How I Saved $12,000/Year with One Python Script (Real Client Story)
Real world Devops case study
Hey there,
I want to share a real project I worked on recently that perfectly shows why Python is non-negotiable for DevOps engineers in 2025.
This isn’t theory. This is a case study that saved a real company real money.
The Call That Started Everything
A client reached out with a simple question: “Our AWS bill keeps growing. Can you help us figure out why?”
They were managing 6 AWS accounts with over 100 RDS Postgres databases. Their cloud costs were climbing every month, but nobody could pinpoint the exact cause.
Most people would start clicking around the AWS console, checking instances one by one.
I took a different approach. I used Python to solve this problem.
Step 1: Write a Script to See the Problem
I wrote a Python script using boto3 to pull storage utilization data from every RDS instance across all 6 accounts.
The script generated an Excel report showing:
Allocated storage
Used storage
Wasted space
Potential monthly savings
I ran it overnight. The next morning, the report was ready.
The results were shocking.
The Discovery
Some databases had 500GB of extra space sitting unused.
Others had 200GB+ of wasted storage.
Almost every database was massively over-provisioned.
One database allocated 600GB was using only 80GB. Another with 400GB allocation was using 120GB.
We observed clear patterns: developers were allocating storage based on “what if we need it someday” rather than actual requirements.
The waste was adding up to over $1,000 per month. That’s $12,000+ annually, just sitting there doing nothing.
The Problem Gets Worse
Here’s where it gets tricky: AWS doesn’t let you simply reduce RDS storage.
You can’t shrink it. You can’t restore from a snapshot with less space.
The only solution? Create a new RDS instance with smaller storage and manually migrate each database.
For 100+ databases across 6 accounts, doing this manually would take months.
And that’s assuming zero mistakes, which in production environments is impossible.
The Solution: Automation
I built a Python automation system to handle the entire migration process:
The Analysis Script: Generated the storage report that showed us exactly where the waste was and calculated potential savings.
The Migration Script:
Pulls RDS instance details using boto3
Takes database backup with pg_dump
Creates a duplicate RDS instance with optimized storage
Waits for the new instance to be ready
Restores the database using pg_restore
Swaps old and new instances by renaming them
Stops the old instance for safe deletion
The Production Deployment:
Dockerized the entire automation
Deployed it as an ECS task on AWS
Created a Lambda function to trigger migrations
Set up the workflow to handle multiple databases
The flow was simple: Lambda triggers → ECS task starts → Python script runs → Database migrates → Old instance stops → Savings begin.
The Results
Analyzed 100+ RDS databases across 6 AWS accounts
Identified exact savings potential before any migration
Reduced storage waste by 60% on average
Saved $1,000+ per month ($12,000+ annually)
Zero downtime during migrations
Fully automated - no manual intervention needed
What I Learned
Initially, I used pg_dump and pg_restore for migrations. For large databases (500GB+), this was too slow and required downtime.
I switched to pgsync for live migrations - much faster with zero downtime.
The biggest lesson? Observing usage patterns before making changes is critical. The report showed us exactly where the waste was, allowing us to prioritize high-impact migrations first.
Why This Story Matters to You
This project perfectly demonstrates why Python is essential for modern DevOps work.
You can’t solve problems like this with:
Basic bash scripts
Manual AWS console clicking
Simple CLI commands
You need Python to:
Interact with cloud APIs at scale
Process and analyze infrastructure data
Build intelligent automation
Deploy production-ready solutions
The client didn’t hire me because I knew how to click buttons in AWS console.
They hired me because I could write code that solved their $12,000 problem automatically.
That’s the difference between a tool operator and a problem solver.
Want to Build Solutions Like This?
I teach Python specifically for DevOps engineers in my bootcamp.
Not generic Python programming. Python for real DevOps work:
AWS automation with boto3
Infrastructure scripting
Production-ready code
Real projects like this case study
The complete code from this project is available in my GitHub repo: github.com/akhileshmishrabiz/python-for-devops
And if you want to learn how to build similar automation solutions yourself, check out my Python for DevOps bootcamp
P.S. If you found this case study valuable, reply and let me know what kind of automation challenges you’re facing. I might write about them in future newsletters.
Superb Case Study, mind-blowing