Streamlit vs Solara: Choosing the Right Dashboard Framework

When it comes to turning your data science work into interactive dashboards, two frameworks stand out: Streamlit and Solara. Both offer compelling advantages, but which one is right for your project?

Overview

Streamlit

Streamlit has been the go-to solution for data scientists since 2019. It's Python-native, easy to learn, and has a massive community.

Solara

Solara is the newer player, built on ipywidgets and designed specifically for reactive applications. It offers more flexibility but requires a steeper learning curve.

Ease of Use

Streamlit: Beginner-Friendly

import streamlit as st
import pandas as pd

st.title("My Dashboard")
df = pd.read_csv("data.csv")
st.dataframe(df)
st.plotly_chart(fig)

Pros:

• Minimal learning curve
• Intuitive API
• Excellent documentation
• Large community

Cons:

• Less control over layout
• Limited customization options
• Can be slow for complex apps

Solara: More Powerful, More Complex

import solara
import pandas as df

@solara.component
def Dashboard():
    df = pd.read_csv("data.csv")
    
    with solara.Column():
        solara.Markdown("# My Dashboard")
        solara.DataFrame(df)
        solara.PlotlyChart(fig)

Pros:

• Reactive programming model
• Better performance for complex apps
• More layout control
• Native Jupyter integration

Cons:

• Steeper learning curve
• Smaller community
• Fewer third-party components
• Documentation still developing

Performance Comparison

Streamlit Performance

• Reruns entire script on each interaction
• Can be slow for large datasets
• Caching helps but requires careful implementation
• Not ideal for real-time applications

Solara Performance

• Reactive updates only affected components
• Better handling of large datasets
• Built-in state management
• More suitable for real-time dashboards

Use Case Recommendations

Choose Streamlit When:

• Building quick prototypes - Get up and running in minutes
• Team has limited frontend experience - Pure Python, no HTML/CSS/JS
• Simple dashboards - Basic charts, tables, and controls
• Rapid iteration - Quick changes and deployments
• Large community support needed - More tutorials, examples, and help

Choose Solara When:

• Complex interactive applications - Multiple interconnected components
• Performance is critical - Large datasets or real-time updates
• Already using Jupyter - Natural integration with existing workflows
• Advanced layouts required - Custom positioning and responsive design
• Reactive programming experience - Team comfortable with modern frameworks

Feature Comparison

Feature	Streamlit	Solara
Learning Curve	Easy	Moderate
Performance	Good	Excellent
Layout Control	Limited	Extensive
Community	Large	Growing
Documentation	Excellent	Good
Jupyter Integration	Basic	Native
Deployment	Easy	Moderate
Customization	Limited	Extensive

Real-World Examples

Streamlit Success Stories

• Financial dashboards for investment firms
• ML model monitoring for tech companies
• Sales analytics for e-commerce businesses
• Research visualizations in academia

Solara Success Stories

• Real-time trading dashboards for financial firms
• Scientific simulations with interactive controls
• Complex data exploration tools for research
• Multi-page applications with state persistence

Migration Considerations

From Streamlit to Solara

# Streamlit version
import streamlit as st
value = st.slider("Select value", 0, 100, 50)
st.write(f"Value: {value}")

# Solara equivalent
import solara

@solara.component
def App():
    value, set_value = solara.use_state(50)
    solara.SliderInt("Select value", value, on_value=set_value, min=0, max=100)
    solara.Markdown(f"Value: {value}")

Key Migration Steps

1. Understand reactive patterns - Learn component-based thinking
2. Refactor state management - Use Solara's reactive state
3. Update deployment - Adjust hosting configuration
4. Test thoroughly - Verify all functionality works correctly

Auto Dashboards Support

Both frameworks are supported by Auto Dashboards, making the choice less critical for getting started:

• Streamlit export - Great for quick sharing and simple dashboards
• Solara export - Better for complex, performance-critical applications
• Easy switching - Try both and see which fits your needs

Conclusion

Both Streamlit and Solara are excellent choices, but for different scenarios:

Choose Streamlit if you want to get started quickly, have a simple dashboard, and prefer a large community.

Choose Solara if you need advanced interactivity, better performance, or are already working heavily in Jupyter.

The good news? With Auto Dashboards, you don't have to choose immediately. Export to both frameworks and see which one better serves your specific use case.

Getting Started

1. Try both frameworks with simple examples
2. Consider your team's skills and learning capacity
3. Evaluate your performance requirements
4. Start with Auto Dashboards to quickly test both options
5. Iterate and refine based on user feedback

The future of dashboard development is bright with both frameworks continuing to evolve and improve.