Streamlit vs Dash: Which Framework is Right for You in 2025?

Name

Olaf Källström

Updated on 11/15/2025

If you're building data apps, AI-powered interactive dashboards, or rapid prototypes, chances are you’ve come across Streamlit and Plotly Dash. Both are popular open-source Python frameworks, but they serve slightly different audiences and use cases.

This updated 2025 guide compares Streamlit and Dash across features, performance, ecosystem, enterprise needs, and real-world scenarios—helping you choose the right tool for your next project.

🛠️ Want to add a Tableau-like UI to your Streamlit app?

PyGWalker (opens in a new tab) embeds a no-code visual analytics interface directly inside Streamlit.

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Understanding Streamlit and Dash

⭐ What is Streamlit?

Streamlit is a lightweight Python framework that turns scripts into shareable apps—fast.
It focuses on developer experience, simple syntax, and zero front-end work.

Key reasons people choose Streamlit:

• Extremely fast to build and iterate
• Minimal boilerplate
• Ideal for data exploration, prototypes, internal tools
• Strong support for AI/LLM-based apps (2024–2025 updates)

Want to recreate the visualization app above? Follow our step-by-step guide here.

⭐ What is Dash?

Plotly Dash is a more traditional web-app development framework built on:

• Flask (backend)
• React.js (frontend)
• Plotly.js (charts)

Dash is designed for:

• Enterprise-grade dashboards
• Highly customizable layouts
• Complex interactions via callbacks
• Embedded analytics in products

Streamlit vs Dash: A Detailed Comparison (2025)

Below is an updated, more realistic comparison table that reflects the current state of both tools:

Streamlit vs Dash: Feature Comparison

Streamlit — Focus on Simplicity

Streamlit excels when you want minimal friction:

• Built-in widgets: sliders, selectors, sidebars
• Hot-reloading for ultra-fast iteration
• Multipage apps (added recently)
• Native chat elements for LLM applications
• “App = Python script” development style

Dash — Focus on Control

Dash is the better fit when you need complete control:

• Complex, multi-layered layouts
• Callback-driven application architecture
• Native Plotly.js power
• Fully customizable CSS + HTML + React.js components
• Production-ready enterprise tooling (Dash Enterprise)

Streamlit vs Dash: Rapid Prototyping

For prototyping, Streamlit is unmatched.

• One file = working app
• No callbacks or MVC structure needed
• Easy integration with ML models, embeddings, LLMs
• Popular among data scientists for quick internal demos

Dash is still quick, but:

• Requires structure
• More boilerplate
• Callbacks can get large as the app grows

Winner: Streamlit

Streamlit vs Dash: Enterprise & Production Use

This is where Dash shines.

Dash advantages:

• True MVC architecture
• Better long-term maintainability
• Rich deployment options (Dash Enterprise)
• Authentication, SSO, RBAC
• Advanced visualization components

Streamlit enterprise use cases are growing, but:

• Limited structure for large codebases
• Fewer out-of-the-box enterprise controls
• Deployment is simpler but not as flexible

Winner: Dash

Streamlit vs Dash: User & Developer Experience

Streamlit:

• Much friendlier for beginners
• Simple mental model
• No web dev knowledge needed
• Great documentation

Dash:

• More concepts to learn
• Callbacks require planning
• But better for developers familiar with web frameworks

Winner: Streamlit for UX; Dash for engineering teams

Streamlit vs Dash: Structure & Adaptability

Dash uses:

• MVC architecture
• Clear separation of layout, callbacks, logic
• Easier to scale to 100+ components

Streamlit uses:

• Script-based execution
• Great flexibility
• But can become messy without discipline

Building a Simple App in Both Frameworks

⭐ Streamlit Code Example

import streamlit as st
import plotly.graph_objects as go
 
fig = go.Figure(data=go.Scatter(x=[1,2,3,4], y=[10,15,7,10]))
st.plotly_chart(fig)

Run with:

streamlit run app.py

⭐ Dash Code Example (Updated for Dash 2.x Syntax)

import dash
from dash import html, dcc
import plotly.graph_objects as go
 
fig = go.Figure(data=go.Scatter(x=[1,2,3,4], y=[10,15,7,10]))
 
app = dash.Dash(__name__)
app.layout = html.Div([dcc.Graph(figure=fig)])
 
if __name__ == "__main__":
    app.run_server(debug=True)

Comparing the Results

Streamlit:

• Less code
• Extremely intuitive
• Perfect for notebooks → app flow

Dash:

• More boilerplate
• But very powerful for large scale dashboards
• Better control over UI and performance

Conclusion: Should You Use Streamlit or Dash?

Use Streamlit if you want:

• Fast prototyping
• AI/LLM-powered apps
• Lightweight internal tools
• Simple dashboards
• Minimal code and maximum speed

Use Dash if you need:

• Enterprise-grade dashboards
• Highly interactive, deeply customized UI
• Complex callback logic
• Production apps with maintainable architecture

Either can produce excellent results—the right choice depends on your project’s depth, complexity, and long-term needs.

FAQs

1. Which is better for rapid prototyping, Streamlit or Dash? Streamlit is faster for prototyping due to its simple, script-based approach.

2. Which is better for enterprise applications? Dash offers more structure and production-ready features suitable for enterprise-scale apps.

3. Which has the larger community? Dash's community has been around longer, but Streamlit’s is growing rapidly.