Optimizing Costs in Azure Kubernetes Service: Analyzing Resource Utilization Trends

Observations on AKS and Kubernetes Cost Management

The growing adoption of Azure Kubernetes Service (AKS) for enterprise workloads underscores a troubling trend: costs are escalating while resource utilization remains alarmingly low. Companies are increasingly investing in powerful compute solutions and GPU infrastructures to support AI initiatives, yet they’re not optimizing the underlying Kubernetes environments effectively. A glance at the findings from the **2026 State of Kubernetes Optimization Report** reveals some stark realities that shouldn't be overlooked. Average CPU utilization across analyzed clusters plummeted from 10% to 8% in 2025, and memory usage dropped from 23% to 20%. In terms of overprovisioning, the situation has worsened drastically—CPU overprovisioning surged to 69%, while memory overprovisioning hit a staggering 79%. GPU utilization is even more telling: the average sits at a mere 5%, and on AKS, this drops to an alarming 2%. This statistic translates into a considerable amount of wasted resources—essentially, for every dollar spent on infrastructure, 98 cents are going to waste. The report relied on direct metrics from thousands of Kubernetes clusters across prominent cloud platforms like AWS, Azure, and GCP, offered a clear picture of operational inefficiency before firms implemented automation solutions from Cast AI.

Why Utilization Is Deteriorating

So, what’s driving these disappointing metrics? One clear area of concern is the deployment of GPUs. AI workloads can exhibit highly variable resource demands; during peak times, significant capacity is required, but that need can drop sharply during quieter periods. Unfortunately, conventional deployment practices often tie workloads to dedicated hardware, leading to significant underutilization. Although methods like GPU sharing and improved scheduling could alleviate this issue, they aren't widespread enough to make a difference. The pattern isn’t just limited to GPU usage; it reflects across CPU and memory as well. Resource requests are typically conservative because the cost of under-provisioning—resulting in throttling or failed deployments—is well understood. Consequently, organizations often overestimate their needs, adopting settings that remain static long after workload requirements have evolved. This overprovisioning not only indicates wasted CPU or memory; it highlights a systemic issue with inflated resource requirements that are perpetuated throughout an organization. Once these assumptions settle in, they can be resistant to change, complicating adjustments that may need to occur as workloads and organizational needs shift.

An Enterprise-Wide Challenge

The low utilization rates reported aren't confined to AKS; they reflect a broader systemic problem impacting enterprise Kubernetes, irrespective of cloud provider. Organizations grapple with static resources while being unable to accurately predict changing workloads. Systems established for earlier traffic patterns or applications often linger long after their relevance expires. As a result, enterprises find themselves caught in a cycle where evolving workloads clash against outdated infrastructure decisions. Notably, not all issues arise from the choice of cloud provider. The findings suggest that the inability to keep pace with infrastructure versatility spans GKE and EKS as well, pointing to a pressing need for organizations to reassess the management and provisioning of their Kubernetes environments. As workloads become increasingly dynamic, optimizing Kubernetes deployment will be crucial.

Tools Alone Won’t Solve the Problem

Despite the wealth of tools available for optimization—autoscalers, resource adjustment mechanisms, and new infrastructure types—these technologies don't operate in isolation. A change to one part of the ecosystem invariably influences others. Modify a workload's resource request and you'll impact node fitting; alter the composition of node types, and suddenly certain workloads may no longer be viable options on cheaper Spot instances. This interconnectedness often stymies long-term optimization efforts. Teams might succeed in optimizing when first rolling out changes, but as environments evolve, those hard-fought gains can erode. Before long, a well-tuned cluster may revert to earlier inefficiencies, unable to keep pace with the shifts in workload demands.

Practical Steps for AKS Teams

The silver lining is that addressing these inefficiencies doesn’t necessitate a complete overhaul of AKS or applications. Instead, it requires a strategic shift in how efficiency is calculated and maintained. AKS teams should start by scrutinizing actual resource consumption rather than just relying on historical resource requests set in deployment manifests. Establishing a more accurate baseline for workloads, revisiting resource requests, and ensuring infrastructure aligns with real-time demands will help streamline costs. Identifying workloads that could benefit from on-demand and Spot capacity will also ensure that resources are used judiciously. Understanding GPU usage is a fundamental consideration too. If GPUs remain mostly idle, simply adding more hardware won’t resolve the issue; instead, organizations should explore shared resources or refined scheduling strategies. Finally, cloud commitments must be recalibrated based on the optimized configuration, ensuring companies aren’t committing to capacities beyond what they truly need. Aligning all these considerations together can lead to a sustained reduction in waste—ultimately turning Kubernetes from a potential pitfall into an effective operational asset. As we prepare to extend the conversation, the questions about underutilization aren’t exclusive to AKS; we'll next examine GKE and EKS in light of the same optimization report findings, paving the way for a deeper understanding of resource dynamics across the Kubernetes ecosystem.