Platform engineering is the discipline of designing, building and maintaining the platform that serves as the foundation for developers to efficiently build, deploy, monitor and scale software.
The goal of platform engineering is to centralize infrastructure, tools and workflows, ensuring developers can focus on writing code instead of managing complex infrastructure. Platform engineers abstract away the complexity of cloud environments, CI/CD pipelines and operational tools to create a seamless and repeatable development process that aligns with organizational standards.
Key responsibilities of a platform engineer include:
- Centralizing resources: Creating a single access point for tools, documentation, and environments to simplify development.
- Standardizing workflows: Establishing consistent pipelines, environments and deployment patterns.
- Abstracting complexity: Simplifying interaction with services and infrastructure through automation and self-service.
- Improving developer experience: Reducing friction and inefficiencies in development workflows.
- Automating infrastructure management: Creating context-aware workflows for tasks like environment setup, testing and deployments.
- Gathering feedback: Treating the platform as a product, continuously improving based on developer needs.
Many of these principles originated in DevOps, demonstrating that platform engineering is an evolution of DevOps rather than a separate discipline.
Components of an Internal Developer Platform
An Internal Developer Platform (IDP) is a collection of tools and services that enable developers to deliver software quickly and reliably. It supports the entire software development lifecycle, from coding to deployment to monitoring.
Key Components:
- Source Code Management (SCM): Centralized systems for version control and collaboration (e.g., GitHub, GitLab, Bitbucket).
- CI/CD Pipelines: Automated pipelines for building, testing and deploying code (e.g., Jenkins, GitHub Actions, CircleCI, Travis CI, Spinnaker).
- Infrastructure as Code (IaC): Tools for infrastructure provisioning and orchestration (e.g., Terraform, Pulumi, Chef, Ansible).
- Container Orchestration: Platforms like Kubernetes, Docker Swarm and OpenShift for consistent deployment environments.
- Monitoring and Observability: Tools for tracking system performance and health (e.g., Prometheus, Grafana, Datadog, New Relic, Splunk).
- Security and Compliance: Automated security checks integrated into CI/CD pipelines (e.g., Snyk, Aqua Security, SonarQube, HashiCorp Vault).
- Internal Developer Portal: A central entry point for developers to access information, standards and self-service.
These tools were widely used in DevOps before platform engineering formalized their role in improving developer experience.
Why a Product Mindset Matters in Platform Engineering
Traditionally, platform engineers focused on solving technical problems. However, as IDPs become crucial internal products, platform engineers need to adopt a product management mindset.
Key Approaches:
- User-Centric Thinking: Developers are the users of the platform, so their experience should be at the center of platform decisions.
- Roadmap and Prioritization: Maintain a roadmap to prioritize improvements that have the highest impact on developers.
- Continuous Feedback & Iteration: Adopt an agile approach—shipping incremental improvements regularly based on developer input.
- Measuring Success: Use developer productivity metrics and operational KPIs to track platform performance.
- Systems Thinking: Understand how different components interact and optimize systems holistically.
By focusing on outcomes rather than specific feature requests, platform engineers can create scalable, future-proof solutions rather than a patchwork of tools.
How Platform Engineers Can Operate Like Product Managers
Anticipate Developer Needs, Don’t Just Fulfill Requests
A key difference between product managers and order takers is the ability to anticipate needs. Rather than simply fulfilling feature requests, platform engineers should dig deeper to understand root problems and deliver scalable solutions.
Example: Instead of just improving deployment scripts for microservices, introduce a self-service platform that standardizes deployment across all teams.
Survey Adjacent Teams: Ensure Security and Scalability
Like product managers gathering insights from stakeholders, platform engineers should collaborate with security, SRE, infrastructure and operations teams to ensure security, scalability and reliability.
Examples:
- Security: Integrate tools like Snyk or SonarQube into CI/CD pipelines.
- SREs and Infrastructure: Introduce automated monitoring and alerting tools like Prometheus and Grafana.
- Operations: Build observability and centralized logging with tools like Elasticsearch or Splunk.
Prioritize Features According to Research Themes
Engineers need to balance short-term needs with long-term platform vision.
- Impact vs. Effort Quadrant: Prioritize high-impact, low-effort changes for quick wins.
- Balance Short-Term Needs with Long-Term Vision: Optimize pipelines today while planning for future scalability.
Example: While automating deployments is a quick win, a GitOps-based strategy (e.g., ArgoCD) prepares for future growth.
Build Iteratively With Feedback Loops
Like software products, platforms should be built iteratively, with continuous feedback loops to refine improvements.
- Define an MVP (Minimum Viable Platform): Start small and solve the most pressing developer needs first.
- Iterate Based on Developer Feedback: Use surveys, platform metrics and discussions to refine features.
- Ship Continuous Improvements: Release small updates regularly based on real-world usage.
Example: If teams struggle with traceability, add tools like Grafana and Prometheus for monitoring.
Measure Success With Data-Driven KPIs
Platform engineers should track key performance indicators (KPIs) to measure platform impact on developer productivity.
Key Metrics:
Developer Productivity
- Flow State: Minimize context switching for uninterrupted coding.
- Build and Test Cycle Times: Ensure fast feedback loops.
- Developer Onboarding Time: Reduce time for new hires to become productive.
- Toil and Cognitive Load: Measure the effort developers spend on repetitive, low-value tasks. High toil leads to burnout, inefficiencies and lower satisfaction.
- Example: Reducing the time developers spend setting up environments by implementing automated provisioning.
- Cognitive Load: The mental effort required for developers to perform tasks. Lowering unnecessary complexity increases efficiency and focus.
- Example: Simplifying documentation access and automating routine processes.
DORA Metrics
- Lead Time for Changes: Time from commit to production.
- Deployment Frequency: How often teams successfully deploy.
- Change Failure Rate: Percentage of releases causing failures.
- Mean Time to Resolve (MTTR): Time to resolve incidents.
Platform Adoption and Performance
- Platform Efficiency: Track feedback loops and system performance.
- Infrastructure Uptime: Ensure stability and reliability.
- Developer Adoption: Monitor platform usage and address adoption gaps.
By measuring these KPIs, platform engineers can continuously improve the platform and ensure it meets developer needs.
Conclusion
Platform engineering is about building and maintaining an internal developer platform that abstracts complexity and empowers developers. By adopting a product management mindset, platform engineers can create seamless development experiences that boost productivity and accelerate software delivery.
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