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The Opportunity Cost of Not Using Autonomous Workload Optimization

Pierre Perrot

Sales Development Representative, Intel Granulate

In today’s business world, leaders are looking for every possible advantage to gain a competitive edge. Deploying autonomous workload optimization improves performance and significantly reduces costs, delivering the advantage organizations need.

Companies that deploy autonomous and continuous workload optimization see significant gains in productivity and efficiency while reducing costs. Conversely, not deploying workload optimization solutions can also lead to significant lost opportunity costs that might otherwise go unnoticed.

Lost Opportunity Costs from Inefficient Workload Optimization

If you’re hitting your budget and production is meeting timelines, congratulations. You are doing better than the third of companies reporting cloud budget overruns as high as 40%. However, even if you’re hitting your numbers, we have to ask this question: Do you know whether you can improve productivity or cost savings?

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Organizations today have to go beyond meeting their budget and output goals to find ways to improve performance, increase output, and reduce costs at the same time. Failing to do so results in lost opportunity costs, including:

Increasing Expenses

Perhaps the biggest opportunity cost is the actual cost. When compute resources are not optimized, the costs can quickly scale out of control. Nearly half of all businesses say they have difficulty managing their cloud costs.

As more digital transformation and cloud migration occurs, computing costs have grown significantly. Big data, artificial intelligence (AI), machine learning (ML), and data analysis require more computing power. As organizations try to meet demand, overprovisioning and inefficient use of resources are becoming more common. Yet, overprovisioning and idle resources produce more than $17.6 billion in waste and often go totally unnoticed.

Lack of Cost Control

The lost opportunity is better cost control. IDC research revealed that reducing overprovisioning can lower operational costs by nearly 40% over a three-year period. Right-sizing your capacity with the ability to scale on demand helps reduce:

  • Capital expenditures
  • Maintenance and management
  • Energy consumption
  • Licensing, software, and cloud costs

Overprovisioning ties up capital that could be used elsewhere.

As budgets balloon, CIOs and CTOs are being scrutinized more closely by CFOs and are expected to meet cost KPIs. Optimizing your IT environment and computing workload delivers the cost savings you need.

More Performance Bottlenecks

As engineers write more code, spin up new instances, and consume more resources, there’s a surprising amount of waste. Though unintentional, idle, unused, or underused resources waste money but can also cause system and software bloat, degrading performance.

For example, inefficient distribution of workloads can create bottlenecks, impacting application response times, user experience, and productivity. Management of burgeoning resources takes more time and can be especially challenging because of the sheer number of potential dependencies.

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Reduced Uptime

In every company and industry, downtime is costly. When you’re under pressure to deliver code, for example, the DevOps time is on hold. It creates gaps in the continuous integration/continuous delivery (CI/CD) pipeline that makes DevOps so effective.

Optimized workloads reduce downtime and wasted resources.

Inefficient Resource Utilization

What you don’t know can be costly. Directors of Cloud know that optimizing resource utilization can make everything run faster and more smoothly. If your current environment is inefficient, however, you could be losing millions of dollars in compute costs and never even know.

You may also be tying up resources with one project that could be freed up for another, increasing productivity and output.

Reduce Flexibility and Scalability

Autonomous workload optimization enables organizations to scale rapidly and on demand. By deploying more efficient resource optimization, you can use the capacity you already have to its optimal level and scale as needed to respond to changing requirements.

When you have to manage resource allocation manually, it’s easy to miss opportunities to move quickly as opportunities arise or market conditions change.

More Manual Work, Less Innovation

Autonomous workload automation reduces low-level, manual tasks that must be done to manage an optimized environment. It avoids tying up IT managers, teams, and engineers on repetitive tasks that can be handled by automation.

When team members are dealing with manual tasks, they are unable to focus their full attention on strategic initiatives and innovation.

Improve Application Performance and Reduce Costs

These lost opportunity costs are abundant and often go unnoticed. They may not jump out at business leaders as they analyze budgets or infrastructure, but they’re there. They may not cause you to lose your jobs, but realizing the benefits of autonomous workload optimization might just make you the hero.

Adopting autonomous optimization will effectively reduce expenses, improve cost control, mitigate performance bottlenecks, ensure stability, make resource utilization more efficient, enhance scalability and, most importantly, make you the FinOps darling of your organization. 

Optimize cloud with Granulate

Granulate improves application performance and reduces cost without any code changes. Autonomous workload optimization creates more efficient use of resources and compute time, reduces response time, and increases throughput.

Contact Granulate today to request a demo and see how workload optimization can improve your operations, save you money, and eliminate opportunity costs.

Optimize application performance.

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