Platform Engineering Maturity Model

Platform engineering has emerged as a discipline focused on building internal developer platforms. But maturity varies widely—from ad-hoc scripts to sophisticated self-service platforms. Understanding where you are helps chart a path forward.

Here’s a maturity model for platform engineering.

The Platform Engineering Spectrum

Why Maturity Matters

platform_maturity_impact:
  low_maturity:
    - Developers wait for ops
    - Manual processes everywhere
    - Inconsistent environments
    - Slow deployments
    - High cognitive load

  high_maturity:
    - Developer self-service
    - Automated workflows
    - Consistent, secure defaults
    - Fast, reliable deployments
    - Developers focus on features

Maturity Levels

Level 1: Ad-Hoc

level_1_characteristics:
  infrastructure:
    - Manual provisioning
    - Snowflake servers
    - No infrastructure as code
    - Tribal knowledge

  deployment:
    - Manual deployments
    - SSH to servers
    - Run scripts by hand
    - Deploy on schedule only

  developer_experience:
    - "File a ticket and wait"
    - Long lead times
    - Inconsistent environments
    - Local dev differs from prod

  team_structure:
    - Central ops team
    - Tickets for everything
    - Adversarial relationship

indicators:
  - "We deploy on Thursdays"
  - "Ask Bob, he knows how it works"
  - "My machine is different from staging"
  - "Infrastructure changes take weeks"

Level 2: Standardized

level_2_characteristics:
  infrastructure:
    - Infrastructure as code emerging
    - Some automation scripts
    - Documentation exists
    - Still centrally managed

  deployment:
    - CI pipelines exist
    - Deployments scripted
    - Some self-service
    - Manual approvals common

  developer_experience:
    - Documented processes
    - Standard environments
    - Some self-service tools
    - Still requires tickets for many things

  team_structure:
    - DevOps team (shared)
    - Some embedded support
    - Better collaboration

indicators:
  - "We have Terraform, but only DevOps can run it"
  - "CI builds the artifact, but deploys are manual"
  - "Check the wiki for the process"
  - "You need approval from DevOps for that"

Level 3: Self-Service

level_3_characteristics:
  infrastructure:
    - Infrastructure as code standard
    - Self-service provisioning
    - Guardrails and policies
    - Templates and modules

  deployment:
    - Automated pipelines
    - Self-service deployments
    - Feature flags
    - Rollback automated

  developer_experience:
    - Self-service portal/CLI
    - Minimal wait times
    - Good documentation
    - Observability included

  team_structure:
    - Platform team supports
    - Developers own pipelines
    - Collaboration focused

indicators:
  - "I provisioned a new service in 10 minutes"
  - "We deploy multiple times per day"
  - "I used the platform template"
  - "Observability was automatic"

Level 4: Product-Minded

level_4_characteristics:
  infrastructure:
    - Self-service everything
    - Secure by default
    - Compliance built-in
    - Cost-optimized

  deployment:
    - GitOps workflows
    - Progressive delivery
    - Canary and blue-green
    - Automatic rollback

  developer_experience:
    - Internal developer portal
    - Service catalog
    - Golden paths
    - Excellent DX metrics

  team_structure:
    - Platform as product
    - Product manager involvement
    - User research
    - Developer feedback loops

indicators:
  - "The platform team surveyed us about pain points"
  - "New hires ship on day one"
  - "I didn't think about security—it was built in"
  - "We measure developer satisfaction"

Level 5: Optimized

level_5_characteristics:
  infrastructure:
    - Intelligent automation
    - Self-healing systems
    - Predictive scaling
    - Cost optimization automated

  deployment:
    - Automated quality gates
    - ML-based anomaly detection
    - Automatic remediation
    - Zero-downtime everything

  developer_experience:
    - Frictionless everything
    - Instant environments
    - AI-assisted operations
    - Exceptional productivity

  team_structure:
    - Continuous improvement culture
    - Data-driven decisions
    - Industry-leading practices
    - Contributing back

indicators:
  - "The system detected and fixed the issue before we noticed"
  - "Our platform is a competitive advantage"
  - "We've open-sourced our tooling"
  - "Engineers love working here because of our platform"

Assessment Framework

Dimension Assessment

assessment_dimensions:
  infrastructure_automation:
    level_1: Manual, snowflakes
    level_2: IaC exists, centrally managed
    level_3: Self-service with guardrails
    level_4: Intelligent defaults, optimized
    level_5: Self-healing, predictive

  deployment_capability:
    level_1: Manual, scheduled
    level_2: CI exists, CD manual
    level_3: Self-service CD
    level_4: Progressive delivery
    level_5: Fully automated quality

  developer_experience:
    level_1: Tickets, waiting
    level_2: Documented processes
    level_3: Self-service tools
    level_4: Product-quality platform
    level_5: Exceptional, frictionless

  observability:
    level_1: Basic monitoring
    level_2: Metrics and logs
    level_3: Distributed tracing
    level_4: Full observability stack
    level_5: AIOps, predictive

  security:
    level_1: Reactive, manual
    level_2: Basic scanning
    level_3: Shift-left security
    level_4: Security by default
    level_5: Continuous security

Scoring

scoring_matrix:
  calculate:
    - Score each dimension 1-5
    - Average for overall maturity
    - Identify gaps

  example:
    infrastructure: 3
    deployment: 3
    developer_experience: 2
    observability: 3
    security: 2
    overall: 2.6 (Level 2-3 transition)

  priority:
    - Address lowest scores
    - Consider impact on developers
    - Balance quick wins with strategic

Improvement Path

Moving from Level 1 to 2

level_1_to_2:
  focus:
    - Standardization
    - Documentation
    - Basic automation

  actions:
    - Adopt infrastructure as code
    - Implement CI pipelines
    - Document processes
    - Create standard environments
    - Start measuring deployment frequency

Moving from Level 2 to 3

level_2_to_3:
  focus:
    - Self-service
    - Developer empowerment
    - Reduce wait times

  actions:
    - Build self-service tools
    - Create templates and modules
    - Implement CD pipelines
    - Add guardrails, not gates
    - Embed observability

Moving from Level 3 to 4

level_3_to_4:
  focus:
    - Product mindset
    - Developer experience
    - Platform as product

  actions:
    - Treat platform as product
    - Measure developer satisfaction
    - Build internal developer portal
    - Create golden paths
    - Implement progressive delivery

Metrics

Platform Metrics

platform_metrics:
  adoption:
    - % teams using platform services
    - % deployments through platform
    - Active users of self-service

  efficiency:
    - Time from commit to production
    - Lead time for changes
    - Infrastructure provisioning time
    - Onboarding time (new hire to deploy)

  quality:
    - Change failure rate
    - Mean time to recovery
    - Platform availability
    - Security incident rate

  satisfaction:
    - Developer NPS
    - Platform satisfaction score
    - Support ticket volume
    - Time waiting for platform team

Key Takeaways

Platform engineering maturity varies from ad-hoc to optimized
Assessment across dimensions reveals specific gaps
Move progressively—jumping levels rarely works
Self-service is the critical transition point (Level 3)
Product thinking differentiates Level 4
Measure adoption, efficiency, quality, and satisfaction
Focus on developer experience, not just tooling
Involve developers in platform decisions
Quick wins build momentum for larger changes
Maturity is ongoing—environments and needs evolve

Know where you are, decide where you need to be, and chart an incremental path.