Dash vs Jupyter | Python Tools Comparison

Dash vs Jupyter - TLDR

Pros: Dash allows a very customizable experience by using React under the hood. Dash easily supports interactive visualizations. There is a huge and active community willing to help out each other.
Cons: Dash is focused around its own visualization library, and may require some extended CSS and HTML knowledge for customization. As Dash also has an Enterprise version, some functionality is reserved for Enterprise only.
Use (Dash) if: You need a high level of customization to adhere to your use-case and may need enterprise features in the future.
Alternatives: Streamlit and Shiny

Pros: The most used and well known notebook tool in the python ecosystem. Every python developer will likely know about and how to use Jupyter. Many projects use a jupyter notebook for demoing. Jupyter is omnipresent in the python ecosystem. There are dozens of integrations with other programming languages as well great charting integration. Prototyping with Jupyter is as fast as it gets. Anything is at the choice of the developer, there is no limitation on charting libraries or SDKs being used. As anything can be used, it is also easy to use machine learning libraries and presentation tools on top of your data.
Cons: Not as eye-catching as other solutions. The stored data is not really easy to digest in versioning systems, which makes merging changes hard when several people work on the same notebook. Notebooks can contain sensitive information, which requires the author to ensure to read secrets from another location. It's also harder to create a web application out of a jupyter notebook than with other dashboarding tools.
Use (Jupyter) if: you need a tool that everyone knows in the python space, Jupyter is a good idea. Same for presentations and demos that many people in your teams should understand easily, as the entry barrier is low.
Alternatives: Take a look at Google Colab, Apache Zeppelin, Spyder, Voila or Panel.