optimizing-streamlit-performance

Streamlit apps rerun on every interaction, so speeding up a slow dashboard requires caching heavy computations with st.cache_data and st.cache_resource. You can also isolate reruns with st.fragment and batch UI actions with st.form to reduce latency.

Use st.cache_data when loading data like DataFrames to avoid recomputing data transformations, and use st.cache_resource for caching connections or loaded models. This distinction prevents unnecessary heavy computations during Streamlit app reruns.

The best way to isolate expensive UI components from Streamlit reruns is using st.fragment. This isolates specific reruns so only the targeted part of the app updates, preventing unnecessary recomputation across the entire dashboard.

To batch form interactions in Streamlit and improve responsiveness, use st.form. This groups multiple UI inputs into a single submission, preventing the app from rerunning on every individual widget change and reducing overall latency.

Yes, you can use conditional rendering to optimize data-heavy Streamlit apps by rendering heavy content lazily. Pre-computing expensive operations alongside this strategy ensures faster UX by only loading content when specifically needed.