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Brief
Streamlit was acquired by Snowflake.

Streamlit

Data Analytics & Developer T

Streamlit enables data scientists and engineers to build and share interactive web apps using only Python, with no front-end expertise required. Its tight integration with Snowflake makes it a powerful tool for secure, enterprise-grade data application development.

Last updated May 11, 2026 by ATDb automated enrichment · Connections updated May 11, 2026

Founded
2018
HQ
San Francisco, California, United States
Parent
Connections
9

At a glance

Employees
51-200
Funding
~$21.7M (pre-acquisition)
8integrations1corporate family

About

Leading open-source Python framework for building data and ML web applications, now embedded within Snowflake's Data Cloud platform

Streamlit is an open-source Python framework designed to enable data scientists, machine learning engineers, and developers to rapidly build and deploy interactive web applications directly from Python scripts. Founded in 2018, Streamlit gained massive traction in the data science community by dramatically lowering the barrier to creating shareable, interactive data apps — eliminating the need for front-end web development skills. Its intuitive API allows users to turn data scripts into fully functional web apps in minutes, making it a go-to tool for prototyping ML models, building internal dashboards, and sharing analytical insights. Snowflake acquired Streamlit in March 2022 for approximately $800 million, integrating it into the Snowflake Data Cloud ecosystem. As a Snowflake subsidiary, Streamlit continues to operate as a distinct product and brand, now deeply integrated with Snowflake's platform through Streamlit in Snowflake (SiS), which allows users to build and run Streamlit apps natively within the Snowflake environment without data ever leaving the platform. This integration significantly expanded Streamlit's enterprise reach and security posture. While Streamlit is not an AdTech-native company, it is widely used within the AdTech and marketing analytics ecosystem for building internal reporting dashboards, campaign performance visualizers, audience segmentation tools, and ML model demos. Its open-source community remains highly active, and the product continues to evolve under Snowflake's stewardship, maintaining its free, open-source core alongside enterprise-grade deployment options.

Business model

Open-Source / SaaS

Target market

Enterprise, Mid-Market, SMB, Individual Developers

What they offer

  • Streamlit Open Source

    Free, open-source Python library for building interactive web applications from data scripts with minimal code

  • Streamlit Community Cloud

    Free hosting platform for deploying and sharing Streamlit apps publicly, targeted at individual developers and open-source projects

  • Streamlit in Snowflake (SiS)

    Native integration allowing users to build and run Streamlit apps directly within the Snowflake Data Cloud, with data never leaving the platform

  • Streamlit Widgets

    Pre-built interactive UI components (sliders, dropdowns, file uploaders, etc.) that can be added to apps with a single line of Python

  • st.cache / st.cache_data

    Built-in caching mechanisms to optimize app performance by storing computation results and data fetches

Key features

Pure Python app development — no HTML, CSS, or JavaScript requiredHot-reloading for instant app updates during developmentNative Snowflake integration via Streamlit in SnowflakeRich widget library for interactive UI componentsBuilt-in support for popular data science libraries (Pandas, Plotly, Altair, etc.)One-click deployment via Streamlit Community CloudSession state management for stateful app interactionsMulti-page app supportCustom theming and layout options

Use cases

Building interactive ML model demos and prototypesInternal analytics and KPI dashboardsCampaign performance reporting tools in AdTechAudience segmentation and exploration interfacesData quality monitoring applicationsReal-time data visualization for business stakeholdersRapid prototyping of data productsSharing Jupyter-notebook-style analyses as interactive apps

Customer segments

Data scientists and ML engineersAnalytics and BI teamsSoftware developers building internal toolsAcademic researchersSnowflake enterprise customersAdTech and MarTech analytics teamsStartups and individual developers

Tech & specs

Technology stack

PythonReact (frontend runtime)Tornado (web server)WebSocketsSnowflake (native integration)GitHub (Community Cloud deployment)Docker (self-hosted deployments)

Security & compliance

SOC 2 (via Snowflake)GDPRHIPAA (via Snowflake)Data residency controls via Snowflake

Deployment

CloudOn-premiseHybridSnowflake-native (Streamlit in Snowflake)

API

Yes

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