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Dash vs Streamlit
Streamlit's script-rerun model ships a simple app fastest; Dash's explicit layout and callback model scales better to complex, multi-page apps.
Side by side
| Dash | Streamlit | |||
|---|---|---|---|---|
| Vendor | Plotly Inc. | Snowflake | ||
| Pricing model | Free tier + paid plans | Free tier + paid plans | ||
| Free tier | Yes | Yes | ||
| Deployment | Cloud, Self-hosted | Cloud, Self-hosted | ||
| Open source | Yes (MIT) | Yes (Apache-2.0) | ||
| Best for | Python/R teams building structured, multi-page internal analytics apps around Plotly visualizations. | Python users who want to ship a data app or dashboard quickly without front-end development. | ||
| Pricing | The Dash framework is free and open source; Dash Enterprise adds managed hosting, auth, and scaling for a custom quote. Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted. | The framework is free and open source to self-host; Community Cloud hosts public apps for free, and Streamlit in Snowflake is billed via Snowflake compute credits.
Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget. | ||
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Verdict
Both turn a Python script into a shareable web app with no front-end code, and both are free, open-source, self-hostable frameworks with a paid enterprise tier layered on top (Dash Enterprise; Streamlit in Snowflake). The difference is the execution model, and it matters more than it sounds. Streamlit reruns the entire script top to bottom whenever an input changes — trivially simple to reason about for a small app, but it can get unwieldy as an app grows into many pages and interdependent states. Dash uses an explicit layout tree with declared callbacks linking specific inputs to specific outputs, closer to how a conventional web app is structured — more setup up front, but it scales more predictably to complex, multi-page interfaces.
Dash is also natively built around Plotly's charting library, so if your visualizations are already Plotly charts, Dash is the more direct path to an interactive app around them. Streamlit is chart-library-agnostic and commonly paired with Plotly, Altair, or Matplotlib output pasted into its layout.
Choose Dash if
- The app has many pages, complex interdependent state, or will grow substantially in scope over time.
- Your charts are already built in Plotly and you want the tightest integration with that ecosystem.
- You want Dash Enterprise's app portal, SSO, and Kubernetes-based scaling for running many internal apps at once.
Choose Streamlit if
- You want the fastest path from a script to a working, shareable app, especially for a first version or a prototype.
- The app is single-page or has simple, linear interaction patterns.
- You want free public hosting via Streamlit Community Cloud, deployed straight from GitHub with one-click updates on push.
What they share
Both are Python-first (Dash also supports R and Julia), free and open source to self-host, and aimed at data scientists and analysts rather than front-end engineers. Neither requires writing HTML, CSS, or JavaScript for a standard app. If you're unsure which model fits, build the same small app in both — the difference between "rerun everything" and "declared callbacks" is easier to feel than to describe, and a day spent prototyping in each will settle it faster than reading documentation. See dashboard and data visualization.
Last reviewed September 22, 2026