Case Study: Building an IDP from Scratch
In this final article of the series, we will follow the complete implementation of an Internal Developer Platform for a tech startup with 30 engineers facing the typical growing pains: inconsistent deployments, slow onboarding, tool sprawl, lack of standardization, and frequent production incidents.
The project is structured in 4 phases over a 4-month timeframe, with a team of 3 people (1 senior platform engineer, 1 SRE, 1 DevOps engineer). We will examine the architectural choices, selected tools, problems encountered, and most importantly the measurable results achieved.
What You'll Learn
- How to plan IDP implementation in a startup
- Phase 1: Kubernetes, Terraform, and basic CI/CD
- Phase 2: Backstage, service catalog, and golden paths
- Phase 3: DORA observability, security, and feedback collection
- Phase 4: Optimization, AI-assisted operations, and scaling
- Before/after metrics and ROI calculation
Context: The Startup and Its Challenges
TechFlow (fictional name) is a B2B SaaS startup with 30 engineers distributed across 5 teams. The company offers a workflow automation platform for enterprise companies. After 2 years of rapid growth, technical and infrastructure debt is slowing development:
- 6 different CI/CD tools used by various teams (Jenkins, GitHub Actions, CircleCI, bash scripts)
- Manual deployments: 3 people know how to deploy to production, creating a bottleneck
- 3-week onboarding: a new developer takes an average of 15 working days to become productive
- 4 incidents per month caused by inconsistent configurations between staging and production
- No standardization: each service has its own structure, conventions, and documentation (when present)
# Initial situation: baseline metrics
baseline-metrics:
dora:
deployment-frequency: "1-2 per week"
lead-time: "5-7 days"
mttr: "4-6 hours"
change-failure-rate: "28%"
developer-experience:
onboarding-time: "15 working days"
nps-score: 3.2 / 10
tools-used-daily: 11
time-on-non-code: "45%"
operational:
incidents-per-month: 4
manual-deployments: "70%"
services-without-owner: "35%"
services-without-docs: "60%"
team:
total-engineers: 30
platform-team: 0 (part-time DevOps by senior engineers)
teams: 5
services: 22
Phase 1 (Month 1-2): The Foundations
The first phase focuses on building the platform foundations: a properly configured Kubernetes cluster, Infrastructure as Code with Terraform, and a standardized CI/CD pipeline.
Phase 1 Deliverables:
- Kubernetes cluster (EKS): production and staging cluster with namespace isolation, RBAC, and resource quotas per team
- Terraform modules: reusable modules for namespaces, databases (RDS), cache (ElastiCache), and networking
- Standardized GitHub Actions: workflow templates for build, test, security scan, and deploy
- ArgoCD: GitOps-based deployments with automatic sync from Git
- Basic monitoring: Prometheus + Grafana with dashboards for basic service metrics
# Phase 1: selected tool stack
phase-1-stack:
compute:
provider: AWS
service: EKS (Kubernetes 1.29)
nodes: 6 (3 prod, 2 staging, 1 platform)
instance-type: m6i.xlarge
iac:
tool: Terraform 1.7
state: S3 + DynamoDB locking
modules:
- eks-cluster
- namespace-with-rbac
- rds-postgresql
- elasticache-redis
- github-actions-runner
ci-cd:
build: GitHub Actions
deploy: ArgoCD
registry: ECR (Elastic Container Registry)
workflow-template:
stages:
- lint
- unit-test
- build-image
- security-scan (Trivy)
- push-to-ecr
- update-argocd-manifest
monitoring:
metrics: Prometheus (kube-prometheus-stack)
dashboards: Grafana
alerting: Alertmanager -> Slack
logging: Loki (basic)
security:
secrets: AWS Secrets Manager (phase 1)
network: Calico network policies
rbac: Kubernetes RBAC per namespace
timeline:
start: "Week 1"
end: "Week 8"
milestones:
- week-2: "EKS cluster operational"
- week-4: "Terraform modules validated"
- week-6: "CI/CD template working for 3 pilot services"
- week-8: "All services migrated to Kubernetes"
Phase 2 (Month 2-3): Developer Portal and Golden Paths
With the foundations in place, the second phase focuses on developer experience: Backstage as the developer portal, golden paths for the main service types, and a service catalog to track service ownership.
Phase 2 Deliverables:
- Backstage: installation with Software Catalog, TechDocs, and 2 Software Templates
- Service catalog: all 22 services registered with ownership, dependencies, and SLAs
- Golden Paths: templates for REST API (NestJS), Web App (React), and Worker (Node.js)
- TechDocs: standardized documentation for all services migrated to the portal
- Self-service: developers can create a new service from the portal in 15 minutes
Phase 2 Quick Win
The turning point was when the first developer created a complete new microservice (CI/CD, monitoring, documentation) in 12 minutes through the Backstage template. The team's reaction: "Why didn't we do this sooner?" This generated a wave of spontaneous adoption.
