Most Kubernetes clusters are running at 20-35% resource utilization. That means 65-80% of your cloud bill is paying for capacity you're not using.

Here's a quick audit you can run right now to find out exactly how much you're wasting.

Step 1: Check Your Cluster-Wide Utilization

kubectl top nodes

Look at the CPU% and Memory% columns. If either average is below 40%, you have significant optimization potential.

Step 2: Find Your Most Overprovisioned Workloads

Look for deployments where actual usage is far below requests:

kubectl top pods --all-namespaces --no-headers | sort -k3 -rn | head -20

Compare these numbers against the requests in your deployment specs. The gap between "requested" and "used" is money you're burning.

Step 3: Calculate the Waste

A rough formula:

Monthly Waste = (Requested - Used) / Requested x Monthly Compute Bill

Example: If your cluster requests 100 vCPUs but only uses 30, and your monthly compute bill is $15,000:

Waste = (100 - 30) / 100 x $15,000 = $10,500/mo wasted

The Three Quickest Wins

1. Right-size your top 10 most expensive pods — adjust requests to 1.3x actual P95 usage. Typical savings: 30-50%.

2. Enable Cluster Autoscaler if you haven't — let the cluster scale nodes to match actual demand instead of keeping peak capacity 24/7.

3. Kill zombie workloads — find deployments with zero traffic and staging environments nobody has touched in weeks.

These three moves alone typically save 25-40% on your Kubernetes bill.

---

I built a full course covering the complete playbook: rightsizing, autoscaler tuning, spot instance strategies, KEDA scale-to-zero, namespace governance, and building FinOps practices into your team. Members are seeing 40-60% reductions in their cloud spend.

Most Kubernetes clusters are running at 20-35% resource utilization. That means 65-80% of your cloud bill is paying for capacity you're not using.

Here's a quick audit you can run right now to find out exactly how much you're wasting.

Step 1: Check Your Cluster-Wide Utilization

kubectl top nodes --no-headers | awk '{
  cpu_used += $3; mem_used += $5; count++
} END {
  printf "Avg CPU utilization: %.0f%%\n", cpu_used/count
  printf "Avg Memory utilization: %.0f%%\n", mem_used/count
}'

If either number is below 40%, you have significant optimization potential.

Step 2: Find Your Most Overprovisioned Workloads

Look for deployments where actual usage is far below requests:

kubectl top pods --all-namespaces --no-headers | sort -k3 -rn | head -20

Compare these numbers against the requests in your deployment specs. The gap between "requested" and "used" is money you're burning.

Step 3: Calculate the Waste

A rough formula:

Monthly Waste = (Requested - Used) / Requested × Monthly Compute Bill

Example: If your cluster requests 100 vCPUs but only uses 30, and your monthly compute bill is $15,000:

Waste = (100 - 30) / 100 × $15,000 = $10,500/mo wasted

The Three Quickest Wins

1. Right-size your top 10 most expensive pods — adjust requests to 1.3× actual P95 usage. Typical savings: 30-50%.

1. Enable Cluster Autoscaler if you haven't — let the cluster scale nodes to match actual demand instead of keeping peak capacity 24/7.

1. Kill zombie workloads — find deployments with zero traffic and staging environments nobody has touched in weeks.

These three moves alone typically save 25-40% on your Kubernetes bill.

---

I built a full course that covers the complete playbook: rightsizing, autoscaler tuning, spot instance strategies, KEDA scale-to-zero, namespace governance, and building FinOps practices into your team. Members are seeing 40-60% reductions in their cloud spend.