Panel vs Streamlit compared

Panel vs Streamlit compariosn

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

Panel vs Streamlit - TLDR


  • Pros: Panel allows you to turn jupyter notebooks into dashboards without much additional work. Due to the omnipresence of notebooks this is an easy way to get started fast with a shared dashboard. Also there is built-in support for the many common graph libraries and widgets. You can integrate your panel app into web application frameworks like Django or FastAPI and also use built-in OAuth integration. Panel also supports caching of results on the server side to speed up rendering dashboards.
  • Cons: The learning curve of Panel is slightly higher than other dashboarding tools, because of  the sheer choice of widgets and libraries that are supported and need to be learned as well. The focus of Panel is more on python and less on HTML/CSS.
  • Use (Panel) if: you want to be flexible about existing widgets and libraries that are supported out of the box and want to make a Jupyter dashboard available for everyone as a web application.
  • Alternatives: Dash. Jupyter.


  • 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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