Optimizing Kubernetes Infrastructure for Cost-Efficient Scaling With Karpenter
A US-based automotive enterprise migrated from Cast AI to Karpenter, achieving feature parity while reducing infrastructure costs and vendor dependency.
At a Glance
The client needed to reduce Kubernetes infrastructure costs while maintaining autoscaling, operational visibility, and infrastructure control.
Industry
Automotive
Function
Cloud Infrastructure & Platform Engineering
Solution
Karpenter-Native Autoscaling
Engagement
Infrastructure Migration
The Client
The client is a US-based automotive enterprise operating a high-traffic digital platform for used-car sales. The platform connects sellers with buyers nationwide and manages the complete transaction lifecycle, from vehicle listing and pricing to financing and final sale.
The Challenges
Reducing Kubernetes Costs Without Losing Autoscaling Capabilities
Cast AI provided dynamic node provisioning, right-sizing, and scale-down capabilities, but its commercial model introduced cost, control, and vendor dependency concerns as the infrastructure grew.
Recurring Licensing Costs
Cast AI's commercial subscription added recurring licensing expenses that increased alongside infrastructure usage.
Vendor Lock-In
Pricing, product decisions, feature changes, and roadmap direction remained dependent on a proprietary commercial platform.
Opaque Optimization Logic
The team had limited visibility into Cast AI's optimization decisions, making it difficult to inspect, audit, or customize provisioning behavior.
Single-Vendor Dependency
Core Kubernetes node provisioning depended on a third-party platform, creating additional operational dependency.
Migration Risk
The team needed to move away from Cast AI without losing autoscaling capabilities or affecting infrastructure performance.
Infrastructure Visibility
The new setup needed clear visibility into provisioning behavior, node lifecycle events, and consolidation activity after migration.
Take Greater Control of Your Kubernetes Infrastructure
Move toward Kubernetes-native infrastructure that reduces recurring platform costs, improves flexibility, and gives engineering teams greater control over scaling decisions.
The Solution
Migrating From Cast AI to Karpenter
Maruti Techlabs migrated the client's Kubernetes autoscaling setup from Cast AI to Karpenter, creating a community-backed and engineering-controlled infrastructure model.
Documented active policies, instance preferences, and scaling behaviors to create a detailed migration checklist.
Documented active policies, instance preferences, and scaling behaviors to create a detailed migration checklist.
The Journey
Commercial to Kubernetes-Native
Business Outcomes
Lower Infrastructure Costs, Greater Kubernetes Control
40% Cost Reduction
Annual infrastructure costs dropped from about $20,000 to $12,000 by removing platform licensing costs.
99.9% Feature Parity
Karpenter matched the required autoscaling capabilities, including right-sizing, scale-down, and spot instance support.
$8K Annual Savings
Removing Cast AI platform costs reduced total annual infrastructure spending by approximately $8,000.
Open-Source Foundation
Karpenter provided a Kubernetes-native, community-backed autoscaling approach with CNCF backing.
Reduced Vendor Dependency
The client no longer depended on a proprietary commercial platform for core infrastructure provisioning.
Better Infrastructure Visibility
SigNoz dashboards provided visibility into node lifecycle events, provisioning latency, consolidation activity, and Karpenter metrics.
Conclusion
The migration reduced infrastructure costs while preserving autoscaling capabilities and giving the engineering team greater control over its Kubernetes environment.
Tech Stack
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Frequently Asked Questions
Cast AI provided the required autoscaling capabilities, but its recurring licensing costs, proprietary model, and vendor dependency became concerns as the client's Kubernetes infrastructure scaled. Karpenter offered a Kubernetes-native alternative without the same commercial licensing overhead.
Yes. The migration maintained feature parity for the capabilities the client relied on, including right-sizing, scale-down behavior, and spot instance support. The client validated these capabilities during the non-production migration before the production cutover.
The previous setup included both Cast AI platform costs and underlying EC2 infrastructure costs. After moving to Karpenter, the client eliminated Cast AI licensing costs and reduced annual infrastructure costs from about $20,000 to about $12,000.
The team first audited the existing configuration and validated Karpenter in development and staging environments. The team configured Pod Disruption Budgets for critical services, then introduced Karpenter progressively in production before fully decommissioning Cast AI.
Amazon EKS provided the Kubernetes environment in which Karpenter was deployed. Karpenter managed node provisioning through NodePools configured around the required instance types, availability zones, and resource constraints.
SigNoz was updated with Karpenter-native metrics and infrastructure events, including node lifecycle activity, provisioning latency, and consolidation behavior. This gave the engineering team real-time visibility into how the new autoscaling setup was operating.