Docker vs Kubernetes for First ML Model: When to Use Each

You Probably Don't Need Kubernetes Yet

Most ML engineers waste a week setting up Kubernetes for their first production model when a single Docker container would've shipped in an afternoon. I've seen teams spend more time debugging pod networking than actually improving their model.

Here's the decision tree that would've saved me three failed deployments: if your model serves under 100 requests per second and you're a solo developer or small team, Docker Compose is enough. If you're coordinating 10+ microservices across multiple machines with auto-scaling requirements, Kubernetes starts paying for itself. Everything in between is a judgment call based on your ops capacity.

But that's oversimplified. Let me show you what actually breaks when you pick the wrong tool.

The Docker-Only Deployment That Worked for 6 Months

I deployed a YOLOv8 object detection API using nothing but Docker and an AWS EC2 t3.medium instance. The entire production setup was a docker-compose.yml file, a FastAPI server, and a GitHub Actions workflow:

python
# app.py

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*Continue reading the full article on [TildAlice](https://tildalice.io/docker-vs-kubernetes-first-ml-model/)*