Container

A container is a lightweight, standalone package that bundles an application with all its dependencies, libraries, and configuration files into a single executable unit. Containers share the host operating system's kernel but run in isolated user spaces, making them far more efficient than traditional virtual machines.

Why It Matters

Containers solve the classic "it works on my machine" problem. When deploying an AI assistant, you need Node.js, system libraries, model provider SDKs, and your gateway binary to all be present and compatible. A container image captures this exact environment, ensuring identical behavior whether running locally, in CI/CD pipelines, or on a cloud platform like Railway. This reproducibility is essential when managing complex AI stacks with multiple interacting services.

How It Works

A container image is built from a Dockerfile, a declarative script that specifies a base image, installs dependencies, copies source code, and defines the startup command. When you run the image, the container runtime creates an isolated process with its own filesystem, network namespace, and resource limits. Multiple containers can run side by side on the same host without interfering with each other.

Multi-stage builds are a common pattern for AI deployments. The first stage compiles the application from source, while the second stage copies only the compiled artifacts into a minimal runtime image. This reduces the final image size and attack surface significantly.

In Practice

When deploying an AI assistant in a container, use environment variables for runtime configuration, volume mounts for persistent data, and health checks for reliability. Keep images small by using slim base images and cleaning up build artifacts. Always pin your base image versions to avoid unexpected breaking changes during rebuilds.