Use case
Cutting GKE Compute Costs by 65% Through Node Restructuring and Workload Optimization
- Vishalkumar Chaurasia
About the Customer
An iGaming platform provider empowers sweepstakes and online casino operators with a complete, modern, and scalable gaming infrastructure. Their platform delivers end-to-end capabilities including player onboarding, CRM, virtual-economy management, in-game mechanics, operational tools, and compliance support enabling operators to launch and scale quickly without relying on legacy systems. Built for flexibility and rapid growth, the company supports operators in delivering seamless and engaging player experiences across digital gaming environments.
Customer Challenge
As the company expanded its iGaming platform capabilities and onboarded more operators, the backend infrastructure supporting real-time workflows, data processing, and microservices grew significantly. Over time, multiple Kubernetes clusters powering these workloads became over-provisioned. Critical inefficiencies emerged: CPU utilization across node pools often remained below 10%, workloads were deployed on machine types mismatched to their memory-heavy profiles, and high minimum node counts forced more than 25 nodes to run continuously.
These inefficiencies led to steadily rising cloud compute costs. Many workloads requested 2–3× more resources than they consumed, autoscaler decisions were restricted due to suboptimal configurations, and lack of workload isolation caused unpredictable scaling behavior. Without intervention, the company risked increasing infrastructure spend month-over-month, reduced operational agility, and potential instability during peak gaming events or large campaign launches.
If left unaddressed, the cost growth and resource waste would impact long-term scalability, reduce infrastructure ROI, and limit the platform’s ability to maintain predictable performance for operators relying on company’s gaming platform.
Solution
IAMOPS delivered a structured and comprehensive optimization initiative aligned with GCP and Kubernetes cost governance best practices. The engagement began with a deep-dive assessment of all GKE clusters, analyzing CPU and memory usage patterns, autoscaling behavior, workload characteristics, and historical resource consumption metrics.
Based on this analysis, IAMOPS redesigned the node pool architecture using modern, cost-efficient machine families that aligned better with company’s memory-intensive services. Non-critical and development environments were migrated to preemptible compute options, enabling significant savings with no interruption to ongoing gameplay or operator services.
To improve operational efficiency, IAMOPS implemented workload isolation using taints, tolerations, and node affinity, ensuring predictable placement for key processes such as real-time ingestion, backend APIs, and operational jobs. Pod density was safely increased by adjusting cluster-level parameters, enabling each node to handle substantially more workloads without impacting performance. Application CPU and memory requests were systematically right-sized based on real-world usage, cutting out unnecessary reservations and allowing the autoscaler to operate more effectively.
Throughout the engagement, IAMOPS provided full MSP support, including pre-implementation planning, production-attentive rollout, monitoring, validation, and post-optimization governance. Weekly reviews ensured autoscaler correctness, cluster stability, and cost efficiency remained consistent. IAMOPS relied on GCP-native services such as GKE, Cloud Monitoring, and cost insights and applied methodologies derived from GCP’s Well-Architected Framework on cost optimization and reliability.
Results & Benefits
The optimization initiative dramatically improved both the performance and cost efficiency of company’s infrastructure. Baseline compute footprint was reduced by more than 70%, lowering the guaranteed node count from over 25 nodes to only 7 across clusters. CPU utilization improved from under 10% to more than 35% due to rightsizing and pod density improvements.
The company experienced a 65% reduction in total compute costs immediately after rollout. Development cluster costs dropped by nearly 80%, while production workloads saw a cost reduction of about 50%. In addition to savings, IAMOPS improved autoscaling responsiveness, reduced operational overhead, and created a more predictable and scalable foundation for company’s gaming operations.
Staging Account
Production Account
The improvements strengthened company’s ability to support rapid operator growth, handle traffic surges during gaming events, and maintain strong performance metrics across its platform ecosystem.
About IAMOPS
IAMOPS is a global DevOps and cloud operations provider specializing in scalable, high-performance infrastructure for SaaS, gaming, fintech, and enterprise platforms. With deep expertise in Kubernetes, cloud modernization, observability, and cost optimization, IAMOPS helps organizations build resilient, efficient, and production-ready cloud environments. IAMOPS also holds GCP partner specializations in DevOps, Cloud Operations, and Migration, ensuring delivery aligned with industry-leading cloud and platform engineering standards.
