When should I use Plotly instead of Redash?

Interactive charts and dashboards renderable in browsers, Jupyter notebooks, and Dash apps. Building data applications with Dash, Plotly's companion web framework for Python. Production-quality interactive visualizations for reports, web applications, and stakeholder tools

When should I use Redash instead of Plotly?

SQL-based dashboards and charts for data and analytics teams using multiple data sources. Sharing query-based visualizations with business stakeholders without requiring SQL knowledge. Simple self-hosted BI tool for teams that need dashboards without a dedicated data platform

What are the main weaknesses of Plotly?

Generated HTML and JavaScript bundles can be very large for complex or multi-chart pages. API inconsistencies between Plotly Express (simple) and Graph Objects (complex) can confuse users. Performance degrades with very large datasets above 100k data points rendered in the browser

What are the main weaknesses of Redash?

Limited visualization types compared to Superset or Metabase for complex analytical charts. Requires a dedicated server and ongoing maintenance for the self-hosted deployment. Development momentum has slowed since acquisition; fewer new features in recent releases

Plotly vs Redash: Key Differences for Python Data Engineering

Data Visualization

Plotly

Interactive Visualization Library

★ 4.8

MIT

pip install plotly

Redash

Data Visualization & Dashboards

★ 4.4

BSD-2-Clause

N/A — web application, deploy via Docker

Side-by-Side Comparison

Plotly

Redash

Plotly

Redash

Best For

✓Interactive charts and dashboards renderable in browsers, Jupyter notebooks, and Dash apps
✓Building data applications with Dash, Plotly's companion web framework for Python
✓Production-quality interactive visualizations for reports, web applications, and stakeholder tools

✓SQL-based dashboards and charts for data and analytics teams using multiple data sources
✓Sharing query-based visualizations with business stakeholders without requiring SQL knowledge
✓Simple self-hosted BI tool for teams that need dashboards without a dedicated data platform

Best For

✓Interactive charts and dashboards renderable in browsers, Jupyter notebooks, and Dash apps
✓Building data applications with Dash, Plotly's companion web framework for Python
✓Production-quality interactive visualizations for reports, web applications, and stakeholder tools

✓SQL-based dashboards and charts for data and analytics teams using multiple data sources
✓Sharing query-based visualizations with business stakeholders without requiring SQL knowledge
✓Simple self-hosted BI tool for teams that need dashboards without a dedicated data platform

Weaknesses

•Generated HTML and JavaScript bundles can be very large for complex or multi-chart pages
•API inconsistencies between Plotly Express (simple) and Graph Objects (complex) can confuse users
•Performance degrades with very large datasets above 100k data points rendered in the browser

•Limited visualization types compared to Superset or Metabase for complex analytical charts
•Requires a dedicated server and ongoing maintenance for the self-hosted deployment
•Development momentum has slowed since acquisition; fewer new features in recent releases

Weaknesses

•Generated HTML and JavaScript bundles can be very large for complex or multi-chart pages
•API inconsistencies between Plotly Express (simple) and Graph Objects (complex) can confuse users
•Performance degrades with very large datasets above 100k data points rendered in the browser

•Limited visualization types compared to Superset or Metabase for complex analytical charts
•Requires a dedicated server and ongoing maintenance for the self-hosted deployment
•Development momentum has slowed since acquisition; fewer new features in recent releases

License

MIT

BSD-2-Clause

License

MIT

BSD-2-Clause

Install

pip install plotly

N/A — web application, deploy via Docker

Install

pip install plotly

N/A — web application, deploy via Docker

Rating

★ 4.8

★ 4.4

Rating

★ 4.8

★ 4.4

Key Features

Plotly

1Interactive charts including scatter, bar, line, heatmap, 3D, and geographic plots
2Plotly Express high-level API for creating charts with a single function call
3Dash framework for building interactive data applications and internal dashboards
4Supports both static export (PNG, SVG) and interactive HTML output
5WebGL rendering for high-performance visualisation of large datasets

Redash

1Open-source BI tool focused on SQL-first data exploration and dashboarding
2Query snippets and parameterized queries for reusable SQL components
3Scheduled query execution with result caching and email alerting
4Supports 35+ data sources via built-in connectors
5Embeds dashboards and charts into other web applications via iframes

How Python Data Engineers Use These Tools

Plotly

Python data engineers use Plotly to build interactive charts for pipeline monitoring dashboards, data quality reports, and stakeholder-facing analytics. Plotly Express integrates directly with Pandas DataFrames, making it straightforward to visualise query results, model outputs, and trend data. The Dash framework extends Plotly into full web applications with dropdowns, sliders, and callbacks — enabling data teams to build internal tools without a separate frontend.

