---
title: "Streamlit vs. NiceGUI: Choose the Best Python Web Framework"
description: "Compare Streamlit vs NiceGUI for Python web apps: architecture, customization, real-time behavior, and which framework fits your use case."
date: 2026-05-04
categories: ["vps"]
tags: ["streamlit","nicegui","python"]
---

Two Python frameworks make building web apps straightforward: [Streamlit](https://streamlit.io/) and [NiceGUI](https://nicegui.io/). Both let you create interactive web apps with mostly Python code, but they work better for different use cases.

[Streamlit](https://streamlit.io/) targets data applications: dashboards, data exploration, and ML demos. You write a Python script and Streamlit renders it as a web app with built-in components for charts and tables.

[NiceGUI](https://nicegui.io/) works better for application-style UIs like forms, admin panels, and monitoring tools. It's built on FastAPI with a web UI layer, giving you more control over events, state, and backend features.

## Quick Comparison Overview

| Feature | Streamlit | NiceGUI |
|---------|-----------|---------|
| Best For | Data apps, dashboards | General web apps, desktop-like UIs |
| Learning Curve | Very easy | Easy |
| Customization | Limited | High |
| Backend | Built-in | FastAPI |
| Real-time Updates | Automatic reruns | Event-driven |

> For more Python web frameworks, see: [Best Python Web Frameworks](https://www.bitdoze.com/best-python-web-frameworks/)

## Key Differences Between Streamlit and NiceGUI

### 1. Primary Purpose
- Streamlit: Built specifically for data science, ML, and analytics dashboards
- NiceGUI: Designed for general-purpose web applications with desktop-like interfaces

### 2. Development Approach
- Streamlit: Script-based - runs from top to bottom on each interaction
- NiceGUI: Event-driven - responds to specific user actions

### 3. Customization Level
- Streamlit: Limited customization, focuses on rapid prototyping
- NiceGUI: Highly customizable with direct access to HTML/CSS/JS when needed

### 4. Backend Architecture
- Streamlit: Built-in server runtime with session/state patterns
- NiceGUI: FastAPI-based backend with more explicit control over routing and integration

### 5. Real-time Features
- Streamlit: Interaction triggers a script rerun; you manage state/caching to keep it responsive
- NiceGUI: Event-driven updates; good fit for live UI patterns (monitoring/control panels)

### 6. Learning Curve
- Streamlit: Extremely beginner-friendly, no web dev knowledge needed
- NiceGUI: Slightly steeper but still accessible, benefits from web dev basics

### 7. Community & Resources
- Streamlit: Large community, extensive documentation, Streamlit Cloud hosting
- NiceGUI: Growing community, good documentation, self-hosting focused

### 8. License & Cost
- Streamlit: Apache 2.0, free with Streamlit Community Cloud (with limitations)
- NiceGUI: MIT License, completely free and open source

## When to Choose Each Framework

### Choose Streamlit if you need:
- Data dashboards with charts, tables, and visualizations
- ML model interfaces for demos and prototypes
- Rapid development with minimal coding
- Built-in data handling (CSV, JSON, databases)
- Easy deployment on Streamlit Cloud

Example use cases:
- Sales analytics dashboard
- Machine learning model demo
- Financial data explorer
- Research data visualization

### Choose NiceGUI if you need:
- Desktop-like applications in the browser
- Real-time interactions without page reloads
- Custom UI components and layouts
- Advanced backend features (authentication, APIs)
- Full control over user experience

Example use cases:
- Project management tools
- IoT device controllers
- Interactive forms and surveys
- Real-time monitoring systems

## Getting Started: Code Examples

Both frameworks are beginner-friendly, but Streamlit has a slight edge for absolute beginners.

