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Platform Engineering in the AI Era

Building Developer Platforms for Cloud-Native and AI-Native Organizations

This book is 100% completeLast updated on 2026-07-31

Software teams are building faster than ever, but scaling engineering takes more than great code. This book shows how to create developer platforms that simplify delivery, support cloud-native systems and prepare organizations for the demands of AI. Practical, technical and grounded in real-world experience.

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About the Book

This book provides a comprehensive guide to designing, building, operating, and evolving modern internal developer platforms for organizations at scale. It covers the full spectrum from foundational DevOps practices through cloud-native architectures to AI infrastructure, offering technical depth, architectural patterns, implementation guidance, and strategic frameworks for senior engineers, platform teams, cloud architects, engineering managers, and technology leaders navigating the transition to platform-centric software delivery in an AI-driven world.

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About the Author

Steve Publications

Steve is a technology professional with more than 20 years of experience in software development, server infrastructure, cybersecurity, vulnerability research and reverse engineering. Throughout his career, he has designed, secured, analyzed and tested complex software and infrastructure, with a particular focus on understanding how systems fail and how they can be made more secure.

Outside of work, Steve enjoys sharing knowledge with the technology community. He collaborates with researchers, industry experts and technology professionals to write practical books covering software development, cybersecurity, cloud computing, networking, DevOps, artificial intelligence and enterprise technologies. His books focus on practical learning through clear explanations, real-world examples and hands-on exercises. With more than two decades of industry experience, his goal is to help IT professionals, students and technology enthusiasts build useful skills and stay current in a rapidly changing industry.

We believe readers deserve to know how our books are created. Most of our authors are not native English speakers, so we use AI to help translate, proofread manuscripts, fix grammar, improve sentence structure and make technical explanations easier to read. AI is used as an editing tool only. It does not replace the research, technical knowledge or hands-on experience behind our books. Some of our authors also prefer to remain anonymous for privacy or professional reasons. In those cases, we publish their work under a different name. The author's name may be different, but the quality of the content and our review process remain the same.

Every book is written, reviewed and maintained by experienced technology professionals, with contributions from our private technical community of more than 420 engineers and researchers from Ukraine, Belarus and Russia. We spend far more time validating technical accuracy and keeping our content up to date than generating text. We are always interested in working with experienced professionals who have deep expertise in a particular technology or domain. If you would like to publish a book with us or help review an existing manuscript, we'd love to hear from you. Send us a message describing your area of expertise. We are especially interested in niche technologies, specialized skills and emerging topics that are underrepresented in existing technical literature.

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Contents

Table of Contents

Building Developer Platforms for Cloud-Native and AI-Native Organizations

Introduction: The Platform Imperative

  1. The Developer Productivity Crisis
  2. Complexity as the Enemy
  3. From DevOps to Platform Engineering
  4. What This Book Will Teach You

Chapter 1: Foundations: The Evolution of Platform Engineering

  1. Before DevOps: The Infrastructure Silo
  2. The DevOps Movement and Its Limits
  3. Site Reliability Engineering and the SRE Model
  4. Platform Engineering as Product Thinking
  5. Core Principles of Modern Platforms

Chapter 2: Platform Team Topologies and Organization

  1. The Four Team Topology Patterns
  2. Centralized Platform Teams and Their Limits
  3. Embedded Platform Engineers
  4. Platform Communities of Practice
  5. Scaling Platform Organizations

Chapter 3: Platform Product Management

  1. Treating Developers as Customers
  2. Platform Roadmaps and Prioritization
  3. Measuring Platform Success
  4. Feedback Loops and Continuous Improvement
  5. Avoiding the Internal Tool Trap

Chapter 4: Organizational Change Management for Platform Engineering

  1. Why Technical Platforms Fail Without Organizational Strategy
  2. The Hidden Costs of Resistance
  3. Understanding the Roots of Resistance
  4. The Stakeholder Alignment Framework
  5. Building a Change Coalition
  6. Communication Strategies That Work
  7. The Pilot Program Approach
  8. Handling Shadow IT and Existing Tooling
  9. Aligning Incentives and Performance Management
  10. Measuring Cultural Adoption Beyond Vanity Metrics
  11. The Long-Term View: Continuous Cultural Evolution

Chapter 5: Cloud-Native Foundations: Kubernetes and Containers

  1. Why Kubernetes Became the Standard
  2. Cluster Architecture and Control Plane Design
  3. Workload Management: Pods, Deployments, and Beyond
  4. Networking and Service Communication
  5. Storage Abstractions and Data Persistence
  6. Multi-Tenancy and Resource Isolation
  7. Service Mesh: Istio, Linkerd, and Traffic Management

