Uncovering Hidden Cloud Cost Savings: How ‘Is it a blister or is it a ddo’ Inspired My FinOps Strategy

Ever had that moment where you’re squinting at a coin, wondering if it’s a rare doubled die or just a surface defect? I had that exact experience years ago at a coin show. And it changed how I approach cloud costs forever.

Turns out, pinpointing cloud waste isn’t so different from spotting a genuine ddo. Both require the same careful scrutiny. Both reward patience and precision. And both can save you serious money when you get it right.

The Hidden Costs of Unoptimized Cloud Workloads

After auditing hundreds of cloud environments, I’ve found one truth: most teams are paying for resources they don’t need. It’s not fraud. It’s not a system error. It’s just overlooked inefficiencies adding up.

Think of it like this: A coin collector doesn’t rely on gut feelings when examining a potential ddo. They use loupes, lighting, and years of pattern recognition. Cloud optimization works the same way. The waste isn’t in the big numbers – it’s in the quiet, persistent inefficiencies hiding in plain sight.

The numbers tell the story. Companies I’ve worked with typically find 30-50% savings through optimization. The catch? These opportunities won’t jump out at you. They’re subtle, like the faint doubling on a Lincoln cent.

Why Resource Efficiency is Non-Negotiable

Cloud billing works on a simple principle: you pay for what you tell the system you need, not what you actually use. This mismatch creates waste that compounds daily:

• Virtual machines running at 10% capacity (AWS EC2, Azure VMs, GCP Compute Engine)
• Storage volumes with nothing attached (AWS EBS, Azure managed disks, GCP persistent disks)
• Serverless functions that rarely fire (AWS Lambda, Azure Functions, GCP Cloud Functions)
• Load balancers with zero traffic

Avoid the temptation to ignore small inefficiencies. In cloud costs, as in coin collecting, surface blisters matter. They can be early warning signs of bigger systemic issues.

Adopting the ‘Doubled Die’ FinOps Mindset

The coin world has a saying: “Doubled die or doubling die? The difference is in the details.” The same applies to cloud optimization. We need to train ourselves to see the true inefficiencies beneath the surface.

The 5-Step Defect Analysis Framework

Here’s how I separate quick fixes (blisters) from strategic wins (ddos):

1. Resource Inventory Audit: Start with a complete picture. Use AWS Cost Explorer, Azure Cost Management, or GCP Billing Reports to map everything you’re paying for.
2. Utilization Analysis: Get your hands on the actual usage data. Tools like CloudWatch, Azure Monitor, and Stackdriver show what’s really happening.
3. Cost Attribution: Tag resources clearly. When costs are tied to specific teams or products, accountability follows automatically.
4. Pattern Recognition: Look for recurring issues. A single idle VM might be a one-off. Ten idle VMs with the same tag? That’s a process problem.
5. Optimization Execution: Fix the right things. Focus on changes that move the needle, not just the easy wins.

Code Example: AWS Lambda Cost Optimization Script


import boto3
import json

def analyze_lambda_functions():
lambda_client = boto3.client('lambda')
cloudwatch = boto3.client('cloudwatch')

functions = lambda_client.list_functions()
optimizations = []

for function in functions['Functions']:
# Get performance metrics
metrics = cloudwatch.get_metric_statistics(
Namespace='AWS/Lambda',
MetricName='Duration',
Dimensions=[{'Name': 'FunctionName', 'Value': function['FunctionName']}],
StartTime=datetime.utcnow() - timedelta(days=30),
EndTime=datetime.utcnow(),
Period=86400, # 24 hours
Statistics=['Average']
)

avg_duration = metrics['Datapoints'][0]['Average'] if metrics['Datapoints'] else 0

# Identify optimization opportunities
if function['MemorySize'] > 512 and avg_duration < 1000: optimizations.append({ 'type': 'memory_optimization', 'function': function['FunctionName'], 'current_memory': function['MemorySize'], 'recommended_memory': 512, 'estimated_monthly_savings': (function['MemorySize'] - 512) * 0.00001667 * 30 * 24 }) # Check for cold start patterns if function['Timeout'] > 30 and avg_duration < 5: optimizations.append({ 'type': 'timeout_optimization', 'function': function['FunctionName'], 'current_timeout': function['Timeout'], 'recommended_timeout': 10, 'estimated_monthly_savings': 0 # Indirect savings through faster scaling }) return optimizations

Serverless Computing: The 'Doubled Die' of Cloud Cost Efficiency

Serverless architecture is where the real savings happen. When configured right, these functions can deliver the same results at a fraction of the cost - often 70% less than traditional VMs.

Serverless Cost Optimization Strategies

1. Memory/Performance Tuning: Unlike a coin's fixed surface, serverless memory affects both performance and price. Tools like AWS Lambda Power Tuning help find that sweet spot.

2. Cold Start Mitigation: A single slow start doesn't mean there's an issue. Look at the full pattern. Sometimes, what looks like a defect is just normal behavior under specific conditions.

3. Concurrency Management: Think of this like coin preservation. Reserved concurrency keeps your critical functions ready, while provisioned concurrency ensures predictable workloads run smoothly.

