Reflex vs Streamlit
The Python Framework for Apps That Outgrow Streamlit
Streamlit is great for a quick dashboard. Reflex is the framework Streamlit users move to when the app needs to ship to production. Same Python, a real full-stack web app underneath.
Migrating our cybersecurity app from Streamlit to Reflex has been excellent. We quickly built a unified interface connecting BigQuery, Salesforce, and PagerDuty for our 15+ team members. The ease of use and rapid development, supported by your responsive team, made it a great experience.
We all have a machine learning background - none of us are front-end engineers. We initially tried Streamlit, but Streamlit re-renders the page at every interaction; it performs poorly. So Reflex was a good find.
Reflex vs Streamlit
Same Python.
A real framework
underneath.
Streamlit is built for quick prototypes. Reflex is built to be the production app. Here is the side-by-side.
- State lives in a Python class between interactions. Components subscribe to the data they read.
- Only the components affected by a change re-render. A slider change won't re-run your database query.
- Long-running background events stream results to the UI as work progresses.
- RUNNING...
- Streamlit reruns the entire script on every widget interaction. Every database query and dataframe transform runs again unless you wrap it in a cache decorator.
- A user who clicks a button that kicks off a 30-second computation watches a frozen UI for 30 seconds. There is no first-class pattern for background work that streams results back.
- Caching is the answer to performance, and it puts correctness on you. You decide what to cache, when it invalidates, and how to keep it fresh as the code changes.
- WebSocket sync pushes server state to the browser as it changes. Live data feeds and streaming model output work without polling.
- An async Starlette backend serves many concurrent users from one process without re-running a script per interaction.
- Background tasks update component state from a worker. The user sees the update the moment it happens.
- Streamlit cannot push to the browser. The page only updates when the user clicks something.
- Streamlit holds per-session state in the server's memory for every active user. Ten concurrent users running a dashboard over a 500 MB dataframe pins around 5 GB in server memory, kept resident until the sessions end.
- Live updates are usually faked with st.rerun() loops or community components.
- URL-based routing with nested layouts, dynamic segments, and server-rendered pages.
- A State class instance follows the user across pages. Global state is the default, not a workaround.
- Type-annotated route params plumb directly into your State class.
- Streamlit's multipage support is a `pages/` directory of separate scripts. There are no dynamic routes, no nested layouts, and no shared state across pages.
- Sharing a deep link to a specific filtered view is a workaround.
- Wrap any React component from npm directly in Python. The Python code stays Python.
- Tailwind classes and CSS props on every component. Pixel-precise UI without leaving the file.
- 60+ built-in components with a modern look and feel.
- Streamlit's customization ceiling arrives quickly. Layout is mostly vertical. Theming is shallow.
- The common escape hatch is `st.markdown(..., unsafe_allow_html=True)`. The real escape hatch is to write a Streamlit Component in React and TypeScript.
- Responsive multi-column layouts tend to be fragile.
- ORM and database migrations are part of the framework. Define a model, generate migrations, deploy.
- File uploads are first-class. Drag-and-drop, async handlers, no separate upload service.
- Background tasks are first-class. Run long jobs and push updates to the UI as they progress.
- Streamlit has no ORM. `st.connection` is a connection wrapper, not a model layer.
- No first-class background job system. Streamlit officially doesn't support multithreading in app code.
- Large file uploads aren't a first-class workflow. `st.file_uploader` reads files into memory.
- Compiles to a Starlette backend with a static React frontend. Containerizes cleanly for any platform.
- One-command deploys to Reflex Cloud or GCP.
- Granian serves the backend with native HTTP/2, WebSocket, and async ASGI.
- Per-session state plus large in-memory dataframes cause out-of-memory failures under modest concurrent load.
- Streamlit Community Cloud is fine for demos. It does not cover the access controls, secrets management, and deployment surface area a business app needs.
- Hosting choices fork between Streamlit Community Cloud (free, limited) and Snowflake (paid, vendor-locked). No in-between managed option.
Why Reflex
Everything Streamlit
leaves you to assemble
Reflex is the framework Streamlit users move to when the app needs more than a script. Real state, real routing, and production essentials built into the framework.
State lives in a Python class. Components update only when the data they read changes.
WebSocket sync pushes server state to the browser. Live dashboards and streaming model output work out of the box.
URL routing with dynamic segments, shared layouts, and global state across pages. Deep links and typed route params are standard features.
Wrap any React component from npm in Python. Tailwind classes and CSS props on every element.
ORM, migrations, file uploads, and background tasks ship with the framework.
Compiles to a Starlette backend with a static React frontend. One-command deploys to Reflex Cloud or GCP.
