How ‘Cherry Picking’ Your CI/CD Pipeline Can Slash Costs by 30%

The Hidden Costs of CI/CD

Your CI/CD pipeline might be costing more than you think. After auditing our own setup, I discovered how small changes could dramatically cut costs while making deployments faster and more reliable. If you’re a DevOps lead or SRE, this is the kind of “quiet expense” you can’t afford to ignore.

Here’s the thing: optimizing CI/CD isn’t just about speed. It’s about building a system that saves money, reduces headaches, and gets your team back to what matters—shipping great code. I’ll walk you through how we used a ‘cherry-picking’ approach to trim 30% off our pipeline costs, with fewer failures and happier developers.

Understanding the CI/CD Pipeline Economics

CI/CD pipelines automate everything from code integration to deployment. But over time, they can bloat with unnecessary steps, wasting time and resources. The real win? Finding and fixing the few things that drag everything down.

Calculating the True Cost

Cost isn’t just about cloud bills. To get the full picture, ask:

• Compute Resources: What are your jobs actually using in CPU, memory, and storage?
• Time: How long do developers wait for builds? That idle time adds up fast.
• Failure Rates: Failed deployments mean downtime, rollbacks, and tons of wasted engineering hours.

Identifying Inefficiencies

Our pipeline was a mess. We were running duplicate tests, using clunky build scripts, and pulling dependencies all over again for every run. The result? Slower builds, higher bills, and more failed releases. Sound familiar?

The Cherry-Picking Strategy

Instead of rebuilding our pipeline from scratch, we focused on the biggest pain points. We call it “cherry-picking”—targeting the few things that matter most. Here’s how we did it.

1. Optimizing Build Scripts

Builds were eating up time and resources. We audited our scripts and made two key changes:

• Parallelizing Tasks: Split our build into parallel jobs. Build time dropped from 30 minutes to 10.
• Cutting Redundancy: Removed duplicate dependency installs and unnecessary tests.

Example: Here’s how we parallelized tests in our Jenkins pipeline:


pipeline {
agent any
stages {
stage('Build') {
steps {
sh 'make build'
}
}
stage('Test') {
parallel {
stage('Unit Tests') {
steps {
sh 'make test-unit'
}
}
stage('Integration Tests') {
steps {
sh 'make test-integration'
}
}
}
}
}
}


2. Managing Dependencies Efficiently

Dependencies were slowing us down. We fixed it with smarter caching and selective installs.

• Caching Dependencies: GitLab CI/CD now caches dependencies between runs.
• Selective Installation: Scripts now install only what’s needed, nothing more.

Example: Caching in our GitLab CI/CD config:


image: python:3.9

cache:
paths:
- .pip/

stages:
- build
- test

build:
stage: build
script:
- pip install --cache-dir .pip -r requirements.txt
- make build

test:
stage: test
script:
- make test


3. Reducing Failed Deployments

Nothing kills momentum like a failed deploy. We made our pipeline more resilient with:

• Automated Rollbacks: Failed deployments now roll back in seconds, not hours.
• Pre-Deployment Checks: Added gates to ensure deployments only go ahead when conditions are right.
• Health Checks: Now we catch problems early, before they become outages.

Example: Rolling back automatically in GitHub Actions:


name: Deploy

on:
push:
branches:
- main

jobs:
deploy:
runs-on: ubuntu-latest
steps:
- name: Deploy to Production
run: |
kubectl apply -f deployment.yaml
sleep 30
kubectl rollout status deployment/my-app || kubectl rollout undo deployment/my-app


4. Leveraging Infrastructure as Code (IaC)

We used Terraform and Ansible to keep environments consistent and automate provisioning.

• Consistent Environments: Terraform ensures staging and production match exactly.
• Automated Provisioning: Spin up new environments in minutes, not days.

Example: Defining a standard AWS environment in Terraform:


provider "aws" {
region = "us-west-2"
}

resource "aws_instance" "web" {
ami = "ami-0c55b159cbfafe1f0"
instance_type = "t3.medium"
key_name = "my-key"
vpc_security_group_ids = [aws_security_group.web.id]
subnet_id = aws_subnet.main.id
}


Measuring the ROI of CI/CD Optimization

Optimizing CI/CD isn’t just technical—it’s financial. We tracked key metrics to prove the value:

• Build Time: Cut from 30 to 10 minutes.
• Compute Costs: Dropped 30% thanks to leaner builds.
• Deployment Failures: Down 50% with automated rollbacks and checks.
• Developer Productivity: Faster builds mean more time for coding, less waiting.

Real-World Impact

The results were clear. We deployed more often, with fewer failures and less downtime. The compute savings alone paid for the effort. But the biggest win? Engineers stopped spending their days fixing rollbacks. They got back to building features and squashing bugs.

Automated rollbacks were a turning point. No more late-night troubleshooting. Just a quiet, reliable pipeline doing its job.

Key Takeaways

Optimizing CI/CD isn’t a one-time project. It’s an ongoing practice. From our experience:

• Start Small: Fix the biggest drains first. Don’t boil the ocean.
• Leverage Automation: Automate rollbacks, builds, and checks. Your team will thank you.
• Measure ROI: Track build times, failures, and costs. Show the value.
• Involve the Team: Get feedback from developers and SREs. They know the pain points.

Conclusion

The ‘cherry-picking’ approach cuts through the noise. By focusing on high-impact areas, we slashed costs, boosted reliability, and made deployments less stressful. For SREs and DevOps leads, that’s the dream: a pipeline that’s fast, cheap, and dependable.

This isn’t about flashy overhauls. It’s about practical, measurable improvements. Your team will deploy with confidence. Your cloud bill will shrink. And your developers? They’ll finally get their time back.

Related Resources

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