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Streamlit VS Bokeh

Compare Streamlit VS Bokeh and see what are their differences

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Streamlit logo Streamlit

Turn python scripts into beautiful ML tools

Bokeh logo Bokeh

Bokeh visualization library, documentation site.
  • Streamlit Landing page
    Landing page //
    2023-10-07
  • Bokeh Landing page
    Landing page //
    2022-11-01

Streamlit features and specs

  • Ease of Use
    Streamlit's API is extremely intuitive and easy to learn, which makes it accessible for developers of varying experience levels. The simplicity allows for rapid development and less time spent on complex front-end coding.
  • Interactive Widgets
    It provides a set of interactive widgets that make it simple to add complex functionalities like sliders, buttons, and file uploaders to your application with minimal code.
  • Real-time Feedback
    Streamlit supports real-time data updates, allowing users to see changes instantly. This is particularly useful for data analysis and machine learning applications where live data visualization is crucial.
  • Integration with Machine Learning Libraries
    Streamlit integrates seamlessly with popular machine learning libraries like TensorFlow, PyTorch, and scikit-learn, making it a great tool for showcasing machine learning models and results.
  • Open Source
    Being an open-source project, Streamlit is free to use and comes with the support and contributions of an active community. This means continuous improvements and a wealth of shared resources.

Possible disadvantages of Streamlit

  • Limited Customization
    Streamlit offers limited customization options compared to traditional web frameworks. This can be a hindrance if you need a highly customized UI/UX for your application.
  • Performance Issues
    For more complex or resource-intensive applications, Streamlit may suffer from performance drawbacks. It is not designed for high-performance computing out of the box.
  • Scalability
    Streamlit is not well-suited for large-scale applications requiring major backend architecture or for scenarios demanding high scalability and concurrency.
  • Limited Widget Style Options
    The styling and customization options for widgets are somewhat limited, meaning your application's look and feel might be more constrained compared to using other front-end frameworks.
  • Deployment Complexity
    While Streamlit provides some deployment options, deploying Streamlit apps in a production environment can sometimes require additional effort and knowledge, especially for those unfamiliar with web deployment practices.

Bokeh features and specs

  • Interactive Visualizations
    Bokeh is designed specifically for creating interactive and highly customizable visualizations, making it suitable for engaging data exploration.
  • Python Integration
    Bokeh integrates well with the Python ecosystem, allowing direct use of pandas, NumPy, and other Python libraries, facilitating seamless data manipulation and visualization.
  • Web Compatibility
    Bokeh generates plots that are ready to be embedded into web applications, making it a powerful tool for creating dashboards and interactive reports.
  • Server Functionality
    Bokeh provides a server component that allows users to build and deploy sophisticated interactive applications using just Python.
  • Variety of Plotting Options
    Bokeh offers a wide range of plotting capabilities including charts, maps, and streamgraphs, enabling users to create complex visual stories.

Possible disadvantages of Bokeh

  • Learning Curve
    Bokeh may have a steeper learning curve for users unfamiliar with JavaScript or those looking for a very simple or quick plotting tool.
  • Performance Issues
    When dealing with very large datasets, Bokeh might suffer from performance issues, as it is primarily client-side rendering.
  • Limited 3D Capabilities
    Bokeh's support for 3D plotting is limited compared to other visualization libraries like Plotly, potentially restricting its use for applications that require 3D visualizations.
  • Documentation and Community Size
    While Bokeh has good documentation, its user community is smaller compared to more mature libraries like Matplotlib, which can mean fewer resources and third-party support options.

Analysis of Streamlit

Overall verdict

  • Overall, Streamlit is well-regarded for its ease of use, speed of development, and ability to create clean and professional-looking applications without in-depth web development knowledge. It provides a seamless bridge between complex data analysis and user-friendly presentation, which can be highly beneficial for a wide range of use cases.

Why this product is good

  • Streamlit is a popular choice for quickly building and deploying data applications and interactive dashboards with minimal code. It is designed to be user-friendly, allowing data scientists and engineers to transform their scripts into shareable web apps. It supports real-time updates, is highly customizable, and integrates well with Python libraries like NumPy, Pandas, and Matplotlib, making it an attractive option for many developers working within the Python ecosystem.

Recommended for

    Streamlit is ideal for data scientists, analysts, and developers looking to rapidly prototype and deploy data-driven applications. It is recommended for those who prioritize simplicity, quick deployment, and seamless integration with Python code. Individuals or teams interested in building dashboards, ML model sharing platforms, or interactive reports will find Streamlit particularly useful.

Analysis of Bokeh

Overall verdict

  • Yes, Bokeh is a good choice for data visualization, particularly if you need to create interactive, high-quality plots that can be shared and displayed on the web.

Why this product is good

  • Bokeh is a powerful and interactive visualization library for Python that is known for its ability to create elegant, scalable, and versatile graphics. It is especially useful for creating web-ready, interactive plots that can be easily embedded into web pages or applications. Bokeh is praised for its intuitive and flexible interface, making it a great choice for both simple and complex visualizations.

