Shiny vs Streamlit

Shiny vs Streamlit compariosn

Trying to decide what Python analytics framework is the right one for your project? Hopefully this comparison of Shiny vs Streamlit will help you make an informed decision. 

Shiny vs Streamlit - TLDR


  • Pros: While still being relatively new for python, Shiny for R has been around for a long time, so that a lot of experiences on that could be put into Shiny for python. Sharing similar concepts should also help R users to migrate. The core of shiny is a reactive programming engine, trying to reduce the required computations as much as possible.
  • Cons: Shiny uses Bootstrap as its framework for layout and styling - this means you must have understood the concepts of Bootstrap in order to change the UI.
  • Use (Shiny) if: You don't have time for web development but want to expose your python code as fast as possible.
  • Alternatives: Dash. Panel.


  • Pros: Streamlit is well suited for fast prototyping. On top of being easy to understand and having a good documentation to get started, the real-time feedback when changing code allows for a fast turnaround. If you do not need a fully scalable application running with thousands of users, then putting a Streamlit application in production should not be a problem.
  • Cons: The biggest strength of fast prototyping is also the biggest weakness, as customization is limited. The look and feel can only be customized to a certain degree.
  • Use (Streamlit) if: you want a fast way of building applications and visualizations for your users, for example for a small internal data app.
  • Alternatives: Dash and Shiny

Firebolt Streamlit Example:

Compatibility with Firebolt

You can use Firebolt with both Strealit and Dash in order to build your applications.

Include the Python SDK for Firebolt to your dependencies and analyze your data stored in Firebolt right away!

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