Why Foxglove? // Competitive Comparison / Webviz

Foxglove vs. Webviz

Webviz is a web-based tool developed by Cruise (RIP) for visualizing robotics data directly in a browser. It supports ROS bag files and live robot data, enabling tasks like debugging and analysis. Webviz allows users to customize layouts using panels such as 3D visualizations, plots, and image displays.

Foxglove is purpose-built visualization and observability for robotics and embodied AI development.

Foxglove empowers over 10,000 robotics and embodied AI developers and supports hundreds of robotics companies in accelerating and scaling their development. From data collection and ingestion to visualizing, debugging, and managing robotic data, Foxglove streamlines every step of the development process, enabling teams to innovate faster and more effectively.

“Things that were impossible before are now possible.”

Robert Sun, Founding Engineer, Dexterity
Visualization and analysis
Offers a comprehensive suite of 20+ integrated panels (e.g., Image (h.264, h.265, VP9, AV1), 3D, plot, state, raw messages, etc) all with extensive analysis features for diverse data types, enabling users to sync and arrange panels within sharable layouts. Foxglove works with live and recorded data streams.
Focused on ROS data, including
3D markers, plots, and primarily relies on ROS image topics. Tailored for lightweight autonomous vehicle data.
Data collection and management
Includes native capabilities managing and visualizing both live and recorded data, facilitating efficient troubleshooting and debugging.
There is no data collection and management capabilities.
Performance
Designed to handle diverse, complex multimodal robotics data efficiently including displaying over a dozen panels streaming h.265 video at 5x speeds and plotting thousands of points from temporal data and rendering point cloud, annotations, and more in complex 3D scenes.
Performance is understood within the community; may require optimization for handling large datasets or complex visualizations.
Extensibility
Supports user-contributed extensions via React, allowing for easy installation of custom panels as well as message converter extensions to convert messages from one schema to another and topic alias extensions to alias topics in your data source to new topics.
Open-source project with extensibility through its codebase; however, contributions can be complex due to tight coupling with proprietary extensions.
Integration
Provides a unified environment for various visualization tools, data formants (MCAP, ROS, Protobuf, FlattBuffers, JSON) reducing the need to install and learn multiple applications.
Focused on autonomous driving use cases and ROS data, which may limit its applicability across diverse robotics applications.
User Interface
Features a modern, user-friendly interface utilizing modern web technologies and languages, including customizable layouts and integrated panels for a streamlined user experience.
UI is functional and straightforward, offering essential panels for visualizing ROS data.
Support
Provides official support channels and documentation; community support is growing.
Established documentation and user-contributed resources.
Learning curve
Offers an intuitive interface with no coding required  that’s easy for new users to learn, especially with integrated tools reducing the need to manage multiple applications.
Has a steeper learning curve due to the necessity of integrating multiple tools for comprehensive functionality.
Platform compatibility
Available as both a web and desktop application (MacOS, Linux, Windows), providing flexibility in deployment and access.
A web-based application, accessible through modern browsers.

Foxglove’s modern, intuitive interface and support for diverse data formats, including ROS (1 and 2), MCAP, Flatbuffers, Protobuf, and JSON, including video codecs like H.264, H.265, and AV1, and telemetry and sensor data, ensure a seamless experience for both real-time and historical analysis. Its performance handling large datasets allows teams to gain actionable insights efficiently, whether debugging complex systems or monitoring live operations.

Getting started with Foxglove is straightforward and requires minimal setup:

1. Download and install Foxglove for your platform (Windows, macOS, or Linux) or use it directly in your browser.
2. Drag and drop your ROS bag files into Foxglove to start replaying and analyzing data immediately.

“The integration was surprisingly straightforward, enabling us to quickly transition from using multiple disjointed tools to a unified workflow. This significantly reduced the barriers for team members to engage with system data.”

Scott Butters, Staff Machine Learning Engineer, Aescape

Start building with Foxglove.

Get started for free