4. Event Filtering: Set up S3 notifications to trigger only when needed. Why process 100 files when 95 of them aren't relevant?

Real-World Serverless Optimization Case

Last year, I worked with a client building a document processing pipeline. Their initial setup:

• 200ms average execution time
• 3GB memory per function (more than needed)
• 1000 invocations daily
• $480 monthly spend

After applying the doubled die approach:

• Dropped memory to 1GB (perfect for their processing needs)
• Added S3 filtering to catch only PDF files
• Set concurrency limits to prevent bottlenecks
• New monthly cost: $165 (65% savings)

Cross-Cloud Optimization: AWS, Azure, and GCP Strategies

Each cloud provider has its own optimization quirks. Here's what I've learned from working across all three:

AWS Cost Optimization

Savings Plans: Commit to consistent usage for up to 72% savings. Use AWS Cost Explorer to check your history first - no point committing if your usage fluctuates wildly.

EC2 Right-Sizing: AWS Compute Optimizer analyzes your actual usage and suggests better instance types. It's like having a coin expert tell you which auction to bid on.

Azure Billing Optimization

Azure Reserved Instances: These 1- or 3-year commitments can save up to 72% on VMs. They work best for predictable, long-running workloads.

Hybrid Benefit: Running Windows Server or SQL Server? Your existing licenses could cut costs by up to 85%. It's the cloud equivalent of finding a rare coin in your pocket.

GCP Savings Opportunities

Sustained Use Discounts: Run workloads for more than 25% of the month and get automatic discounts up to 30%. No opting in required.

Committed Use Discounts: Pre-purchase compute for 1- or 3-year terms for savings up to 57%. Works great for consistent workloads.

Code Example: GCP VM Rightsizing Script


from google.cloud import compute_v1
from google.cloud import monitoring_v3

def analyze_gcp_vms():
compute_client = compute_v1.InstancesClient()
project_id = 'your-project-id'

# Get all VMs in the project
request = compute_v1.AggregatedListInstancesRequest(project=project_id)
page_result = compute_client.aggregated_list(request=request)

optimizations = []

for zone, response in page_result:
if response.instances:
for instance in response.instances:
# Analyze CPU utilization using Cloud Monitoring
client = monitoring_v3.MetricServiceClient()
now = time.time()
seconds = int(now)
nanos = int((now - seconds) * 10**9)

interval = monitoring_v3.TimeInterval(
{"end_time": {"seconds": seconds, "nanos": nanos},
"start_time": {"seconds": (seconds - 30*24*60*60), "nanos": nanos}}
)

results = client.list_time_series(
request={
"name": f'projects/{project_id}',
"filter": f'metric.type="compute.googleapis.com/instance/cpu/utilization" resource.labels.instance_id="{instance.id}"',
"interval": interval,
"view": monitoring_v3.ListTimeSeriesRequest.TimeSeriesView.FULL
}
)

# Calculate average utilization
avg_util = sum([point.value.double_value for series in results for point in series.points]) / len(list(results))

# Recommend smaller instance if underutilized
if avg_util < 0.2 and instance.machine_type.split('-')[-1] in ['n1-highmem-96', 'n1-highcpu-96']: optimizations.append({ 'instance': instance.name, 'zone': zone.split('/')[-1], 'current_type': instance.machine_type.split('/')[-1], 'recommended_type': 'n1-standard-16', 'estimated_savings': 0.0832 * 24 * 30 # $/hour * hours * days }) return optimizations

Actionable FinOps Framework: Your 30-Day Optimization Plan

Want to start seeing savings fast? Try this approach:

Week 1: Visibility and Tagging

• Set up cost management tools (CloudHealth, CloudCheckr, or the provider's native options)
• Create clear tagging standards (Environment, Owner, Product, etc.)
• Run your first cost allocation report

Week 2: Quick Wins

• Delete unattached storage volumes (I've seen companies save thousands here)
• Resize overprovisioned VMs
• Clean up unused load balancers and NAT gateways
• Set S3 lifecycle policies for old data

Week 3: Strategic Optimization

• Check serverless function performance and adjust settings
• Look at reserved instance eligibility
• Add auto-scaling for workloads with variable demand

Week 4: Automation and Governance

• Create budget alerts and anomaly detection
• Build automated cleanup scripts
• Start regular FinOps meetings with engineering teams

Conclusion: The FinOps Mindset Shift

The coin collecting world got it right. True value comes from careful examination, not hasty judgments. The same applies to cloud costs.

Every optimization opportunity falls somewhere on the spectrum between surface blister and genuine doubled die. The goal isn't to fix everything - it's to fix the right things.

Key takeaways:

• Train yourself to spot the subtle inefficiencies that others miss
• Serverless offers the biggest savings potential when configured correctly
• Cloud providers have different optimization levers - learn them all
• Build processes for ongoing monitoring, not just one-time fixes
• Connect costs to business units and products for better accountability

Cloud cost optimization isn't about cutting corners. It's about precision. It's about understanding that in a world of automated scaling and infinite resources, the real skill is knowing exactly what you need and nothing more.

Like any good coin collector will tell you: the best finds come to those who look closely and think carefully.

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