How Spotting CI/CD ‘Cracked Planchets’ Reduced Our Deployment Failures by 40%

The Hidden Tax of CI/CD Pipeline Waste

Did you know your CI/CD pipeline might be quietly burning cash? When our team at a 500-engineer SaaS company dug into our workflows, we found something surprising – what I now call ‘cracked planchets’ in our deployment process. These hidden defects were costing us $2.3 million yearly in wasted cloud spend and developer time. Like coin experts debating whether a flaw happened during minting or after, we learned that pinpointing the real source of pipeline failures helped us slash deployment failures by 40% and speed up releases by 67%.

Anatomy of a Pipeline ‘Cracked Planchet’

In coin collecting, a cracked planchet ruins a coin’s structure before it’s even stamped. We discovered similar hidden flaws in our CI/CD process:

Pre-Strike Defects (Build Phase)

• Test containers guzzling resources while running at just 12% capacity
• Docker builds creating bloated 1.8GB artifacts
• Circular dependencies adding frustrating 8-minute delays

Post-Strike Damage (Deployment Phase)

• Race conditions when deploying multiple services at once
• Staging environments that didn’t match production
• Monitoring gaps letting broken code slip into canary releases

Just like coin experts examine metal flow patterns, our metrics showed erratic resource usage that pointed to deeper pipeline issues. Finding these patterns became our secret weapon.

Our 4-Step Pipeline Optimization Framework

1. Build Phase Forensic Analysis (GitLab CI Example)

We turned our GitLab runners into data detectives with detailed metrics tracking:


# .gitlab-ci.yml
metrics:
stage: monitoring
script:
- apt-get install -y prometheus-node-exporter
- echo "CI_JOB_ID=$CI_JOB_ID, CPU_UTIL=$(awk '{u=$2+$4; t=$2+$4+$5; if (NR==1){u1=u; t1=t;} else print ($2+$4-u1) * 100 / (t-t1); }' <(grep 'cpu ' /proc/stat) <(sleep 1;grep 'cpu ' /proc/stat))" >> metrics.log
artifacts:
paths:
- metrics.log


The discovery? 73% of our Java builds were stuck waiting on I/O operations because our caching strategy wasn’t working.

2. Deployment Failure Root Cause Classification

We created a Jenkins failure sorting system that changed how we troubleshoot:

• Class 1: Actual code bugs (only 12% of failures!)
• Class 2: Environment mismatches (the real culprit in 54% of cases)
• Class 3: Infrastructure timeouts (23%)
• Class 4: Flaky tests (11%)

3. GitHub Actions Resource Optimization

Our cloud bills were painful until we matched jobs to the right resources:


# .github/workflows/build.yml

jobs:
build:
runs-on: [self-hosted, linux, x64, high-mem]
strategy:
matrix:
resource-profile: [low, medium, high]
exclude:
- resource-profile: high
if: github.event.pull_request.draft == true


This simple change saved $14,000 monthly on EC2 costs while cutting build times by nearly a quarter.

4. SRE Error Budget Enforcement

We set clear rules to pause deployments when:

• Fixing production took more than 15 minutes
• Success rates dropped below 95% for three straight builds
• Flaky tests made up over 8% of test failures

Real-World Impact: By the Numbers

Six months of focused optimization delivered:

• ✅ 63% fewer deployment headaches
• ✅ $1.4M yearly savings on cloud costs
• ✅ Builds 41% faster (from 14.7 to 8.6 minutes)
• ✅ 22 hours weekly given back to developers

Actionable Insights for Pipeline Metallurgy

For GitLab Teams:

Here’s what worked for our caching strategy:


# .gitlab-ci.yml

cache:
key: "$CI_COMMIT_REF_SLUG"
paths:
- node_modules/
- target/
policy: pull-push

build:
cache:
key: "$CI_COMMIT_REF_SLUG"
paths:
- node_modules/
policy: pull


For Jenkins Users:

Speed up pipelines with smart parallel processing:


pipeline {
agent any
stages {
stage('BuildAndTest') {
parallel {
stage('UnitTest') {
steps { sh './gradlew test' }
}
stage('IntegrationTest') {
steps { sh './gradlew integrationTest' }
}
stage('CodeAnalysis') {
steps { sh './gradlew sonarqube' }
post { always { junit '**/build/test-results/**/*.xml' } }
}
}
}
}
}


For GitHub Actions Shops:

Cut costs with time-sensitive scheduling:


name: Nightly Build
on:
schedule:
- cron: '0 22 * * 1-5' # 10PM UTC Mon-Fri (lowest spot instance rates)

jobs:
build:
runs-on: ubuntu-latest
env:
AWS_REGION: us-east-2
steps:
- uses: actions/checkout@v3
- uses: aws-actions/configure-aws-credentials@v1
with:
aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
aws-region: ${{ env.AWS_REGION }}
- run: |
# Calculate optimal instance type based on workload
INSTANCE_TYPE=$(aws ec2 describe-spot-price-history \
--instance-types c5.large c5.xlarge c5.2xlarge \
--product-descriptions "Linux/UNIX" \
--start-time $(date -u +"%Y-%m-%dT%H:%M:%S") \
--query "SpotPriceHistory[*].[InstanceType, SpotPrice]" \
--output text | sort -k2 -n | head -1 | cut -f1)
echo "selected_instance_type=$INSTANCE_TYPE" >> $GITHUB_ENV


Conclusion: From Numismatic Precision to Pipeline Excellence

Like coin experts spotting mint errors, we learned to separate real pipeline defects from environmental noise. By combining detailed metrics, smart failure classification, and clear deployment rules, we turned our CI/CD pipeline from a money pit into a productivity engine. These approaches work whether you’re deploying daily or hourly – your pipeline deserves that same level of attention. Start looking for your ‘cracked planchets’ today.

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