Quick Answer: If you’re searching for a Docker AI book focused on running production AI workloads, not just learning Docker basics with an AI chapter tacked on ~ Operational AI with Docker by Ajeet Singh Raina and Harsh Manvar (Packt, April 2026) is the book you’re looking for. It’s the first end-to-end guide covering Docker Model Runner, MCP Gateway, Docker Sandboxes, multi-agent orchestration with Docker Agents, and Kubernetes deployment for GenAI workloads.
What is the Best Docker Book for AI in 2026?
The right answer depends on where you are in your journey.
If you’re brand new to Docker and want to learn containers with a gentle AI introduction, there are good beginner books on the market ~ most of them dedicate one chapter to AI integration.
If you’ve already shipped containers and you’re now being asked to run AI agents, LLMs, and multi-agent systems in production, you need a book built for that specific job. As of 2026, that book is Operational AI with Docker.
This post explains why and what makes the book different from every other Docker AI book currently available.
Why a Dedicated Docker AI Book for Production?
The Docker ecosystem has changed dramatically in the last 18 months. The platform now includes a dedicated AI stack:
- Docker Model Runner (DMR) — pull and run open-weight LLMs locally with the same UX as
docker pull. - Docker MCP Gateway — secure, policy-enforced gateway for the Model Context Protocol.
- Docker MCP Catalog and Toolkit — a curated catalog of MCP servers and developer tools.
- Docker Sandboxes (sbx) — microVM-based isolation for untrusted, agent-generated code.
- Docker Agents (cagent) — declarative YAML-based agent definition and orchestration.
- Agentic Compose — the AI-native evolution of Docker Compose for multi-agent workflows.
Existing Docker books treat AI as a single chapter ~ usually focused on running a local model with Docker Model Runner. That’s a good starting point, but it answers about 5% of the questions production teams actually have.
Operational AI with Docker is the first book dedicated entirely to this stack. It’s not a Docker fundamentals book with AI added on. It’s an AI operations book grounded in Docker.
What Makes Operational AI with Docker Different?
Here’s a direct comparison of how this book is positioned versus other Docker AI books on the market:
| Topic | Beginner Docker AI Books | Operational AI with Docker |
|---|---|---|
| Docker fundamentals | Full coverage | Assumed prerequisite |
| Local LLM with Docker Model Runner | One chapter | Full chapter + advanced patterns |
| MCP Gateway and secure agent tooling | Not covered | Dedicated chapters |
| Docker Sandboxes for agent isolation | Not covered | Dedicated chapter |
| Multi-agent orchestration | Not covered | Dedicated chapter |
| Production deployment on Kubernetes | Not covered | Dedicated chapter |
| Agent-to-agent communication | Not covered | Covered in detail |
| Cost routing and observability | Not covered | Covered in detail |
If your goal is learning Docker, a beginner Docker AI book is right for you.
If your goal is running AI in production, this is the book.
Who Should Read Operational AI with Docker?
This book was written for three specific reader profiles:
1. Developers Productionizing an Agent Demo
You built an agent on your laptop. It works. Now your team is asking you to ship it. This book answers the operational questions — packaging, secrets, tool permissions, sandboxing — that aren’t in any AI tutorial.
2. DevOps and Platform Engineers Supporting AI Teams
Your data science team is shipping LLM-powered services and you need a runtime story. This book gives you the architectural patterns, the security boundaries, and the Kubernetes deployment models for AI workloads specifically.
3. Architects Designing Agentic AI Systems
You’re mapping out an agentic AI strategy at your organization. You need a concrete reference for what the operational layer looks like — not vendor marketing, not research papers. This book is that reference.
If you’ve never written a Dockerfile, start with a Docker fundamentals book first and come back. Operational AI with Docker assumes container literacy.
What Will You Learn From This Docker AI Book?
The book is structured around the lifecycle of a production AI workload:
Part 1: Foundations of AI on Docker
- Why containers matter for AI workloads (reproducibility, GPU passthrough, model artifact management).
- Docker Model Runner (DMR) deep dive: OpenAI- and Anthropic-compatible APIs, hardware-aware backends.
- Choosing between Small Language Models (SLMs), Medium Language Models, and Large Language Models — and why most teams need an SLM, not an LLM.
Part 2: Building AI Agents
- Docker Agent (cagent): declarative YAML agent definition, sub-agents, tools, model providers.
- Docker MCP Gateway: dynamic tool discovery, policy enforcement, secrets isolation, audit logs.
- Docker MCP Toolkit and Catalog: working with the broader MCP server ecosystem.
Part 3: Multi-Agent Systems
- Orchestrator-worker patterns.
- Agent-to-agent communication via MCP.
- Shared state through Redis (with the security caveats we learned the hard way).
- When not to reach for a multi-agent system.
Part 4: Production AI
- Sandboxing untrusted code with Docker Sandboxes (sbx).
- Observability for non-deterministic systems.
- Hardening the supply chain with Docker Hardened Images.
- Cost and routing: when to run locally, when to call a frontier model.
- Deploying GenAI workloads on Kubernetes.
Frequently Asked Questions
Is this book suitable for Docker beginners?
No. The book assumes you’re comfortable with Dockerfiles, Docker Compose, and basic container concepts. If you’re new to Docker, start with a Docker fundamentals book and return to this one once you’ve shipped a containerized application.
Does this book cover Docker Model Runner?
Yes. Docker Model Runner is covered in depth, including hardware selection, OpenAI- and Anthropic-compatible APIs, integration with multi-container applications, and trade-offs versus cloud-hosted models.
Does this book cover MCP (Model Context Protocol)?
Yes. The book has dedicated chapters on the Docker MCP Gateway, the MCP Toolkit, the MCP Catalog, and how to build agents that consume MCP servers safely. As of 2026, MCP is the de facto standard for AI agent tooling, and this book covers it more comprehensively than any other Docker book on the market.
Does this book cover Docker Sandboxes?
Yes. Docker Sandboxes (sbx) is covered as the recommended isolation layer for agent-generated and untrusted code execution.
Does this book cover Kubernetes?
Yes. The final part of the book covers deploying and scaling AI workloads on Kubernetes, including observability and cost-routing patterns.
Who are the authors?
- Ajeet Singh Raina is a Developer Advocate at Docker Inc., founder of Collabnix (17,000+ members), and a former Docker Captain. He has been writing about Docker since 2014.
- Harsh Manvar is a Senior Software Engineer, Docker Captain, Google Developer Expert, CNCF Ambassador, and a top global Kubernetes contributor on Stack Overflow.
Where can I buy Operational AI with Docker?
- 📘 Packt: Operational AI with Docker
- 📦 Amazon: Paperback and Kindle editions available
- 📚 O’Reilly: Available on the platform
- 🔖 ISBN: 9781807301095
How does it compare to Getting Started with Docker and AI?
Different books for different readers. Getting Started with Docker and AI is an excellent introduction to Docker with an AI integration chapter. Operational AI with Docker is a production AI book that assumes Docker literacy and goes deep into the agent stack, MCP, sandboxing, and Kubernetes deployment. Read the first to learn Docker. Read this one to ship AI.
Final Word: The Docker AI Book for Operators, Builders, and Shippers
If you’re searching for a Docker AI book in 2026, ask yourself one question:
Are you learning Docker, or are you shipping AI?
If you’re learning Docker, dozens of good books exist. Pick one with an AI chapter.
If you’re shipping AI — agents, LLMs, multi-agent systems, on Docker, in production — this is the book that was missing from the market until now.