Redash

Python data engineers use Redash's API to programmatically create and update queries and dashboards — triggering query execution via the REST API after a pipeline run and sharing dashboard links with stakeholders. Redash is commonly deployed as the lightweight analytics UI for internal pipelines, letting non-technical users explore warehouse data without SQL knowledge.

More Data Visualization Comparisons

Data Visualization

Matplotlib vs Seaborn

Data Visualization

Matplotlib vs Plotly

Data Visualization

Bokeh vs Matplotlib

Data Visualization

Altair vs Matplotlib

Data Visualization

Apache Superset vs Matplotlib

Data Visualization

Matplotlib vs Redash

Individual Tool Pages

View Plotly details →View Redash details →

Side-by-Side Comparison

Plotly

Redash

Plotly

Redash

Best For

✓Interactive charts and dashboards renderable in browsers, Jupyter notebooks, and Dash apps
✓Building data applications with Dash, Plotly's companion web framework for Python
✓Production-quality interactive visualizations for reports, web applications, and stakeholder tools

✓SQL-based dashboards and charts for data and analytics teams using multiple data sources
✓Sharing query-based visualizations with business stakeholders without requiring SQL knowledge
✓Simple self-hosted BI tool for teams that need dashboards without a dedicated data platform

Best For

✓Interactive charts and dashboards renderable in browsers, Jupyter notebooks, and Dash apps
✓Building data applications with Dash, Plotly's companion web framework for Python
✓Production-quality interactive visualizations for reports, web applications, and stakeholder tools

✓SQL-based dashboards and charts for data and analytics teams using multiple data sources
✓Sharing query-based visualizations with business stakeholders without requiring SQL knowledge
✓Simple self-hosted BI tool for teams that need dashboards without a dedicated data platform

Weaknesses

•Generated HTML and JavaScript bundles can be very large for complex or multi-chart pages
•API inconsistencies between Plotly Express (simple) and Graph Objects (complex) can confuse users
•Performance degrades with very large datasets above 100k data points rendered in the browser

•Limited visualization types compared to Superset or Metabase for complex analytical charts
•Requires a dedicated server and ongoing maintenance for the self-hosted deployment
•Development momentum has slowed since acquisition; fewer new features in recent releases

Weaknesses

•Generated HTML and JavaScript bundles can be very large for complex or multi-chart pages
•API inconsistencies between Plotly Express (simple) and Graph Objects (complex) can confuse users
•Performance degrades with very large datasets above 100k data points rendered in the browser

•Limited visualization types compared to Superset or Metabase for complex analytical charts
•Requires a dedicated server and ongoing maintenance for the self-hosted deployment
•Development momentum has slowed since acquisition; fewer new features in recent releases

License

MIT

BSD-2-Clause

License

MIT

BSD-2-Clause

Install

pip install plotly

N/A — web application, deploy via Docker

Install

pip install plotly

N/A — web application, deploy via Docker

Rating

★ 4.8

★ 4.4

Rating

★ 4.8

★ 4.4

Key Features

Plotly

1Interactive charts including scatter, bar, line, heatmap, 3D, and geographic plots
2Plotly Express high-level API for creating charts with a single function call
3Dash framework for building interactive data applications and internal dashboards
4Supports both static export (PNG, SVG) and interactive HTML output
5WebGL rendering for high-performance visualisation of large datasets

Redash

1Open-source BI tool focused on SQL-first data exploration and dashboarding
2Query snippets and parameterized queries for reusable SQL components
3Scheduled query execution with result caching and email alerting
4Supports 35+ data sources via built-in connectors
5Embeds dashboards and charts into other web applications via iframes

How Python Data Engineers Use These Tools