How I Used a Rare Coin Finding Technique to Slash My AWS, Azure, and GCP Bills by 40%

Let me tell you about my cloud bill nightmare. Last year, I was staring at a $12,000 monthly tab across AWS, Azure, and GCP for a project that should’ve cost half that. Sound familiar?

That’s when I stumbled onto something unexpected—a cost-cutting method borrowed from coin collectors. Not kidding. I adapted the meticulous process of finding rare “doubled die” coins (the DDODDR 2021 D 1C- Unlisted on all doubled die reference sites MUST SEE technique) to cloud billing. The result? A 40% reduction in our infrastructure costs within three months.

Understanding the DDODDR 2021 D 1C- Unlisted Method

Think of your cloud bill like a coin collection. At first glance, everything looks uniform. But when you examine each coin (or service) closely, you’ll find subtle differences—some valuable, some worthless. That’s the core of the DDODDR method: microscopic inspection of your cloud footprint to find hidden waste.

No black magic. Just five simple steps that helped me turn our bloated infrastructure lean:

Breaking Down the DDODDR Method

The DDODDR method breaks down into several key steps:

• Detailed Data Collection: Gather comprehensive data on your cloud resource usage. This includes CPU utilization, memory usage, network traffic, and storage utilization. I start with a week of perfect data—no estimates.
• Deep Dive Analysis: Analyze the collected data to identify patterns and anomalies. This is similar to zooming in on the minute details of a rare coin to uncover its unique features. Look for the “off-center” stuff—overprovisioned databases, zombie instances that never die.
• Optimized Deployment: Based on the analysis, deploy optimizations such as auto-scaling, reserved instances, and spot instances to match demand more efficiently. Less guessing, more data.
• Dynamic Refinement: Continuously monitor and refine the deployment to ensure optimal performance and cost-effectiveness. Cloud environments change fast—your cost controls should too.
• Resource Review: Regularly review resource allocation to ensure no resource is underutilized or over-provisioned. I check every three weeks. You’d be surprised what creeps up.

Applying the Method to AWS Cost Optimization

AWS offers a vast array of services, but without proper management, costs can spiral out of control. Here’s how I applied the DDODDR method to cut our AWS bill by 35%.

1. Detailed Data Collection with AWS Cost Explorer

Use the AWS Cost Explorer to collect detailed data on your current and past AWS usage. This tool provides granular insights into your spending across different services and regions.

aws ce get-cost-and-usage \
  --time-period Start=2023-01-01,End=2023-12-31 \
  --granularity MONTHLY \
  --metrics "BlendedCost" "UsageQuantity"

Pro tip: I run this on the first of every month and compare the current month to the same period last year. Patterns jump out fast.

2. Deep Dive Analysis with AWS Trusted Advisor

Run AWS Trusted Advisor to identify areas for optimization. This includes recommendations for idle resources, underutilized EC2 instances, and opportunities for Reserved Instances or Savings Plans.

Here’s what shocked me: Trusted Advisor found 17 EC2 instances running at less than 5% CPU utilization. We shut down 14 immediately.

3. Optimized Deployment with Auto Scaling

Implement Auto Scaling policies to dynamically scale resources based on demand. This ensures you only pay for the resources you actually use.

aws autoscaling put-scaling-policy \
  --auto-scaling-group-name my-asg \
  --policy-name ScaleInPolicy \
  --scaling-adjustment -1 \
  --adjustment-type ChangeInCapacity

For our web app, we set scale-down policies at 30% CPU utilization. Our bill dropped $800/month overnight.

4. Dynamic Refinement with CloudWatch Alarms

Set up CloudWatch Alarms to monitor resource utilization and trigger scaling actions when thresholds are breached.

I set alarms at 80% (scale up) and 20% (scale down) for our API tier. No more over-provisioning for traffic spikes that never come.

5. Resource Review with AWS Cost Allocation Tags

Use Cost Allocation Tags to track spending by department, project, or environment. This helps identify which resources are contributing the most to your costs and refine your deployment strategy accordingly.

I color-coded tags by environment (prod/green, staging/yellow, dev/red). Now I can see exactly which team is spending what—and why.

Optimizing Azure Billing

Azure provides robust tools for cost management and optimization, but the DDODDR method can help you take it a step further.

1. Detailed Data Collection with Azure Cost Management

Use Azure Cost Management to collect detailed data on your Azure spending. This tool provides insights into usage patterns and cost trends.

I export to CSV and build pivot tables. The “cost per department” view revealed our QA environment was costing more than production.

2. Deep Dive Analysis with Azure Advisor

Run Azure Advisor to get personalized recommendations for optimizing your Azure resources. This includes recommendations for rightsizing VMs, deleting unused resources, and implementing reserved instances.

