How ‘Cherry Picking Your Own Fake Bin’ Can Slash Your AWS, Azure, and GCP Cloud Bills

Every developer makes small choices that quietly add up on their cloud bill. I’ve seen it firsthand—teams deploy fast, test quickly, then get hit with a surprise $10k AWS charge at the end of the month. The truth? How you build, deploy, and run code has a direct line to your cloud costs.

Think of cloud cost optimization like sorting through a bin of coins. You’ve got rare silver dollars, worn pennies, and a bunch of fakes that look good at first glance. As a collector (or a cloud architect), your job isn’t to grab everything—it’s to pick the *right* ones. No hype. No overkill. Just smart, intentional choices that cut waste and keep performance strong.

That’s what I call **cherry-picking your own fake bin**: finding the real value in your cloud environment, one deliberate decision at a time. Whether you use AWS, Azure, or GCP, this mindset can turn cloud spend from a cost center into a competitive advantage.

The Problem: Cloud Waste Is the New Technical Debt

Cloud platforms give us power like never before. But power without control? That’s where waste creeps in.

Research shows 30–40% of cloud spend is wasted—not because anyone is careless, but because it’s easy to ignore what you can’t see. Idle resources. Over-provisioned instances. Functions running with 2GB of memory when they only need 512MB. These aren’t mistakes. They’re the result of convenience over cost-awareness.

Imagine this: a dev team spins up an m5.2xlarge EC2 instance to test a new feature. It works great. But no one shuts it down. Now it’s running 24/7—costing $1,500 a month—while handling 5% of its capacity. That’s not innovation. That’s a fake coin.

Why “Cherry Picking” Matters in Cloud Cost Optimization

In coin collecting, “cherry picking” means spotting the rare, valuable pieces in a mixed lot. In cloud cost management, it means focusing on the few high-impact fixes that deliver the biggest savings—without breaking the system.

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• Find idle resources (like unattached EBS volumes or abandoned load balancers)
• Match instance types to real workload needs, not gut feelings

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• Right-size serverless functions (Lambda, Cloud Functions) based on actual usage

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• Cut redundant steps in CI/CD pipelines that run but don’t add value

Each pick isn’t about cutting everything. It’s about choosing the *right* thing to fix—like plucking a mint-condition coin from a pile of counterfeits.

Step 1: Audit Your “Fake Bin” with Cloud Cost Intelligence Tools

You can’t optimize what you can’t see. First, map your actual spend and uncover the “fake coins”—the resources that look useful but aren’t pulling their weight.

Tool Stack Recommendations

• AWS: AWS Cost Explorer, Compute Optimizer, Trusted Advisor
• Azure: Cost Management, Azure Advisor, Monitor VM Insights
• GCP: Cost Table, Recommender, Billing Export to BigQuery
• Cross-Cloud: CloudHealth by VMware, Kubecost, or Spot by NetApp

Here’s a real example: I ran AWS Compute Optimizer for a client and found a batch job using an m5.4xlarge (16 vCPUs, 64 GB RAM). It only needed 2 vCPUs and 8 GB RAM. We downsized to a t3a.xlarge—same job, same results, $2,100/month saved.

Actionable Audit Checklist

• List all running instances and filter for CPU/memory use under 20% for 30+ days
• Find unattached disks: EBS, Azure Managed Disks, GCP Persistent Disks
• Check CDN distributions (CloudFront, Azure CDN, Cloud CDN)—are they still in use?
• Delete old snapshots, unused AMIs, and orphaned load balancers
• Review serverless logs: memory use, runtime, and how often functions actually run

Step 2: Optimize Serverless Costs—The Hidden Efficiency Goldmine

Serverless is “pay-per-use,” right? Not if you’re over-allocating. A 1 GB Lambda function running 1 million times a month at 100 ms costs about 6x more than one with 512 MB. The fix? Right-size and tune concurrency.

Right-Sizing Serverless Functions (AWS Lambda Example)

Use aws lambda get-function to check current settings. Then test performance across memory levels with the AWS Lambda Power Tuning tool:

# Install and run power tuning
npm install -g serverless
serverless invoke -f powerTuning --data '{"num": 10, "parallelInvocation": true}'

This runs your function at different memory levels and shows where cost per execution is lowest. I use it to find the “sweet spot”—fast enough, cheap enough.

Pro Tip: Set concurrency limits with AWS Application Auto Scaling. For low-traffic APIs, cap it at 10. Prevents runaway bills during testing.

Azure Functions & GCP Cloud Functions

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• Azure: Set memory with WEBSITE_MEMORY_LIMIT_MB and track in Application Insights
• GCP: Adjust memory and max-instances in your gcloud functions deploy command

Step 3: Automate Right-Sizing with Infrastructure as Code (IaC)

Audits are helpful. But automation is how you keep the bin clean. Use IaC to bake cost optimization into every deployment.

Terraform Example: EC2 Instance Right-Sizing

resource "aws_instance" "web_server" {
  ami           = "ami-0abcdef1234567890"
  instance_type = "t3a.medium"  # Down from m5.large
  tags = {
    Name = "web-server"
    CostOptimization = "right-sized"
  }
}

# Hook into AWS Compute Optimizer
# Auto-create PRs when better instance types are recommended

Connect the AWS Compute Optimizer API to your CI/CD pipeline. When it spots a better fit, auto-suggest a Terraform or CloudFormation update. No more manual checks.

Kubernetes Cost Optimization

On Kubernetes, use metrics-server and Vertical Pod Autoscaler (VPA) to adjust CPU and memory requests based on real use. Add Kubecost for live cost visibility per pod, namespace, or app.

Step 4: Leverage Spot, Reserved, and Sustained-Use Discounts

Not every workload needs on-demand. Spot instances (AWS), Spot VMs (GCP), and Low-Priority VMs (Azure) can cut costs by 60–90%—if you design for resiliency.

• Use Spot for batch jobs, CI/CD runners, and stateless services
• Buy Reserved Instances (AWS) or Committed Use Discounts (GCP) for predictable, long-running apps
• Enable sustained-use discounts when usage is consistent

Real result: A client switched nightly data pipelines from on-demand EC2 to Spot + mixed instances. Saved 72%. Same job. Same output.

Step 5: Foster a FinOps Culture—Beyond the Tools

Tools help. But lasting change starts with people. Build cost awareness into your team’s DNA:

• Add cost-per-request to your dashboards—make it visible
• Host monthly “cost hackathons” to find and fix inefficiencies
• Tag all resources: Owner, Project, Environment
• Use AWS Cost Allocation Tags or GCP Billing Labels to show teams their real spend

Conclusion: Cherry-Picking Is a Discipline, Not a One-Time Task

Cloud cost optimization isn’t a project. It’s a habit. Like a coin collector who keeps refining their collection, you need to audit, adjust, and automate—again and again.

By “cherry-picking your own fake bin,” you’re not just saving money. You’re building a cloud environment that’s lean, efficient, and built to last.

Key Takeaways:

• Use cost intelligence tools to find your waste (your “fake bin”)
• Right-size serverless functions and VMs based on real data, not defaults
• Automate optimizations with IaC and CI/CD pipelines
• Use Spot, Reserved, and sustained-use pricing where it makes sense
• Make cost part of your team’s routines—not an afterthought

Start small. Audit one service. Right-size one function. Automate one policy. Each fix is a coin in your collection. And over time? Your cloud bill gets lighter, cleaner, and smarter.

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