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Data & Analytics

Streamlit

Streamlit is an open-source Python framework designed for data scientists and AI/ML engineers to create interactive data applications with minimal code. Developers write Python scripts enhanced with Streamlit commands and run them using a single command, which launches a local server and opens the app in a web browser. The framework supports displaying data frames, charts, maps, text, and interactive widgets such as sliders, buttons, checkboxes, and selectboxes. Streamlit apps rerun the entire script from top to bottom upon user interactions to update the display dynamically. The framework includes features like sidebar layouts for control widgets, multipage app support through page definitions and navigation components, and theming options including Light, Dark, and custom themes configurable via a configuration file. Streamlit is distributed as open-source software, with no pricing details publicly provided.

Updated Jan 6, 2026open-source
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Overview

Streamlit enables data scientists and AI/ML engineers to build interactive data apps in Python with minimal setup.

Pricing
open-source
Category
Data & Analytics
Company
Visual Guide
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Key Features
01
Includes sliders, buttons, checkboxes, selectboxes, and components for data visualizations, input forms, and custom HTML.
02
Instant app updates occur as code changes are made, facilitating rapid development.
03
Supports multipage applications by defining pages with `st.Page` and navigation using `st.navigation` in an entry point script.
04
Offers Light, Dark, and custom themes configurable via the `config.toml` file, matching user OS or browser preferences.
05
Allows pinning of widgets on the left side of the app using `st.sidebar` for better control placement.
Real-World Use Cases

Interactive Data Visualization

Data scientists can create apps that display data frames, charts, and maps with interactive widgets to explore datasets.

Rapid Prototyping of AI/ML Models

AI/ML engineers can build interfaces to test and demonstrate machine learning models with live input controls.

Quick Start
1
Install Streamlit
Install Streamlit using pip.
2
Add Streamlit Commands
Add Streamlit commands such as import streamlit as st and use elements like st.write or widgets in your Python script.
3
Run the Script
Run the script with streamlit run your_script.py to start a local server and open the app in a browser.
4
Interact with Widgets
Use widgets in the app; the script reruns top-to-bottom on changes to update the display.
5
Create Multipage Apps
Organize your app into multiple pages by creating page files and an entry point script using st.Page and st.navigation.
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Strategic Context for Streamlit

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Pricing
Model: open-source

Streamlit is open-source with no publicly available pricing information.

Assessment
Strengths
  • Apps run via a single command after adding Streamlit commands to a Python script.
  • Supports built-in widgets, charts, data frames, and maps without additional setup.
  • Provides sidebar for controls and multipage structure for larger apps.
  • Includes theming options like Light, Dark, and custom configurations.
Limitations
  • The entire script reruns from top to bottom on every widget interaction or screen update.
  • Limited details available on deployment options beyond built-in support for platforms like Heroku, AWS, and Google Cloud.