If you write Python and want a web UI without learning React, you've narrowed your choices to two: Gradio and Streamlit. Both are open source, both are widely used in the ML community, and both let you ship something in an afternoon. They look interchangeable in tutorials. They aren't.
The split is simple: Gradio is built around models — inputs and outputs of a function. Streamlit is built around scripts — a top-to-bottom narrative your user scrolls through. Pick the wrong one and you'll spend your time fighting the framework instead of shipping.
Why this comparison matters
Both tools solve the same surface-level problem (Python in, web app out) but with different mental models. Choosing wrong won't break anything, but it will slow you down once your app stops being a toy. A Gradio app forced to act like a dashboard ends up with awkward layout hacks. A Streamlit app wrapping a single model ends up running the whole script on every input change.
Feature comparison
| Feature | Gradio | Streamlit |
|---|---|---|
| Core abstraction | Function with inputs/outputs | Top-down script that re-runs |
| Built-in UI components | 40+, heavy ML focus (audio, image, video, 3D) | 30+, heavy data focus (charts, dataframes, metrics) |
| Layout control | Blocks API (rows, columns, tabs) | Columns, tabs, containers, sidebars |
| State management | Per-request, explicit state objects | Session state, reruns on widget change |
| Public sharing | One-line share=True tunneled URL (72h) | No built-in tunnel — deploy to share |
| Free hosting | Hugging Face Spaces (unlimited public apps) | Streamlit Community Cloud (public apps) |
| Auth / private apps | Basic HTTP auth, OAuth via Spaces | OAuth in Community Cloud, full auth in Snowflake |
| Streaming output | First-class (token streams, generators) | Supported via st.write_stream |
| Multi-page apps | Tabs and Blocks composition | First-class multi-page routing |
| Ecosystem fit | Hugging Face, Transformers, Diffusers | Snowflake, pandas, Plotly, Altair |
| Performance model | Function-call per event | Whole script re-runs; cache with decorators |
Pricing comparison
Both are free and open source. The cost question is really about hosting.
Gradio
- Library: Free, MIT-style license.
- Hugging Face Spaces: Free CPU tier with unlimited public Spaces. Paid GPU instances start around $0.60/hr for a T4 and scale up; persistent storage is extra.
- Private Spaces: Included with the HF Pro plan ($9/mo per user).
Streamlit
- Library: Free, Apache 2.0.
- Community Cloud: Free for public apps connected to a GitHub repo. Resource limits are modest — fine for demos, tight for real workloads.
- Streamlit in Snowflake: Custom pricing, billed via Snowflake compute. Real cost depends on your warehouse size and uptime — budget for it like any Snowflake workload, not like a SaaS seat.
If you don't already pay for Snowflake, Streamlit's commercial story is awkward. If you do, it's the most natural way to put a UI on internal data. Gradio's hosting story is cleaner for indie builders: HF Spaces is the obvious answer and it's free for public work.
Use case scenarios
Pick Gradio if you're shipping a model demo
A user uploads an image, you return a classification. A user types a prompt, you stream tokens back. A user records audio, you transcribe it. This is Gradio's sweet spot. The Interface API is literally gr.Interface(fn, inputs, outputs) — your model is the app. Sharing on Hugging Face Spaces takes one push, and the audience that uses HF is exactly the audience that wants to try ML demos.
Pick Streamlit if you're building a data app
Multiple charts, filters that affect the whole page, a sidebar of controls, a story the user reads top to bottom. Streamlit's rerun model fits this naturally — change a date filter, the whole page recomputes. st.cache_data and st.cache_resource keep it fast. The widget library leans toward pandas and Plotly, which is what most internal data tools need.
Pick Gradio for chat interfaces and streaming
gr.ChatInterface gives you a ChatGPT-style UI in about 10 lines, with streaming, history, and retry built in. Streamlit can do chat (st.chat_message, st.chat_input) but you'll write more glue code, and the rerun model fights you on streaming state.
Pick Streamlit for internal dashboards and tools
If the deliverable is "a thing my team uses every Monday morning," Streamlit wins. Multi-page apps, session state, and the dashboard idiom are first-class. Auth via Community Cloud or Snowflake covers most internal needs without standing up a reverse proxy.
Pick Gradio if you're deep in the Hugging Face ecosystem
Transformers, Diffusers, datasets, Spaces — they all assume Gradio. Free GPU on a public Space is genuinely useful, and the integration is tight enough that you'll feel friction trying to use anything else.
Pick Streamlit if you need real layout control
Both frameworks are limited compared to a real frontend, but Streamlit's columns, containers, and sidebars give you more layout vocabulary out of the box. Gradio's Blocks API closes the gap but feels less ergonomic for non-model UIs.
Verdict
Neither tool is better. They're answering different questions.
Ship a model demo, especially one you want strangers to try: Gradio. The Interface API, ChatInterface, and Hugging Face Spaces are the fastest path from model.predict() to a public URL someone can use.
Build an internal data tool or dashboard your team will use repeatedly: Streamlit. The script-rerun model, session state, multi-page routing, and pandas-first widgets fit the dashboard shape better, and Snowflake integration is a real edge if you already live there.
Building a chatbot or streaming LLM UI: Gradio, by a wide margin. ChatInterface saves you a day of work.
Prototyping and you genuinely don't know yet: start with whichever you've used before. Both let you throw the prototype away cheaply once you know what you're actually building. The mistake isn't picking the wrong one — it's pretending you have to commit before you have a real app.