Streamlit vs Gradio: Comparison for AI Demos and ML Apps
Compare Streamlit and Gradio on interactivity, ML integration, and deployment. Discover which framework best fits your AI demos and prototype applications.
Streamlit
A Python framework for building data and ML applications with pure Python. Streamlit provides a declarative API where code reload directly updates the UI. It is popular for dashboards, data exploration, and ML demos with a rich component library.
Gradio
A framework designed for ML models and AI demos. Gradio auto-generates UIs from function signatures and offers built-in hosting via Gradio Spaces. Ideal for quickly sharing model inference without much frontend code.
Comparison table
| Feature | Streamlit | Gradio |
|---|---|---|
| Focus | Data apps, dashboards, full applications | ML model demos, inference interfaces, quick prototypes |
| UI construction | Imperative — add widgets explicitly, manage state | Declarative — Interface() from function signatures |
| ML integration | Flexible — integrate any framework, custom layout | Native — automatic input/output for models |
| Hosting | Streamlit Cloud, self-host, or Vercel | Gradio Spaces (free), self-host |
| Real-time | WebSockets for live updates | Queuing for long inference, progress bars |
| Learning curve | Low — Python-only, no frontend needed | Very low — few lines for working demo |
Verdict
Streamlit is the choice for full data and ML applications with rich interactivity. Gradio wins when you want to demonstrate a model as fast as possible with minimal code. For production ML apps with multiple views, choose Streamlit. For one model, one interface, and quick sharing, choose Gradio.
Our recommendation
AVARC Solutions from Haarlem uses Streamlit for internal AI tools and client dashboards where custom flows are needed. We use Gradio for prototype demos and model evaluation with stakeholders. For production LLM apps we use Next.js and Vercel AI SDK — Streamlit and Gradio are best for internal tools and rapid POCs.
Frequently asked questions
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