Kubex releases data from a survey of over 500 U.S. software developers, revealing a disconnect between cost sensitivity, scrutiny and resource efficiency.
An optimization gap that could be costing millions
Over 90% of corporate leadership has intensified scrutiny on cloud spending, with 30% making it a board-level or C-suite priority. However, 89% of organizations are wasting 10% or more of their total cloud infrastructure budget on idle capacity, oversized instances and zombie resources. This gap represents millions of dollars spent on infrastructure that delivers zero business value.
For Kubernetes-specific spending, the pressure is equally intense. Nearly 90% of organizations report increased executive oversight of Kubernetes (K8s) costs, yet one in four organizations wastes more than 30% of their infrastructure budget.
What this tells us: Cost optimization has become a key directive at the board level. Treating it as optional signals inefficiency to executives, who now see cloud waste as having a direct impact on profitability. This trend is expected to intensify as AI workloads drive GPU costs to unprecedented levels.
The automation advantage hiding in plain sight
Nearly half (47%) still conduct periodic weekly, bi-weekly or monthly manual reviews and corrections, an approach that is misaligned with the scale and complexity of modern cloud environments.
The ROI demonstrates the benefits of automation. Organizations that invest in optimization see measurable results: 44% reported cost reductions of 10-20% over the past year, 26% achieved savings of 20-30%, and 9% exceeded 30% in savings. Only 3% experienced minimal or no results.
What this tells us: The traditional “monthly cost review” model is inadequate for dynamic cloud environments, where resources scale continuously. Organizations require automated, policy-driven approaches that optimize in real-time, rather than after waste has accumulated. Manual optimization often results in financial losses.
The AI infrastructure reality check
AI workloads are now mainstream, with 84% of respondents actively managing the infrastructure that supports them. But organizations are flying blind financially. While 43% are transitioning to cost controls and governance, 28% still maintain unlimited AI spending, prioritizing innovation over cost.
Over half of the respondents (55%) face difficulties with security and access control, while a similar percentage deal with issues regarding the quality of their data infrastructure. Additionally, 50% cite scalability and overall infrastructure concerns, and 44% struggle with resource utilization and cost control.
Looking ahead to 2026, the shift from training to inference workloads will significantly restructure the landscape. Companies have deployed hundreds of GPUs to launch AI services and are now discovering that some show virtually zero utilization. The question will shift from “do we have enough GPUs” to “are we using them properly” — and whether every AI workload even needs a GPU at all.
What this tells us: AI infrastructure has become too expensive to manage reactively. GPU costs now represent such a significant portion of cloud spend that optimization is no longer optional. Organizations that fail to implement cost-per-token tracking and select fit-for-purpose infrastructure burn through budgets at a high rate.
‘Houston, we have a visibility problem.’
While 95% of teams claim to be confident in their monthly cloud spend forecasts, the data tells a different story. If visibility is truly that strong, why are 89% still losing double-digit percentages of their budgets to inefficiency?
The answer lies in what’s being measured. Organizations track spending accurately but lack visibility into the productive versus unproductive use of resources. They can forecast costs but can’t identify which containers are oversized, which instances sit idle or which workloads could run on cheaper infrastructure.
The next 12 months will center on closing this visibility gap. Sixty-five percent of organizations are prioritizing better observability and cost visibility for K8s resources, while 53% are focusing on automating workload optimization and rightsizing.
What this tells us: Spending visibility and resource optimization visibility are not the same thing. Organizations that confuse the two may stay within budget while still losing money. ‘True’ visibility involves understanding usage patterns, identifying waste in real-time, and automatically acting on optimization opportunities before they lead to significant budget impacts
Multi-cluster complexities
Most organizations (65%) now operate hybrid infrastructure models, scaling K8 across both on-premise and in the cloud. This reality introduces complexity in resource management, cost allocation and optimization.
Teams are prioritizing solutions: 51% cite managing multi-cluster or hybrid K8s complexity as a key focus area for the next year, while 50% work to migrate to more cost-effective distributions or platforms. The technical debt of disparate environments collides with intensifying cost pressure.
The top operational challenges reflect this complexity: 63% struggle with security, policy enforcement, and compliance across environments, while 56% have storage and data consistency issues. More than half (54%) cite networking and service mesh complexity as ongoing concerns.
What this tells us: Infrastructure fragmentation multiplies waste. Invisible resources can’t be optimized. Organizations need unified visibility and control across all environments, not point solutions for individual clusters.
The opportunity ahead
With 89% of organizations wasting large portions of their cloud budgets and 79% under heightened executive scrutiny, rethinking infrastructure management has never been more urgent. The survey highlights the actions organizations should take: Automate continuous optimization, enable real-time visibility into costs and utilization, align infrastructure choices with AI workload demands, and unify management across hybrid environments.
Optimization is now a board-level business imperative. In a world where every GPU dollar is measured and cloud costs are under constant review, the organizations that bridge the gap between cost confidence and efficiency will capture millions in savings, while others continue to lose ground to idle infrastructure.
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This research is based on a survey of over 500 U.S. software developers who have managed modern cloud infrastructure, conducted by Centiment on behalf of Kubex in October 2025. Those surveyed currently manage their employer’s K8s infrastructure (82%), previously managed their employer’s K8s infrastructure (10%) or have worked with K8s but have not been responsible for managing the infrastructure (8%).
