Docker

Docker is a platform for building, shipping, and running applications inside lightweight, portable containers. A container packages an application with all its dependencies -- runtime, libraries, system tools, and configuration -- so it runs consistently across any environment, from a developer's laptop to a cloud production server.

How It Works

Docker uses a layered image system defined by a Dockerfile. Each instruction in the Dockerfile (installing packages, copying files, setting environment variables) creates a new layer. These layers are cached and reused, making builds fast and efficient. When you run an image, Docker creates a container -- an isolated process with its own filesystem, network, and process space -- but sharing the host's OS kernel, which makes containers far lighter than traditional virtual machines.

Docker images are stored in registries (like Docker Hub or private registries) and can be pulled and run on any machine with the Docker runtime. This eliminates "works on my machine" problems and makes deployments reproducible.

Why It Matters

Docker is the backbone of modern application deployment. It provides isolation between services, simplifies dependency management, and enables consistent environments across development, staging, and production. For AI assistant platforms, Docker makes it possible to package complex applications -- including the assistant runtime, gateway, and all dependencies -- into a single deployable unit.

In Practice

Platforms like Railway use Docker images as the deployment artifact. A multi-stage Dockerfile might build an AI assistant from source in one stage and create a minimal production image in another. Environment variables configure the container at runtime, and volumes provide persistent storage for state and workspace data. Understanding Docker basics -- images, containers, volumes, and networking -- is essential for anyone deploying and maintaining AI applications in production.