### Streamlit Example: Data Dashboard

```python
import streamlit as st
import pandas as pd

st.title('Sales Dashboard')

data = pd.DataFrame({
    'Month': ['Jan', 'Feb', 'Mar', 'Apr'],
    'Sales': [100, 150, 120, 200],
})

month_filter = st.selectbox('Select Month', data['Month'])
filtered = data[data['Month'] == month_filter]

st.bar_chart(data.set_index('Month'))
st.write(f'Sales for {month_filter}: ${int(filtered["Sales"].iloc[0])}')
```

### NiceGUI Example: Interactive Form

```python
from nicegui import ui

def handle_submit():
    ui.notify(f'Hello {name.value}! You are {age.value} years old.')

ui.label('User Information Form')

with ui.row():
    name = ui.input('Name', placeholder='Enter your name')
    age = ui.number('Age', value=25, min=0, max=120)

ui.button('Submit', on_click=handle_submit)

ui.run()
```

### Key Differences in the Code

- Streamlit: Linear, script-like flow - perfect for data workflows
- NiceGUI: Component-based with explicit event handling - better for interactive apps

## NiceGUI Advantages

### Real-Time Interactions
- Event/callback-driven UI updates
- Good fit for monitoring, controls, and app-like UIs

### FastAPI Integration
- You can use FastAPI concepts (routing, dependencies, middleware) as your app grows
- Useful when you need APIs alongside the UI, or want more control over auth and request handling

Note: Built-in authentication and user management isn't automatic in FastAPI itself. You typically implement auth using FastAPI patterns and libraries, then integrate it with your app.

### Advanced Customization
- More layout and component control than Streamlit
- Can integrate with web concepts when needed (styling and custom behavior)

### Development Experience
- Good local dev workflow and straightforward self-hosting
- Works well in containers when you need repeatable deployment

### Flexibility
- Natural fit for event-driven apps
- Can integrate with existing Python services and libraries

## Streamlit Advantages

### Data Apps First
- Excellent defaults for pandas DataFrames, charts, and common analytics workflows
- Great for ML demos and internal dashboards

### Ultra-Simple Development Model
- Very low web framework overhead: write Python top-to-bottom
- Easy to iterate quickly and share prototypes with teammates

### Deployment Options
- Streamlit Community Cloud is convenient for quick publishing
- Also deployable to your own infrastructure (VPS, containers, etc.)

### Data-Focused Widgets
- Strong widget set for filters, inputs, and interactive exploration
- Built-in caching and session state patterns help keep apps responsive

### Strong Ecosystem
- Large community, lots of examples and integrations

## Making Your Choice: Decision Guide

### Choose Streamlit if:
- You're building data dashboards or ML demos
- You want rapid prototyping with minimal code
- You're new to web development
- You need built-in data visualization
- You want easy deployment and sharing

### Choose NiceGUI if:
- You need real-time interactions without page reloads
- You're building general-purpose web apps
- You want desktop-like UI in the browser
- You need advanced backend features
- You want full customization control

## Quick Start Resources

Streamlit:
- [Official Documentation](https://docs.streamlit.io/)
- [30 Days of Streamlit Challenge](https://30days.streamlit.app/)
- [Deploy Streamlit on VPS](https://www.bitdoze.com/streamlit-deploy-vps-cloudflare/)

NiceGUI:
- [Official Documentation](https://nicegui.io/)
- [NiceGUI for Beginners](https://www.bitdoze.com/nicegui-get-started/)
- [GitHub Examples](https://github.com/zauberzeug/nicegui/tree/main/examples)

## What's New in 2026

Both frameworks have evolved since this comparison was first written:

**Streamlit** has continued to refine its data app workflow. Recent releases improved multi-page app support, added better theming controls, and enhanced performance for large DataFrames. Streamlit Community Cloud remains the easiest way to deploy a data app.

**NiceGUI** shipped its 2.0 release with updated dependencies and has been steadily adding features — better TypeScript support, improved Tailwind CSS integration, and more native components. The community grew significantly through 2025, and self-hosting remains the primary deployment model.

**New alternatives worth watching:**
- **Mesop** — Google's Python UI framework that compiles to web components. Similar idea to Streamlit but with a different rendering model.
- **Reflex** (formerly Pynecone) — full-stack Python framework that compiles to Next.js. More like building a traditional web app, just in Python.
- **Gradio 5** — refreshed UI with better customization, competing more directly with Streamlit for ML demos.

The landscape is more crowded now, but Streamlit and NiceGUI still hold their positions well — Streamlit for data, NiceGUI for applications.

## Final Verdict

Both frameworks excel in their domains. Streamlit dominates data science applications with its simplicity and built-in data tools. NiceGUI shines for interactive web applications that need desktop-like functionality.

Pick the framework that matches your project requirements, not which one is objectively "better." Start with what fits your immediate needs - you can explore the other later as your projects evolve.