Containerize Your AI Agent Stack With Docker Compose: 4 Patterns That Work

Your AI agent runs fine on your laptop. Then you deploy it and discover you need a model server, a vector database, a message queue, and monitoring -- all wired together correctly. You spend two days writing shell scripts. Docker Compose defines your entire AI agent stack in a single YAML file. One command brings it all up. Here are 4 patterns that handle the common deployment scenarios. Pattern 1: Model Runner as a Compose Service Docker Compose now supports a top-level models element that declares AI models as first-class infrastructure. Instead of manually running a model server and wiring environment variables, you declare the model and bind it to your agent service. Here is the compose.yaml: services: agent: build: context: ./agent ports: - "8080:8080" environment: - OPENAI_API_KEY=${OPENAI_API_KEY} models: llm: endpoint_var: MODEL_RUNNER_URL model_var: MODEL_RUNNER_MODEL depends_on: - vectordb vectordb: image: qdrant/qdrant:v1.17.0 ports: - "6333:6333" volumes: - qdrant_data:/qdr