Bokeh
Bokeh is an open-source Python library for creating interactive visualizations for web browsers.
Profile
Bokeh is an open-source Python library for creating interactive web-based visualizations and dashboards.
Bokeh is an open-source Python library for creating interactive visualizations for web browsers. It was first released in 2013 by a team including Bryan Van de Ven, and is now a project of the NumFOCUS foundation, a 501(c)(3) nonprofit. As of June 2026, the project's GitHub repository has over 20,400 stars and has seen more than 21,000 commits.
The library is maintained by a core team of volunteer contributors and a small number of paid developers funded through NumFOCUS donations and Tidelift subscriptions. Bokeh's primary product is the bokeh library itself, which allows users to generate interactive plots, dashboards, and data applications that run in modern web browsers without requiring any JavaScript knowledge. The library's architecture separates the Python backend from the browser-based frontend (BokehJS), enabling complex client-side interactivity.
Bokeh has no corporate parent, no disclosed revenue, and no paid sales team. Its funding comes entirely from community donations and sponsorship programs. The project has not raised any venture capital and has no known plans to do so.
Bokeh's development pace has been steady, with the latest stable branch being 3.10 as of June 2026. The project has no disclosed headcount, customer base, or notable enterprise clients. Its user base is primarily individual data scientists, researchers, and developers who use the library for exploratory data analysis, scientific publishing, and building internal dashboards.
Bokeh competes with other open-source Python visualization libraries like Plotly, Matplotlib, and Altair, as well as commercial tools like Tableau and Power BI. The library's main differentiator is its ability to produce standalone HTML files that can be shared without any server infrastructure. Bokeh has no known controversies, leadership changes, or layoffs, as it has never had a formal corporate structure.
The project's main risk is its reliance on volunteer maintainers and the potential for contributor burnout, which is common in open-source projects. Bokeh has not announced any major new features or partnerships in the past year.
Who buys this
- Data scientists and analysts who need to create interactive plots for exploratory analysis
- Researchers and academics who publish interactive figures in scientific papers and presentations
- Developers building internal dashboards and data applications for their organizations
- Educators teaching data visualization and data science courses
- Journalists and media organizations creating interactive data-driven stories
Strengths and what to watch
Strengths
- Produces standalone HTML files that can be shared without any server or backend infrastructure
- Large and active open-source community with over 20,400 GitHub stars and 21,000+ commits
- Backed by NumFOCUS, a well-established nonprofit that provides governance and fiscal sponsorship for open-source projects
Watch for
- Relies entirely on volunteer maintainers and community donations, with no paid staff or corporate backing
- Faces strong competition from better-funded open-source alternatives like Plotly and commercial tools like Tableau
- No disclosed revenue, customer base, or enterprise adoption metrics, making it difficult to assess commercial viability
Key Information
- Industry
- Open Source Infra - Visualizations
- Founded
- 2013
Frequently Asked Questions
What is Bokeh and what does it do?
Bokeh is an open-source Python library for creating interactive web-based visualizations and dashboards. It lets you generate interactive plots and data applications that run in modern browsers without needing JavaScript knowledge.
How is Bokeh funded and who maintains it?
Bokeh is a NumFOCUS project funded through community donations and Tidelift subscriptions. It has no corporate parent or venture capital. A core team of volunteer contributors and a few paid developers maintain the library.
Who typically uses Bokeh for data visualization?
Bokeh's users include data scientists, researchers, developers building internal dashboards, educators teaching data science, and journalists creating interactive stories. It is popular for exploratory analysis and scientific publishing.
What makes Bokeh different from other Python visualization libraries?
Bokeh's main differentiator is producing standalone HTML files that can be shared without any server infrastructure. This contrasts with libraries like Plotly or Matplotlib, which often require backend support for interactivity.
What are the main risks of using Bokeh for projects?
Bokeh relies entirely on volunteer maintainers and community donations, with no paid staff or corporate backing. This creates a risk of contributor burnout, which is common in open-source projects and can slow development.
How does Bokeh compare to Plotly and Tableau?
Bokeh competes with open-source libraries like Plotly and commercial tools like Tableau. It offers standalone HTML outputs without server needs, but faces strong competition from better-funded alternatives with larger enterprise adoption.
Sources
- github.com — GitHub repository showing 20,400+ stars, 21,000+ commits, and branch-3.10 as the latest stable version
- numfocus.org — Bokeh is a NumFOCUS project and accepts donations through the NumFOCUS website
- tidelift.com — Bokeh receives funding through Tidelift's open-source maintenance subscription program
- github.com — Bokeh has a GitHub Sponsors page for receiving community donations