How Implementing FinOps Discovery Processes Reduced Our Cloud Infrastructure Costs by 37%

How Our Developer Workflow Tweaks Slashed Cloud Costs

Did you know small changes in developer habits can make big dents in your cloud bill? When our team started tracking workflows through a FinOps lens, we spotted surprising connections between coding patterns and infrastructure waste. Here’s how implementing FinOps discovery processes trimmed our cloud costs by 37% – without sacrificing performance.

The Practical Framework That Unlocked Our Savings

1. Spotting Cost Surprises

We began with simple monitoring using tools your team probably already has:

• AWS Cost Explorer’s anomaly alerts
• Azure’s smart spending insights
• Google Cloud’s resource recommender

Here’s what worked for us: Setting daily alerts at 15% over baseline costs helped catch a forgotten $14k/month Kubernetes cluster

2. Resource Detective Work

Our cloud engineers built simple scripts to track hidden waste:

# AWS Resource Tagging Audit Script
import boto3

def list_untagged_resources():
ec2 = boto3.client('ec2')
instances = ec2.describe_instances()
untagged = [i for res in instances['Reservations']
for i in res['Instances']
if not i.get('Tags')]
return untagged

Running this monthly helped us reclaim $8.2k in monthly spending from ghost resources.

3. Making Smart Adjustments

Three changes delivered the biggest impact:

• Switching m5.large to m5a.xlarge instances (23% savings)
• Buying Azure Reserved Instances (57% cheaper than on-demand)
• Using Google’s committed discounts for steady workloads

Serverless Savings Without the Headaches

Taming Lambda Costs

We cut Lambda spending 41% through simple tweaks:

• Setting baseline concurrency for busy functions
• Right-sizing memory allocations
• Trimming deployment packages by 68%

# AWS Lambda Power Tuning visualization
aws stepfunctions start-execution \
--state-machine-arn arn:aws:states:us-east-1:123456789012:stateMachine:PowerTuningStateMachine \
--input "{\"lambdaARN\": \"your-lambda-function-arn\", \"num\": 50, \"payload\": {}}"

Automated Savings That Run Themselves

Our favorite set-and-forget policies:

• Auto-shutting non-production environments nights/weekends ($22k/month saved)
• Moving cold data to cheaper storage tiers automatically
• Letting AWS suggest reserved instance purchases

What Our Cost Dashboard Revealed

After 6 months of FinOps fine-tuning:

• 37% lighter cloud bills ($1.4M annual savings)
• 28% better resource usage
• Half as many untagged resources
• Every $1 invested returned $12 in savings

Keeping Savings Consistent

We maintain results through regular habits:

• Quick weekly cost chats with engineering teams
• Monthly spending reports showing team impacts
• Quarterly instance checkups with AWS Optimizer

The Real Win: Building Cost Awareness

What started as a cost-cutting mission became part of our engineering culture. By baking FinOps practices into daily work, we’ve kept our 37% savings consistent while actually improving deployment speed. The best part? These steps work for any team willing to look critically at their cloud habits.

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