction capability.

Under the Enterprise Consumption Model, IBM assigns a price per MSU per hour and charges solely based on machine consumption. With this pricing model, the cost per MSU is fixed, so the only variable factor is consumption.

How to reduce costs of Tailored Fit Pricing

This new pricing option complicates traditional mainframe cost savings measures since mainframes will no longer be billed according to peak values. Previously, reducing the cost of your environment came down to reducing the R4HA, but today things are different.

The best way to optimize your IT budget in the age of Tailored Fit Pricing is to learn where your money is actually going. You can’t optimize your budget if you don’t even know where your money is going. It’s as simple as that. Under Tailored Fit Pricing, budget and financial adjustments will be made on a per-MSU basis, so it’s more important than ever to know what is occurring on your system, and, more importantly, what value that activity provides.

What does this mean for mainframe managers? With a greater focus being put on MSU consumption, IT teams will need a way to collect, monitor, and analyze mainframe logs with greater efficiency.

Only one z/OS software offers all the logs analytics you need on one platform : ZETALY.

Do more with less with ZETALY Cost Control

Now, more than ever, proactive consumption planning is necessary to maximize your IT budget.
Under Tailored Fit Pricing, every single MSU counts. Sure, you can get consumption insight by analyzing SCRT, but these reports are fleeting and offer no day-to-day consumption insight.
The main goal of ZETALY is to add value to mainframe logs. Whether you’re building reports to make sense of mainframe logs or managing MSU consumption to control IT costs, ZETALY is the all-in-one operational data analytics platform.

This turn-key mainframe analytics platform offers a variety of ways to observe and manage MSU consumption:

  • Analyze consumption: Use ZETALY analytics capabilities to gain in-depth, real-time consumption info that directly reflects your working environment. Through customizable, adaptable dashboards, users can visualize MSU relationships that would normally go unnoticed. With this unprecedented insight, you can make strategic adjustments to help maximize mainframe efficiency and affordability.
  • Analyze cost: ZETALY illustrates expenses down to the departmental level, so you can align business needs with IT costs. No more abstract spreadsheets — ZETALY gathers information about MSU consumption and makes it clear which business needs are being served by mainframe activity. This information is displayed in easy-to-understand reports so everyone can understand and, more importantly, be held accountable for the mainframe budget.
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