Enhancing Operational Excellence and Cost Efficiency on Amazon EKS
The Kubernetes platform was initially designed using Cluster Autoscaler as the primary node scaling mechanism for Amazon EKS clusters. This approach relied on multiple Auto Scaling Groups to support different workload profiles and availability requirements.
As the platform grew, the operational complexity of managing numerous node groups increased significantly. Ensuring high availability for critical services, performing timely cluster and node upgrades, and maintaining a strong security posture became increasingly challenging. In parallel, infrastructure costs continued to rise due to over-provisioning and inefficient resource utilization.
The limitations of Cluster Autoscaler became more apparent as workload diversity increased and scaling requirements became more dynamic. A more modern and flexible autoscaling solution was required to support the platform’s continued growth.
At a Glance
- Industry Automotive, Digital Marketplace
- Engagement Type Cloud Infrastructure & Modernization
Client
The client’s platform plays a central role in enabling transactions between buyers and sellers, meaning that any performance degradation, downtime, or instability directly impacts user confidence and commercial outcomes. As the platform scaled, the client required improvements ininfrastructure reliability, operational visibility, and the ability to handle unpredictable demand while maintaining a consistent user experience.
Challenge
The key challenges identified included:
- Increasing operational overhead caused by managing multiple Auto Scaling Groups
- Inefficient resource utilization leading to higher infrastructure costs
- Slow node provisioning impacting workload startup latency
- Limited flexibility in instance selection and sizing
- Difficulty maintaining timely node upgrades and security patches
- These challenges directly affected platform efficiency, cost control, and the ability to scale reliably.
Our Solution
To address these issues, the client migrated from Cluster Autoscaler to Karpenter, adopting a Kubernetes-native autoscaling approach tightly integrated with AWS APIs.
Karpenter enables dynamic node provisioning based on real-time pod requirements, eliminating the need for predefined node groups. Nodes are launched just in time, sized precisely for pending workloads, and automatically terminated when no longer needed.
The migration simplified cluster architecture, reduced operational complexity, and enabled more intelligent scaling decisions driven directly by workload demand.
Results
The migration from Cluster Autoscaler to Karpenter represented a pivotal step in modernizing workload scaling on Amazon EKS. The platform gained the ability to respond more rapidly to changes in demand, improve overall resource utilization, and significantly reduce operational overhead.
The initiative delivered measurable improvements in cost efficiency, performance, and operational scalability, while establishing a flexible and future-ready foundation capable of supporting continued platform growth and innovation.
- Faster workload startup through rapid node provisioning
- Reduced over-provisioning by matching instance types to real-time pod resource requirements
- Improved resiliency through proactive handling of spot instance interruptions
- Fewer node configurations and simplified autoscaling logic
- Increased platform flexibility, including support for ARM-based workloads and GPU-enabled instances
- Dynamic node sizing to efficiently support large containers and compute-intensive workloads, including AI-related use cases
About allOps Solutions
allOps was engaged as a strategic cloud and DevOps partner to assess the existing Kubernetes scaling approach and design a more efficient, future-proof solution aligned with cloud-native best practices.
The allOps team worked closely with the client’s engineering teams to analyze workload characteristics, scaling behavior, cost drivers, and operational constraints. Based on this assessment, allOps proposed and led the migration from Cluster Autoscaler to Karpenter, focusing on improving scalability, reducing costs, and simplifying cluster operations without disrupting production workloads.
allOps was responsible for the solution design, migration planning, implementation, and validation, ensuring the new autoscaling model met performance, reliability, and security requirements.