Advisor flagged 20 “orphaned” disks. Deleting them saved $420/month. Tiny win, but they add up.

3. Optimized Deployment with Azure Reserved Instances

Commit to Reserved Instances for predictable workloads to get significant discounts. Reserve instances for up to 72% savings compared to on-demand pricing.

We reserved our SQL databases (80% utilization, predictable load). Saved $1,200/month. The math is easy: if you know you’ll use it, reserve it.

4. Dynamic Refinement with Azure Monitor

Use Azure Monitor to track resource utilization and set up alerts for underutilized resources. This helps you dynamically refine your deployment to match demand.

Set alerts at 15% CPU for non-critical workloads. Now I get Slack messages when someone leaves a dev VM running overnight.

5. Resource Review with Azure Tags

Use Azure Tags to track spending by department, project, or environment. This helps you identify inefficiencies and refine your cost optimization strategy.

We tag everything: “owner=jane”, “project=checkout-redesign”, “env=prod”. Makes accountability easy.

Reducing GCP Savings

Google Cloud Platform (GCP) offers a range of tools for cost optimization, and the DDODDR method can help you leverage them effectively.

1. Detailed Data Collection with GCP Cost Table

Use the GCP Cost Table to collect detailed data on your GCP spending. This tool provides granular insights into your costs across different services and projects.

I love GCP’s “cost breakdown” view. You can drill down to the VM level—exactly what I need for the DDODDR approach.

2. Deep Dive Analysis with GCP Recommender

Run GCP Recommender to get personalized recommendations for optimizing your GCP resources. This includes recommendations for rightsizing VMs, deleting unused resources, and implementing committed use discounts.

Recommender suggested downgrading 12 VMs from n2-standard-8 to n2-standard-4. We did—no performance hit, $900/month saved.

3. Optimized Deployment with Committed Use Discounts

Commit to Committed Use Discounts for predictable workloads to get significant discounts. Commit to resources for up to 57% savings compared to on-demand pricing.

We committed to our data warehouse (bigquery) for 3 years. The discount was worth the commitment.

4. Dynamic Refinement with Cloud Monitoring

Use Cloud Monitoring to track resource utilization and set up alerts for underutilized resources. This helps you dynamically refine your deployment to match demand.

Set up “idle VM” alerts. Caught three developers running GPU instances for “testing” that lasted three months.

5. Resource Review with GCP Labels

Use GCP Labels to track spending by department, project, or environment. This helps you identify inefficiencies and refine your cost optimization strategy.

Labels like “team=backend”, “app=api-gateway” make cost reports make sense.

Serverless Computing Costs

Serverless computing is a powerful tool for cost optimization, but it requires careful management to avoid unexpected costs. The DDODDR method can help you optimize serverless costs effectively.

1. Detailed Data Collection with AWS Lambda and Azure Functions

Monitor execution times and invocations for your serverless functions. Use tools like AWS CloudWatch and Azure Monitor to collect detailed data.

Our “image resize” Lambda was running 10x longer than needed. Saved $300/month by optimizing the code.

2. Deep Dive Analysis with Cost Anomaly Detection

Use cost anomaly detection tools to identify unusual spending patterns in your serverless workloads.

Caught a rogue function that was triggering every 10ms instead of every 10 minutes. Disaster averted.

3. Optimized Deployment with Reserved Concurrency

Set Reserved Concurrency for critical functions to ensure they always have capacity, while allowing non-critical functions to scale down when not in use.

For our payment webhook, we reserved 10 instances. For reporting, we let it scale to zero.

4. Dynamic Refinement with Automated Alerts

Set up automated alerts to notify you of sudden spikes in serverless function usage, allowing you to take corrective action quickly.

Got an alert at 2 AM about a 500% increase in auth function calls. Turned out to be a misconfigured cron job.

5. Resource Review with Tagging

Use tagging to track spending by function, environment, or project, helping you identify and optimize high-cost functions.

Tags like “owner=security-team”, “purpose=auth” make it clear who to talk to when costs jump.

Conclusion

The DDODDR 2021 D 1C- Unlisted on all doubled die reference sites MUST SEE method is a powerful approach to cloud cost optimization. By adopting this method, you can achieve significant reductions in your AWS, Azure, and GCP bills. The key is to meticulously collect and analyze data, optimize your deployments dynamically, and regularly review your resources to ensure efficiency. Whether you’re a CTO, a freelancer, or a VC managing cloud infrastructure, this method can help you achieve more efficient code, faster deployments, and most importantly, direct reductions in your monthly cloud infrastructure costs. Start implementing this approach today and see your cloud costs plummet.

My team now treats cloud optimization like a monthly ritual—not a one-time project. The 40% savings? That’s $60,000 back in our pocket this year. Not bad for borrowing tricks from coin collectors.

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