Streamlit is an open‑source Python library that turns a single script into an interactive web app without requiring HTML, CSS, or JavaScript. It offers declarative UI functions, automatic reloading, and native integration with data‑science tools such as pandas, NumPy, Matplotlib, and Plotly, allowing data scientists and ML engineers to prototype and share visualizations and model demos quickly. Apps can be run locally or deployed via Streamlit Community Cloud or standard cloud platforms.
Funding
$35M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Data scientists and machine learning engineers often need to share interactive visualizations, model demos, or data exploration tools, but building such web applications typically requires front‑end development skills and separate deployment pipelines, slowing down the iteration cycle.
Solution
Streamlit is an open‑source Python library that enables users to turn a single Python script into a fully functional, interactive web app without writing any HTML, CSS, or JavaScript. By exposing a concise set of UI primitives (e.g., sliders, charts, file uploaders) as Python functions, it lets developers focus on data logic while the framework handles rendering, state management, and live reloading. The library integrates natively with popular data‑science stacks such as pandas, NumPy, Matplotlib, Plotly, and PyTorch, allowing seamless embedding of charts, tables, and model outputs. Streamlit apps can be run locally for rapid prototyping or deployed to cloud platforms and Streamlit Community Cloud for broader sharing, providing a low‑friction path from prototype to production‑ready demo.
Target Audience
Primary users are data scientists, machine‑learning engineers, analysts, and research teams who need to prototype and share interactive data applications without front‑end development expertise.
Features
- Declarative UI API (`st.title`, `st.slider`, `st.plotly_chart`, etc.) that maps directly to Python code, eliminating front‑end boilerplate
- Automatic script re‑execution on source changes, delivering instant live updates during development
- Built‑in caching decorator (`@st.cache`) to memoize expensive computations and improve performance
- Native support for major visualization libraries (Matplotlib, Altair, Plotly, Vega‑Lite) and data frames, enabling rich interactive charts and tables
- Theme customization and layout primitives (columns, containers, sidebars) for responsive app design
- Simple deployment options: local server, Docker container, or Streamlit Community Cloud with one‑click sharing
- Extensible component framework that allows embedding custom React or JavaScript widgets as reusable Streamlit components
- Open‑source MIT license with an active community contributing plugins, templates, and integrations