AI Projects Always Need Docker. Always.

I went down this path last month. Simple plan: integrate Claude into an ERPNext module, build a workflow automation layer. Ship it in two weeks.

Then reality hit.

The first issue was dependency hell. I'm running Node.js 18, the client's on 16. My local setup works, their server breaks. I thought about it for five minutes, realized I was wasting time, and just Dockerized it.

That single decision cascaded into learning things I didn't plan for.

Multi-stage Dockerfiles became mandatory when my image hit 1.2GB. I optimized it down to 280MB by separating build and runtime stages. Sounds boring, but it matters when you're deploying to multiple servers.

Then came orchestration headaches. The AI service needed Redis for caching, PostgreSQL for state management, the actual Node service running the agents. Docker Compose solved this. One docker-compose.yml file and the entire stack runs locally exactly like production.

Volumes got interesting too. Logs, model caches, persistent embeddings—all need to survive container restarts. Learning how bind mounts work versus named volumes saved me hours of debugging why my vector cache kept disappearing.

Here's the thing nobody tells you: AI projects require this infrastructure. You're dealing with async jobs, external API calls, state management across multiple services. A basic Node script doesn't cut it. You need isolation, reproducibility, easy scaling.

The irony? I learned Docker faster by actually needing it than I would have in a standalone course. Real problems teach better than tutorials.

Now when I start any AI integration—whether it's Telegram bots, workflow automation, or custom modules—my first move is the Dockerfile. Not after it breaks in production. At the start.

The rabbit hole pays for itself.