Case Study

How an Emerging Media-Tech Company Cut Kubernetes Costs by 60% in 30 Days

A small production cluster — proof that the waste is real at every scale. No lock-in, no operational risk.

The Hidden Cost No One Talks About in Kubernetes

Everything looked stable. No crashes. No outages. No scaling incidents. But money was leaking silently. Like most fast-growing media-tech teams, workloads had been sized for theoretical peak demand. It felt safe. It felt responsible. It was costly.

The Real Problem: Overprovisioning Disguised as Stability

▲ CPU utilization: 3.3%
▲ 96.7% of allocated CPU sitting idle
▲ 55% of pods operating below 40% efficiency
▲ No granular namespace-level cost visibility
▲ No systematic rightsizing process

They were not overspending because of poor engineering. They were overspending because they could not see workload-level waste.

Dashboard View

Use the Standard vs OptOps toggle below to compare baseline waste against optimized performance.

Case Study Dashboard

Cluster-01 (Production) · us-east-1 · 52 nodes

Avg Efficiency36.8%

Waste Score56.3%

Monthly Cost$1,162

Risk StatusAt Risk

Pod Efficiency Distribution

Excellent: 2%
Good: 4%
Fair: 39%
Poor: 55%

Cost by Namespace

High waste concentration and weak workload efficiency.

prod

dev

gmp-system

gitlab-runner

optops-system

Before vs After Comparison Table

Metric	Before	After
Monthly Infrastructure Cost	$1,162	$464
Monthly Savings	--	$698
Cost Reduction	--	60%
Optimization Coverage	--	95%

Why This Worked

Advisory-first approach with read-only deployment — trust before automation.
Pod and namespace-aware Kubernetes intelligence.
Continuous optimization cycles instead of one-time audits.
Measurable ROI that engineering and finance both trust — realized savings reported separately from remaining opportunity.

Ready to See Your Cluster Waste?

Website: www.optops.ai

Email: prince@optops.ai

Phone: +91 98115 77006

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