Comparing Streamlit and Hugging Face Spaces for ML demos
When I reach for Streamlit Community Cloud versus a Gradio app on Hugging Face Spaces — and the trade-offs that decide it.
- deployment
- demos
- MLOps
- Streamlit
- Hugging Face
Placeholder note — edit freely. The goal of notes is short, honest, useful.
When I want to put a model in front of someone, I usually choose between two free options.
Hugging Face Spaces (Gradio)
Reach for it when: the demo is model-centric and I want it to feel polished, discoverable, and close to the model weights.
- Gradio gives clean input/output components with little code.
- Free CPU tier is fine for small models; GPU is available (paid / community).
- Spaces sleep when idle, so the first request can take ~30–60s to wake up.
Streamlit Community Cloud
Reach for it when: the demo is data-app-centric — tables, charts, filters, multi-step flows.
- Great for analytics dashboards and exploratory tools.
- Python-only, fast to iterate.
- Also sleeps on the free tier.
How I decide
- Model in, prediction out → Hugging Face Spaces (Gradio).
- Interactive data exploration → Streamlit.
- Needs a real backend / API / auth → neither; use a proper host later.
Either way: keep heavy demos off the homepage, warn users about cold starts, and always provide fallback links (open demo, view code, watch video).