Both are open-source Python frameworks for building data apps without writing JavaScript, and both draw excellent charts. They differ in one design decision — how the app reacts when a user changes something — and almost every practical difference follows from it.
The one difference that matters
Streamlit reruns your script.
You write an ordinary Python script, top to bottom. When a user moves a slider, Streamlit runs
the whole script again with the new value. Caching (st.cache_data) stops the slow
parts recalculating, and st.session_state keeps values between runs. It is the
simplest mental model there is, which is why a working app can take an afternoon.
Dash wires up callbacks. Built by Plotly on top of Flask, React and Plotly.js, a Dash app declares a layout and then functions that connect specific inputs to specific outputs. When a dropdown changes, only the callbacks that depend on it run, and only their components update. More code up front, more control afterwards.
Side by side
| Streamlit | Dash | |
|---|---|---|
| How it updates | Reruns the script | Callbacks update chosen components |
| Time to a first app | Hours | Days |
| Layout control | Columns, tabs, sidebar — opinionated | Full control of the page structure |
| Complex interactions | Possible, gets awkward | What it is designed for |
| Charts | Plotly, Altair, Matplotlib and more | Plotly, deeply integrated |
| Runs as | Its own server | A Flask application |
| Commercial offering | Part of Snowflake | Dash Enterprise from Plotly |
When Streamlit is the right answer
- An internal tool for a handful of people, where speed of delivery beats polish.
- A front end for an existing analysis in pandas, NumPy or SciPy.
- A prototype to find out which charts and filters people actually use, before investing in more.
When Dash is the right answer
- A dashboard customers will log into, with your branding and a precise layout.
- Interactions that chain — selecting a point on one chart filters three others.
- An app that needs to sit inside an existing Flask application, its authentication and its deployment pipeline.
- Many concurrent users, where rerunning a whole script per interaction would waste work.
And when neither is
If your team already lives in a BI tool and the job is standard reporting on a data warehouse, a dedicated BI product may be the simpler choice. Python dashboards win when the analysis itself is custom — statistics, forecasting logic, data cleaning — or when the dashboard has to be embedded in your own software.
A common path is to start in Streamlit to learn what people need, then rebuild in Dash once the shape is known. The analysis code carries over; only the interface is rewritten.
Sources
- Streamlit documentation — Streamlit
- Dash documentation — Plotly