Kubernetes Cost Optimization with Karpenter & Spot Instances

In today’s cloud-first enterprise environment, organizations are adopting Kubernetes to power modern applications at scale. While Kubernetes brings agility, running clusters on cloud infrastructure often results in rapidly increasing compute costs. Striking the balance between performance, scalability, and cost efficiency is a common challenge.

In this blog, we highlight a real-world customer success story where we implemented Karpenter with Spot Instances on AWS. This approach significantly reduced cloud compute costs while ensuring resilient, production-grade workloads.

Customer Problem Statement

Our customer was operating multiple Kubernetes clusters to support transaction-heavy applications. With business growth, compute demand increased steadily — but so did costs. Over six months, their EC2 spend had risen by nearly 40%, raising concerns at the executive level.

Key challenges identified:

• High On-Demand Usage: Majority of workloads were running on On-Demand instances with no optimization for variable workloads.
• Slow Autoscaling: The default Cluster Autoscaler often lagged by several minutes, leading to performance bottlenecks during traffic spikes.
• Limited Spot Adoption: While Spot Instances were considered, lack of proper interruption handling made them unsuitable for production.
• Cost Governance Gaps: No clear visibility into node utilization and scaling efficiency.

Solution Implemented

Our team conducted a Kubernetes Cost Optimization Assessment, focusing on the compute layer where the bulk of costs were incurred. The following initiatives were implemented using Karpenter and AWS best practices:

1. Right-Sizing & Dynamic Scaling

• Deployed Karpenter, replacing the Cluster Autoscaler to achieve near real-time node provisioning.
• Configured Provisioners to right-size nodes automatically based on pod requests (vCPU, memory, GPU).
• Introduced Consolidation policies to continuously replace underutilized nodes with more cost-effective alternatives.

2. Hybrid Spot + On-Demand Strategy

• Designed a hybrid provisioning model:
• Critical, latency-sensitive services → On-Demand nodes.
• Batch, ML, and scalable workloads → Spot Instances.
• Implemented diversification policies across multiple instance families (M, C, R series) and Availability Zones to mitigate Spot interruption risk.
• Integrated AWS Node Termination Handler to gracefully drain and reschedule workloads during Spot interruptions.

3. Governance & Monitoring

• Enabled Karpenter metrics via CloudWatch and Prometheus for cluster-level visibility.
• Established PodDisruptionBudgets (PDBs) and Topology Spread Constraints to maintain resilience during scaling events.
• Collaborated with FinOps teams to set cost anomaly detection and resource tagging for accountability.

Business Value Achieved

Within three months of adopting Karpenter with Spot Instances, the customer achieved measurable business value:

• 35% reduction in monthly EC2 spend, with no degradation in application performance.
• 50% Spot Instance utilization across clusters, up from less than 5% previously.
• Scaling time reduced from ~5 minutes to <30 seconds, ensuring seamless user experience during traffic surges.
• Improved resiliency, with zero customer-facing downtime despite Spot interruptions.
• Enhanced visibility and governance, empowering DevOps and Finance teams to collaborate on ongoing optimization.