Phase 3 (Month 3-4): Observability and Security
The third phase completes the platform with advanced observability, security hardening, and the first feedback collection cycle:
- DORA dashboard: Grafana dashboard with the 4 DORA metrics calculated automatically
- Distributed tracing: Tempo integrated with OpenTelemetry for cross-service tracing
- Security: migration from AWS Secrets Manager to Vault, Kyverno policy enforcement, complete network policies
- Developer survey: first NPS survey with structured feedback collection
- Improved alerting: SLO-based alerts with reduced false positives
# Phase 3: DORA Grafana dashboard
grafana-dashboard:
title: "Platform DORA Metrics"
panels:
- name: "Deployment Frequency"
type: time-series
query: |
sum(increase(
argocd_app_sync_total{
project="production"
}[24h]
))
target: "> 1 deploy/day per service"
- name: "Lead Time for Changes"
type: gauge
query: |
avg(
github_pr_merge_time_hours{
base_branch="main"
}
) + avg(
argocd_sync_duration_seconds / 3600
)
target: "< 4 hours"
thresholds:
- value: 4
color: green
- value: 24
color: yellow
- value: 168
color: red
- name: "Change Failure Rate"
type: stat
query: |
sum(argocd_app_sync_total{status="failed"}[30d])
/
sum(argocd_app_sync_total[30d])
* 100
target: "< 15%"
- name: "MTTR"
type: gauge
query: |
avg(
pagerduty_incident_resolution_time_minutes
)
target: "< 60 minutes"
Results: Before vs After
After 4 months of implementation, the results were significant and measurable:
- Deployment Frequency: from 1-2/week to 3-5/day (10x improvement)
- Lead Time: from 5-7 days to 4-6 hours (20x improvement)
- MTTR: from 4-6 hours to 45 minutes (6x improvement)
- Change Failure Rate: from 28% to 8% (71% reduction)
- Onboarding: from 15 days to 3 days (80% reduction)
- NPS Score: from 3.2 to 7.8 out of 10
- Incidents: from 4/month to 1/month (75% reduction)
# Results after 4 months: final metrics
final-metrics:
dora:
deployment-frequency: "3-5 per day (per team)"
lead-time: "4-6 hours"
mttr: "45 minutes"
change-failure-rate: "8%"
classification: "High performer (near Elite)"
developer-experience:
onboarding-time: "3 working days"
nps-score: 7.8 / 10
tools-used-daily: 4 (Backstage, IDE, Git, Slack)
time-on-non-code: "15%"
operational:
incidents-per-month: 1
manual-deployments: "0% (all GitOps)"
services-without-owner: "0%"
services-without-docs: "5%"
roi-calculation:
investment:
platform-team-salaries: "3 FTE * 4 months"
tooling-costs: "$2,400/month (Backstage hosting, monitoring)"
total-investment: "~$180,000"
savings:
developer-productivity: "+35% (30 eng * $120k avg * 35% = $1.26M/year)"
reduced-incidents: "-75% incidents * $15k/incident = $45k/year"
faster-onboarding: "5 new hires/year * 12 days saved = $36k/year"
total-annual-savings: "~$1.34M/year"
roi: "645% in first year"
payback-period: "~2 months"
Lessons Learned
Every implementation has its challenges. Here are the most important lessons learned during the project:
- Start small, demonstrate value quickly: do not try to build the perfect platform. The first working Golden Path generated more enthusiasm than any presentation
- Engage early adopters: identify 2-3 enthusiastic teams as pilots. Their success will convince the skeptics
- Feedback is gold: every week the platform team collected feedback. The best decisions were guided by developer data, not platform team opinions
- Documentation is part of the product: a template without documentation is a template nobody uses
- Do not force adoption: make the platform so useful that teams choose it spontaneously. Coercion generates resistance
Mistakes Not to Repeat
Transparency about mistakes made during implementation:
- Too much initial complexity: we started with Istio service mesh in month 1. It was too early. We removed it and reintroduced it in month 5 when the team was ready
- Underestimating migration: migrating the existing 22 services to Kubernetes took more time than expected. A more gradual approach was needed
- Too many Backstage plugins: at launch we had installed 12 plugins. Developers were confused. We went back to 4 essential plugins
- Ignoring networking: network policies were implemented late, causing connectivity problems that were difficult to diagnose
The Most Important Lesson
An IDP is not a project with an end date: it is a continuously evolving product. After the first 4 months, the work is not finished: it has just begun. The platform team continues to collect feedback, prioritize improvements, and iterate. The platform you build today will be different from the one in 6 months, and that is a good thing.
Future Roadmap
With the base platform operational, the plan for the next 6 months includes:
- Month 5-6: service mesh introduction (Istio), multi-environment preview, and cost dashboards
- Month 7-8: AIOps for anomaly detection, advanced auto-scaling, and level 2 self-healing
- Month 9-10: public platform API for custom integrations, internal plugin marketplace
- Month 11-12: multi-region deployment, automated disaster recovery, SOC2 compliance
This series of 12 articles has covered all the fundamental aspects of Platform Engineering: from theory to practical implementation, from architecture to security, from metrics to AI integration. Platform Engineering is not just a technology trend: it is how modern software organizations build and scale their delivery capabilities. Start today, start small, and let the results guide the path.