deck.gl vs plotly.py
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A GPU-accelerated framework for drawing large data sets as layers on maps and 3D views.
Open source · MIT
A Python library for making interactive, browser-based charts and graphs.
The short answer
Choose deck.gl if you need…
- Developers building interactive maps or geospatial dashboards over large data sets
- Teams who need GPU rendering for tens of thousands of points or shapes
- Python users who want to produce deck.gl maps through pydeck
- Projects that need custom rendering by extending core classes
Choose plotly.py if you need…
- Python users who want hoverable, zoomable charts without writing JavaScript
- Data analysts working in Jupyter notebooks
- People building shareable HTML charts for reports
- Developers embedding charts in Dash dashboards
Side-by-side
| deck.gl | plotly.py | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 14.7k | 18.9k |
| Setup difficulty | Intermediate | Beginner |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | TypeScript | Python |
| Last code update | 4 days ago | 5 days ago |
What each one is
deck.gl
deck.gl is a visualization framework that uses WebGL2 and WebGPU to draw large data sets quickly in the browser. You feed it an array of data objects, choose layers such as icons, polygons or text, and view them through map, first-person or orthographic views. It is used by developers who need interactive maps and charts over big data, and it also has a Python wrapper called pydeck.
plotly.py
plotly.py is a Python library for creating interactive charts that open in a web browser. It is built on top of plotly.js and lets you describe a chart in code, then view it in Jupyter notebooks, as standalone HTML files, or inside Dash applications. The README says plotly.js provides over 30 chart types, including 3D, statistical, map and financial charts.
Best for
deck.gl
- Developers building interactive maps or geospatial dashboards over large data sets
- Teams who need GPU rendering for tens of thousands of points or shapes
- Python users who want to produce deck.gl maps through pydeck
- Projects that need custom rendering by extending core classes
plotly.py
- Python users who want hoverable, zoomable charts without writing JavaScript
- Data analysts working in Jupyter notebooks
- People building shareable HTML charts for reports
- Developers embedding charts in Dash dashboards
Strengths
deck.gl
- Built for GPU rendering of large data sets
- Large catalog of tested layers and flexible APIs for customization
- Open-source under the MIT license, part of the OpenJS Foundation's vis.gl project
plotly.py
- Charts are interactive by default, so users can hover, zoom and inspect data
- Two APIs: a short high-level one (Plotly Express) and a detailed one (Graph Objects)
- Licensed under MIT with a large community and active maintenance
Not ideal for
deck.gl
- People wanting a finished tool without coding, since it is a framework for building visualizations
- Simple static charts with small data, where a lighter charting library is easier
- Users on browsers without WebGL2 support, since rendering depends on it
plotly.py
- People who only need static images for print; the README points to a separate Kaleido package and a Chrome install for that, which adds setup
- Users who need a non-Python language; this project is Python-specific
- Projects needing a built-in dashboard server on its own, since the README links Dash as a separate product
Trade-offs
deck.gl
- Requires programming knowledge and is not a point-and-click tool
- Relies on WebGL2 or WebGPU support in the user's browser
- The README is truncated here, so detailed setup requirements are not fully confirmed
plotly.py
- Static image export needs an extra package and a Chrome or Chromium install
- Interactive charts depend on a browser, so they are less suited to fully offline or print-only workflows
- The README is truncated here, so detailed API coverage and version-specific changes were not verified
Getting started
deck.gl
Install the npm module
npm install deck.glInstall the Python package
pip install pydeckplotly.py
Install plotly with pip
pip install plotlyInstall with Plotly Express support
pip install plotly[express]Frequently asked questions
What is the difference between deck.gl and plotly.py?
deck.gl: A GPU-accelerated framework for drawing large data sets as layers on maps and 3D views. plotly.py: A Python library for making interactive, browser-based charts and graphs. deck.gl suits developers building interactive maps or geospatial dashboards over large data sets, while plotly.py suits python users who want hoverable, zoomable charts without writing javascript.
Are deck.gl and plotly.py free?
deck.gl: Free and open source. plotly.py: Free and open source.
Which is easier to set up, deck.gl or plotly.py?
plotly.py is easier to get started with (rated beginner); deck.gl is rated intermediate and usually needs more setup.
Is deck.gl or plotly.py more popular?
plotly.py has more GitHub stars (18.9k) than deck.gl (14.7k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to deck.gl and plotly.py?
Other options people compare them with include kepler.gl, Plotly.js, visx, G6, G2.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.