Recommended for

  • Data scientists who need to create interactive visualizations for data exploration.
  • Web developers looking to incorporate dynamic plots into their applications.
  • Educators and researchers who need to present data interactively in a web-based format.
  • Anyone seeking a versatile tool compatible with various data formats and capable of producing real-time streaming plots.

Streamlit videos

+ Add

My thoughts on web frameworks in Python and R (PyWebIO vs Streamlit vs R Shiny)

More videos:

  • Review - 1/4: What is Streamlit
  • Tutorial - How to Build a Streamlit App (Beginner level Streamlit tutorial) - Part 1

Bokeh videos

+ Add

"Bokeh" - Netflix Film Review

More videos:

Category Popularity

0-100% (relative to Streamlit and Bokeh)

Streamlit

Bokeh

Developer Tools
100 100%
Developer Tools
0% 0
Charting Libraries
0 0%
Charting Libraries
100% 100
Productivity
100 100%
Productivity
0% 0
Data Visualization
0 0%
Data Visualization
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Streamlit and Bokeh

Streamlit Reviews

We have no reviews of Streamlit yet.
Be the first one to post

Bokeh Reviews

Top 8 Python Libraries for Data Visualization
Pygal is a Python data visualization library that is made for creating sexy charts! (According to their website!) While Pygal is similar to Plotly or Bokeh in that it creates data visualization charts that can be embedded into web pages and accessed using a web browser, a primary difference is that it can output charts in the form of SVG’s or Scalable Vector Graphics. These...
Source: www.geeksforgeeks.org

Social recommendations and mentions

Based on our record, Streamlit seems to be a lot more popular than Bokeh. While we know about 210 links to Streamlit, we've tracked only 5 mentions of Bokeh. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Streamlit mentions (210)

  • Predictive Maintenance Systems for Cleaning Robots: Boosting Efficiency Through Smart Tech
    Use Streamlit to visualize and test predictions interactively:. - Source: dev.to / about 19 hours ago
  • Build Code-RAGent, an agent for your codebase
    The only thing left to do then was to build something that could showcase the power of code ingestion within a vector database, and it immediately clicked in my mind: "Why don't I ingest my entire codebase of solved Go exercises from Exercism?" That's how I created Code-RAGent, your friendly coding assistant based on your personal codebases and grounded in web search. It is built on top of GPT-4.1, powered by... - Source: dev.to / about 1 month ago
  • How AI is Transforming Front-End Development in 2025!
    Streamlit.io: Great documentation and reusable components to integrate with your AI application for rapid python front-end AI development. - Source: dev.to / about 1 month ago
  • Querying DynamoDB with Natural Language Using MCP
    The agent uses MCP to translate this into a DynamoDB query. Then, using Streamlit UI, results are returned in a structured format, making it easy to use. - Source: dev.to / 3 months ago
  • Can I run this LLM?
    It's powered by something called "Streamlit" (https://streamlit.io). > A faster way to build and share data apps Website doesn't even load for me. I don't even know what to say...welcome to web dev 2025 edition. - Source: Hacker News / 3 months ago
View more

Bokeh mentions (5)

  • [OC] Chemical Diversity of The GlobalChem Common Chemical Universe
    Visualization: https://docs.bokeh.org/en/latest/. Source: about 3 years ago
  • Profiling workflows with the Amazon Genomics CLI
    Now that we can get task timing information in a consistent manner, let’s do some plotting. For this, I’m going to use Bokeh which generates nice interactive plots. - Source: dev.to / about 3 years ago
  • 10 Python Libraries For Data Visualization
    Bokeh The Bokeh library is native to Python and is mainly used to create interactive, web-ready plots, which can be easily output as HTML documents, JSON objects, or interactive web applications. Like ggplot, its concepts are also based on the Grammar of Graphics. It has the added advantage of managing real-time data and streaming. This library can be used for creating common charts such as histograms, bar plots,... - Source: dev.to / over 3 years ago
  • Graphic library Bokeh is underrated and underdocumented
    It's not in the least bit "underrated" and it's documentation is extensive. Source: about 4 years ago
  • Help with Bokeh Interactive Plot
    Hi guys! I am currently working on a project to enrich my Master thesis with some interactive plots. I have been using the Bokeh library to make a standalone application, which I was then planning to deploy in Heroku. You can find the code in this repository. But I will also add it at the bottom of the post. Source: about 4 years ago

What are some alternatives?

When comparing Streamlit and Bokeh, you can also consider the following products

Anvil.works - Build seriously powerful web apps with all the flexibility of Python. No web development experience required.

Plotly - Low-Code Data Apps

Recut - Edit silence out of videos automatically

RAWGraphs - RAWGraphs is an open source app built with the goal of making the visualization of complex data...

FastAPI - FastAPI is an Open Source, modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.

D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.

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