Organizations migrate to the cloud for high availability, efficient service delivery, and to reduce their operational costs. Often, however, the cost of doing business in the cloud surpasses budget expectations. More than half of businesses report going over budget for cloud resources and 84% say they expect spending to increase throughout 2023.

Top Cloud Management Mistakes That Cost Millions

The average cloud project exceeds budget by 13%. Nearly a third of cloud spending is wasted. Yet, much of this wasted spending can be eliminated by fixing these mistakes.

Mistake #1: Using the Wrong Regions

If you store your data in the wrong place, it’s costly. While the actual costs will vary depending on the type of resources and configuration, larger regions typically have lower costs due to economies of scale. For example, storing data in Stockholm, Sweden rather than Zurich, Switzerland can reduce costs by 14% when using AWS S3 standard pricing.

The cost of doing business in certain areas also impacts fees. Data storage costs in the US West (Northern California) region are 13% higher than in any other U.S. region.

Mistake #2: Not Managing Data Egress Costs

Data egress fees to pull data across regions or local use can add up fast. Apple spent $50 million in one year on data transfer fees. Netflix and Airbnb spent $15 million.  Adobe, Snap, and Salesforce racked up $7 million — just moving their own data around for analysis.

The region you choose and your proximity will make a big difference in managing data transfer fees, but it can be complicated by data localization and privacy regulations.

Mistake #3: Overprovisioning Resources

According to Forrester’s research, 94% of enterprises are overspending in the cloud — mainly from overprovisioning and underused resources.

Companies want to make sure they have enough resources available to handle workloads but often purchase more than they need. Right-sizing your resources and dynamically scaling your capacity can avoid overprovisioning. For example, Intel Tiber App-Level Optimization’s automatic capacity optimization can reduce Kubernetes costs by up to 45% through autonomous dynamic scaling.

Mistake #4: Unused, Idle, and Orphaned Resources

When you can identify and proactively manage your unused and idle resources, you can stop paying for instances and resources you aren’t using.

Many accounts run idle resources when they aren’t being used. Developers often fail to close resources when not in use. There are also orphaned resources, such as when a VM shuts down but the resources associated with the machine still exist.

Mistake #5: Cloud Migration Mistakes

With the focus on digital transformation, companies sometimes prioritize a lift-and-shift approach to move resources to the cloud quickly. However, this can also be inefficient. Often, the benefits gained from cloud deployment are only realized with a rewrite of on-premises applications to be cloud native. At the same time, lift and shift may not account for interdependencies, resulting in performance lags or higher costs.

Replatforming, refactoring, or building cloud-native applications can optimize infrastructure to avoid unnecessary costs.

Mistake #6: Manual Optimization

Today’s cloud environments are continuously evolving and manual optimization simply cannot keep up.

While performance engineers may manually conduct point-in-time performance vs. cost analysis, code is changed so frequently that the data is out of date quickly, especially when DevOps CI/CD processes are employed.

Real-time continuous optimization is the solution. For example, Intel Tiber App-Level Optimization not only lowers third-party costs but reduces cluster sizes and compute resources to deliver better overall performance.

Intel Tiber App-Level Optimization’s agents automatically learn your resource usage patterns and data flow to optimize resources and capacity.

Mistake #7: Lack of Visibility into Spending

Many enterprises simply have a lack of visibility into their company’s spending activity until it’s too late. Without the right tools to track and manage cloud spending, hidden costs can mount. There are so many things that can impact spending and billing is complex.

The Intel Tiber App-Level Optimization gCenter tracks, analyzes, and displays key performance and cost measures with resource mapping for real-time visibility. When Intel Tiber App-Level Optimization is deployed, you get holistic visibility and can monitor how much spending you’re saving in real-time. Want to see how much money you can save by leveraging Intel Tiber App-Level Optimization’s automated cost-saving solutions? Use this Workload Cost Savings Calculator to see the impact of deploying real-time, continuous optimization for your company.

Mistake #8: Not Optimizing Code

Inefficient code hotspots can eat up valuable memory and CPU resources, delivering sub-optimal performance and higher infrastructure costs. As code evolves, it often increases in efficiency over time due to bloat. Inefficient code can make excessive calls to cloud services, driving up costs, and increasing the cost of security and compliance.

Production profiling can help understand code-level performance to identify bottlenecks and optimization opportunities. Intel Tiber App-Level Optimization’s continuous profiling provides a system-wide view into cloud environments to gain complete visibility into every line of code, optimizing performance at a granular level by fixing, rolling back, or rewriting methods that impact performance. This reduces cluster size and improves CPU utilization.

Autonomous, Continuous Workload Optimization

Intel Tiber App-Level Optimization is an Intel Company focused on autonomous, continuous workload optimization to improve application performance and reduce cloud spending.

As engineering teams face increased pressure to lower costs, Intel Tiber App-Level Optimization provides an easy way to implement cost controls and reduce spending by up to 45% — automatically. Intel Tiber App-Level Optimization helps organizations overcome the common cloud management mistakes that are costing companies millions of dollars in wasted spend.