How Observing the ‘Cartwheel Effect’ Reduced Our Cloud Infrastructure Costs by 37%

The Hidden Connection Between Coin Collecting and Cloud Cost Optimization

Let me show you how an obscure coin collecting trick helped our team slash cloud costs by 37% last quarter. It all started when I noticed something surprising – cloud optimization shares more with numismatics than you’d think. Just like rare coins reveal hidden details when tilted, your cloud infrastructure shows waste patterns when examined from multiple angles.

What Coin Collectors Can Teach Us About Cloud Waste

That “cartwheel effect” coin experts love? It’s the shimmering pattern that appears when you tilt a coin just right. We discovered similar hidden patterns in our AWS bill by examining infrastructure three different ways:

• Time-based waste: Databases humming along at 3 AM when everyone’s asleep
• Configuration waste: Virtual machines sized for Christmas traffic in July
• Architectural waste: Services talking in circles when they should take direct paths

AWS Cost Optimization: The Cartwheel Method in Action

Right-Sizing EC2 Instances Through Angular Analysis

Our cloud cost breakdown revealed 22% savings by shifting perspectives:

1. Vertical Check (Resource Use): Like inspecting a coin’s edge, we tracked CPU usage peaks and valleys:

   aws cloudwatch get-metric-statistics \
     --namespace AWS/EC2 \
     --metric-name CPUUtilization \
     --dimensions Name=InstanceId,Value=i-1234567890abcdef0 \
     --statistics Average \
     --start-time 2023-01-01T00:00:00 \
     --end-time 2023-03-31T23:59:59 \
     --period 86400

2. Horizontal Scan (Workload Patterns): Matching usage spikes to actual team activities
3. Depth Inspection (Cost Attribution): Tagging resources to specific projects and teams

The Serverless Savings Rotation

Tilting our view of Lambda functions exposed three adjustment points:

• Memory vs. runtime sweet spots
• Cold start patterns during deployments
• Concurrency limits choking performance

Simple tweaks here cut our Lambda bill by 41% – while actually speeding up response times.

Azure Billing: Exposing Hidden Savings Through Multiple Angles

Virtual Machine Scale Set Optimization

Azure’s cost tools helped us spot idle resources:

az consumption usage list \
  --start-date 2023-01-01 \
  --end-date 2023-03-31 \
  --query "[?contains(meterDetails.meterName,'Virtual Machines')]" \
  --output table

We found entire VM families running nightly reports that could finish in minutes.

Disk Storage Tilt Analysis

By examining storage from three angles:

1. How often files were actually opened
2. Performance needs vs. reality
3. Backup versions piling up

We moved 17TB of “just in case” data to cool storage – saving enough monthly to fund our team’s coffee habit.

GCP Savings: The Kubernetes Cost Cartwheel

Cluster Right-Sizing Through Rotation

Our GKE inspection revealed nodepools sized for worst-case scenarios:

gcloud container clusters list \
  --format="table(name,location,currentMasterVersion,currentNodeCount,currentNodeCount)"

Three fixes that mattered:

• Auto-provisioning thresholds matching actual traffic
• Spot instances for batch jobs
• Smarter pod resource limits

BigQuery Cost Tilting Method

Looking at queries differently uncovered:

1. Midnight analysis jobs using premium slots
2. Historical data no one queried
3. Complex joins eating budget

Adjusting these dropped costs by 34% without slowing anyone down.

The FinOps Cartwheel Framework: A 5-Step Implementation Guide

Step 1: Establish Your Cost Observation Points

Start by setting up these five cost viewpoints:

1. Engineering: "Are we using what we have?"
2. Finance: "What's this actually costing?"
3. Business: "Does this create value?"
4. Architecture: "Could this work smarter?"
5. Time: "When do we really need this?"

Step 2: Implement Multi-Angle Monitoring

Build dashboards that combine:

• Native tools like AWS Cost Explorer
• Third-party FinOps platforms
• Custom usage trackers

Step 3: Conduct Weekly Cost Rotation Reviews

Our team’s quick daily checks:

• Monday: Compute resources
• Tuesday: Storage systems
• Wednesday: Data transfers
• Thursday: Serverless functions
• Friday: Commitment planning

The Serverless Cost Cartwheel: Lambda, Azure Functions, and Cloud Run

Execution Profile Tilting

For AWS Lambda tuning:

aws lambda get-function-configuration \
  --function-name my-function \
  --query "[FunctionName, MemorySize, Timeout]"

Match settings to real needs:

• Memory vs. speed tradeoffs
• Timeout safety margins
• Concurrency hot spots

Cold Start Rotation Strategy

We warmed up functions by:

1. Scheduling provisioned capacity before meetings
2. Trimming bulky initialization code
3. Reusing execution environments

Actionable Takeaways for Immediate Savings

Try these today:

• 15-Degree Bill Review: Pick three new angles to examine your cloud invoice
• Rotation Audit: Check four random hours across a workday
• Cost Reflection: Ask “How does this code change affect spending?”

Conclusion: Becoming a Cloud Cost Numismatist

Just like finding hidden details on a rare coin, cloud cost optimization reveals its secrets when you look from multiple angles. By applying these “cartwheel effect” techniques across AWS, Azure, and GCP, we’ve consistently found 25-40% savings – often while improving performance. The real lesson? Sometimes you need to tilt your perspective to see the savings right in front of you.

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