Plotly alternatives

3 tools to consider instead of Plotly, shown against it.

Plotly D3.js Apache ECharts Matplotlib
Vendor Plotly, Inc. D3.js open-source project (Observable) Apache Software Foundation NumFOCUS (Matplotlib Development Team)
Pricing model Open source + paid options Open source + paid options Open source + paid options Open source + paid options
Free tier Yes Yes Yes Yes
Deployment Self-hosted Self-hosted Self-hosted Self-hosted
Open source Yes (MIT) Yes (ISC) Yes (Apache-2.0) Yes (Matplotlib License (BSD-compatible))
Best for Analysts and developers who want interactive charts with minimal setup across Python, R, or JS. Front-end and data-visualization engineers who need fully custom, non-templated charts. Teams building dashboards that need many chart types with less custom code than D3. Python users who need precise control over static, publication-quality charts.
Pricing

The charting libraries themselves are free and open source; Plotly's paid products (Dash Enterprise) are separate.

Pricing has not been verified yet — see the vendor's site.

Free, open-source library with no paid tier or vendor pricing page.

Pricing has not been verified yet — see the vendor's site.

Free, open-source Apache project with no pricing page.

Pricing has not been verified yet — see the vendor's site.

Free, open-source project fiscally sponsored by NumFOCUS with no pricing page.

Pricing has not been verified yet — see the vendor's site.

Features
  • Interactive zoom, pan, and hover tooltips out of the box
  • Statistical, scientific, financial, and 3D chart types
  • Pandas DataFrame integration
  • Consistent rendering across Python, R, and JavaScript
  • Export to standalone HTML
  • WebGL rendering for large datasets
  • Low-level data-to-DOM binding
  • SVG, HTML, and Canvas rendering
  • Scales, axes, and shape generators
  • Animated transitions
  • Force-directed and hierarchical layouts
  • Geographic projections
  • Full control over every visual element
  • Large catalog of built-in chart types
  • Canvas, SVG, and WebGL rendering
  • Built-in data zoom and brushing interactions
  • Geographic and geo-map visualizations
  • Large-dataset performance optimizations
  • Theming and responsive layout support
  • Object-oriented Figure/Axes API
  • Fine-grained control of every chart element
  • Publication-quality static output (PNG, SVG, PDF)
  • Wide range of plot types
  • Tight NumPy and Pandas integration
  • Inline rendering in Jupyter

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