Real-time, interactive web applications for Python-driven AI and Data Science.
H2O Wave is a lightweight, high-performance Python framework designed for the rapid development of real-time web applications, specifically tailored for AI and data science workflows. Unlike traditional web frameworks that require a mastery of HTML, CSS, and JavaScript, H2O Wave allows AI architects to define complex layouts and stateful interactions entirely in Python. In the 2026 landscape, H2O Wave has solidified its position as the enterprise alternative to Streamlit, offering superior handling of concurrent users and real-time streaming data via its 'waved' server-side state management. It provides a rich library of pre-built, aesthetically polished components ranging from simple buttons to complex interactive plots and Markdown editors. Its technical architecture utilizes a socket-based communication protocol, ensuring that UI updates are pushed instantly to clients without full-page reloads. For organizations, it bridges the gap between raw ML model output and production-grade decision-making tools, facilitating a seamless transition from notebook to enterprise-ready AI app within the H2O AI Cloud ecosystem.
Uses a binary protocol over WebSockets to synchronize UI state between the Python backend and the browser frontend.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
The 'waved' server acts as an object store for images, files, and data assets.
Dynamic grid-based layout system that automatically adjusts based on viewport size using CSS Flexbox/Grid logic under the hood.
Global theme objects allow for deep customization of colors, typography, and card styles.
Native support for Python asyncio, allowing the UI to remain responsive during heavy ML computations.
Includes over 100+ specialized cards for charts (Vega-Lite), Markdown, navigation, and input forms.
Wave apps can be embedded within other web platforms as micro-frontends.
Visualizing high-frequency trading signals without UI lag.
Registry Updated:2/7/2026
Non-technical stakeholders needing to understand black-box model decisions.
Detecting model drift in production environments.