Multi-Cloud Kubernetes Autoscaling

Overview

Architected and deployed Kubernetes Cluster Auto-scaling with Cluster API across a multi-cloud platform (AWS, GCP, On-Prem). This initiative achieved a 62% reduction in infrastructure costs, translating to $100K+ annual savings.

Key Achievements

• 💰 62% cost reduction — $100K+ annual savings
• ☁️ Multi-cloud — Unified scaling across AWS, GCP, and on-premises
• ⚡ Intelligent scaling — Dynamic resource allocation based on workload demand

Technical Details

Cluster API Integration

Leveraged Cluster API (CAPI) to provide a consistent, declarative approach to cluster lifecycle management across providers:

• AWS: EKS with managed node groups and Karpenter
• GCP: GKE with node auto-provisioning
• On-Premises: Custom provider integration with bare-metal

Scaling Strategy

# Example: Workload-aware scaling policy
apiVersion: autoscaling.cluster.x-k8s.io/v1beta1
kind: MachinePool
spec:
  minSize: 1
  maxSize: 100
  scaleDownDelay: 10m
  scaleUpDelay: 0s

Cost Optimization Techniques

1. Right-sizing — Automatic node size selection based on workload requirements
2. Spot/Preemptible instances — Cost-effective nodes for fault-tolerant workloads
3. Bin-packing — Efficient pod scheduling to maximize node utilization
4. Scale-to-zero — Development environments scale down during off-hours

Technologies

Kubernetes Cluster API AWS EKS GCP GKE Karpenter Terraform GitOps