Getting Started with Streamlit for Data Science

Chapter 8: Deploying Streamlit Apps with Heroku and AWS

In Chapter 5, Deploying Streamlit with Streamlit Sharing, we learned how to deploy our Streamlit applications with Streamlit Sharing. Streamlit Sharing is quick, easy, and very effective for most applications but has a few downsides, mainly that we are limited by only being able to host three free applications at once and that we also are limited in the computational power at hand. The following excerpt is from the Streamlit Sharing page:

Apps get up to 1 CPU, 800 MB of RAM, and 800 MB of dedicated storage in a shared execution environment.

If you are in a situation where you want to deploy more than three applications at a time, or you want more compute as you run, for example, more complex ...