Cloud Autoscaling & Rightsizing: 35% Cost Reduction
Intelligent resource scaling with Karpenter and KEDA while maintaining performance SLAs
35%
Cost Reduction
99.9%
SLA Maintained
60%
Less Over-Provisioning
Quick Facts
Industry: E-commerce SaaS
Cloud: AWS EKS
Timeline: 6 weeks
Monthly Savings: $45,000
Tech Stack: Karpenter, KEDA, Prometheus
The Challenge
A fast-growing e-commerce SaaS platform was spending $130,000/month on AWS compute resources. Despite using Cluster Autoscaler, they were experiencing both cost overruns and occasional performance issues during traffic spikes.
Their infrastructure team was manually managing instance types, leading to significant over-provisioning during off-peak hours and under-provisioning during flash sales events.
Pain Points
❌ Cluster Autoscaler too slow (5-10 min provisioning)
❌ Manual instance type selection
❌ Over-provisioned 60% of the time
❌ Under-provisioned during traffic spikes
❌ No scale-to-zero for batch workloads
Our Solution
🚀
Karpenter Implementation
Replaced Cluster Autoscaler with Karpenter for intelligent node provisioning. Automatic instance type selection based on pod requirements, spot instance integration, and sub-minute scaling.
📊
KEDA Event-Driven Scaling
Implemented KEDA for workload-aware scaling based on queue depth, HTTP traffic, and custom Prometheus metrics. Enabled scale-to-zero for batch processing jobs.
🔍
Rightsizing Analysis
Deployed continuous rightsizing recommendations using Prometheus metrics and custom Grafana dashboards. Identified 40+ over-provisioned deployments.
💰
Spot Instance Strategy
Configured Karpenter provisioners to prefer spot instances for fault-tolerant workloads, with automatic fallback to on-demand for critical services.
Results
$45K
Monthly Savings
Reduced from $130K to $85K
35%
Cost Reduction
Immediate and sustained
<60s
Scale-Up Time
Down from 5-10 minutes
99.9%
SLA Maintained
Zero performance degradation
Frequently Asked Questions
What is Kubernetes autoscaling with Karpenter?
Karpenter is a Kubernetes node autoscaler that automatically provisions right-sized compute resources in response to pending pods. Unlike Cluster Autoscaler, it directly provisions nodes from cloud providers, selecting optimal instance types.
How does KEDA help with cost optimization?
KEDA scales workloads based on external metrics like queue depth or HTTP requests. It can scale deployments to zero when idle, significantly reducing costs for event-driven workloads.
What is rightsizing in cloud cost optimization?
Rightsizing matches instance types to workload requirements by analyzing CPU, memory, and network usage patterns to identify over-provisioned resources that can be downsized.
How long does implementation take?
A typical Karpenter + KEDA implementation takes 4-8 weeks, including assessment, implementation, and optimization phases.
Related Resources
Ready to Optimize Your Cloud Costs?
Get a free FinOps assessment and discover your savings potential.
Get Free Assessment
Subscribe to our newsletter
Get monthly email updates about improvements.