Chapter 6: Infrastructure as Code and GitOps

  1. The Declarative Infrastructure Paradigm
  2. Terraform Ecosystem and Module Design
  3. Kubernetes-Native IaC: Crossplane and Helm
  4. GitOps Workflows with ArgoCD and Flux
  5. Policy Enforcement in the Pipeline
  6. Drift Detection and Remediation

Chapter 7: CI/CD Pipelines and Developer Experience

  1. Pipeline Architectures for Scale
  2. Ephemeral Environments for Development
  3. Progressive Delivery: Canaries, Blue-Green, and Feature Flags
  4. Test Automation Strategies
  5. Optimizing Build Speeds at Enterprise Scale
  6. CI/CD for AI Workloads and Model Artifacts
  7. Developer Experience as a First-Class Concern

Chapter 8: Developer Portals and Self-Service Platforms

  1. The Role of the Developer Portal
  2. Software Catalogs and Service Inventory
  3. Golden Paths and Software Templates
  4. Self-Service Infrastructure Provisioning
  5. Balancing Standardization with Flexibility
  6. Platform APIs and Extensibility

Chapter 9: Security, Governance, and Compliance by Default

  1. Security as a Platform Service
  2. Policy as Code with OPA and Kyverno
  3. Identity, Access, and Secrets Management
  4. Software Supply Chain Security
  5. Compliance Automation and Audit Trails
  6. Zero Trust Architecture Patterns
  7. Security for AI Workloads and Agentic Systems

Chapter 10: Observability, Reliability, and Resilience Engineering

  1. The Three Pillars of Observability
  2. Service Level Objectives and Error Budgets
  3. Distributed Tracing at Scale
  4. Incident Response and Postmortem Culture
  5. Chaos Engineering and Resilience Testing
  6. Resilience Patterns: Circuit Breakers, Bulkheads, and Retries
  7. Event-Driven Architecture and Stream Processing

Chapter 11: Multi-Cloud, Hybrid Cloud, and Platform Portability

  1. When Multi-Cloud Makes Sense
  2. Hybrid Cloud Architecture Patterns
  3. Abstracting Provider-Specific Services
  4. Networking Across Cloud Boundaries
  5. Data Residency and Sovereignty
  6. Migration Strategies Between Clouds
  7. AI Workload Portability Across Clouds

Chapter 12: FinOps and Platform Economics

  1. The FinOps Framework
  2. Cost Allocation and Showback Models
  3. Right-Sizing and Resource Optimization
  4. Spot Instances and Cost-Aware Scheduling
  5. Platform ROI and Value Measurement
  6. Building Cost Awareness into Developer Workflows
  7. FinOps for AI and GPU Workloads

Chapter 13: Data Platform Architecture for AI-Native Organizations

  1. Why Data Platforms Are the Foundation of AI Infrastructure
  2. Data Platform Reference Architecture
  3. Ingestion Patterns: Batch, Streaming, and Hybrid
  4. Lakehouse Table Formats: Iceberg, Delta Lake, and Hudi
  5. Feature Stores: Architecture and Trade-offs
  6. Data Transformation: Batch ETL, Streaming, and dbt
  7. Data Quality: Enforcing Trust at Scale
  8. Data Governance: Access Control, Lineage, and Compliance
  9. The Medallion Architecture Pattern
  10. Data Platforms Supporting AI Workloads

Chapter 14: AI Infrastructure: GPUs, Vector Databases, and Model Serving

  1. GPU Infrastructure and Cluster Design
  2. Training Pipelines and Distributed Training
  3. Vector Databases and Embedding Infrastructures
  4. Model Serving and Inference Platforms
  5. LLMOps: Managing LLM Workloads in Production
  6. Cost Management for AI Workloads
  7. Data Platforms: Feature Stores, Lakehouses, and Pipelines

Chapter 15: Agentic AI Systems and AI-Assisted Development

  1. Architecting for Agentic Workloads
  2. Tool Use and API Access Patterns
  3. Safety, Guardrails, and Human-in-the-Loop
  4. AI-Assisted Software Development at Scale
  5. Platform Integration with AI Coding Assistants
  6. Emerging Patterns and Unknowns

Chapter 16: Platform Automation, Orchestration, and Future Trends

  1. Advanced Automation Patterns
  2. Platform Orchestration and Control Planes
  3. WebAssembly and the Next Abstraction Layer
  4. Edge Computing and Distributed Platforms
  5. Serverless Evolution and FaaS at Scale
  6. The Future of Platform Engineering

Conclusion: Building Your Platform Strategy

  1. Assessing Your Current State
  2. A Phased Approach to Platform Maturity
  3. Building the Business Case
  4. Key Decisions and Trade-offs
  5. The Path Forward

References

Glossary

Index

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