Software Alternatives, Accelerators & Startups

Scikit-learn VS Bryntum

Compare Scikit-learn VS Bryntum and see what are their differences

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Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Bryntum logo Bryntum

High performance web components for SaaS apps - including Gantt, Scheduler, Grid, Calendar and Kanban widgets. Seamless integration with React, Vue, Angular or plain JS apps.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Bryntum
    Image date //
    2024-12-28
  • Bryntum
    Image date //
    2024-12-28
  • Bryntum
    Image date //
    2024-12-28
  • Bryntum
    Image date //
    2024-12-28

Tired of building scheduling features from scratch? Bryntumโ€™s high-performance components handle the heavy lifting - no more date-time nightmares. Our JavaScript widgets (Scheduler, Data Grid, Gantt, TaskBoard, Calendar) integrate seamlessly with React, Angular, or Vue. They process massive datasets, deliver fast rendering, and adapt to your style. With robust docs, flexible APIs, and dedicated support, Bryntum helps you build top-tier apps without the late-night debugging.

Bryntum

$ Details
Free Trial $850.0 / One-off (OEM license for commercial use)
Platforms
React Angular Vue JavaScript TypeScript
Release Date
2009 September
Startup details
Country
Sweden
City
Stockholm
Founder(s)
Mats Bryntse
Employees
10 - 19

Scikit-learn features and specs

  • Ease of Use
    Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
  • Extensive Documentation and Community Support
    The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
  • Integration with Other Libraries
    Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
  • Variety of Algorithms
    It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
  • Performance
    Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.

Possible disadvantages of Scikit-learn

  • Limited Deep Learning Support
    Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
  • Not Ideal for Large-Scale Data
    While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
  • Lack of Online Learning Algorithms
    The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
  • Less Flexibility in Customization
    It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
  • Dependency Overhead
    Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.

Bryntum features and specs

  • Drag-drop
    Drag drop and resize any task
  • RTL
    Right-to-Left support
  • Import & Export
    Export + Import from MS Project / Excel
  • WCAG 2.1
    Fully accessible
  • Travel time
    Visualize travel time for each task
  • Easy theming
    Roll your own theme or customize one of the built-in ones
  • Dark theme
    For working late
  • High performance
    Handles tens of thousands of tasks/rows
  • UX
    Excellent UX your users will love
  • AI Copilot
    In-product AI assistant letting you navigate and schedule using natural language

Analysis of Scikit-learn

Overall verdict

  • Yes, Scikit-learn is generally regarded as a good library for machine learning, especially for beginners and intermediate users who need reliable tools with efficient implementation of numerous algorithms.

Why this product is good

  • Scikit-learn is considered a good machine learning library because it provides a wide range of state-of-the-art algorithms for supervised and unsupervised learning. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is well-documented, easy to use, and has a consistent API that simplifies the integration of different algorithms. Furthermore, there's a strong community and continuous development, which means it is well-maintained and updated regularly with new features and improvements.

Recommended for

  • Beginners learning machine learning concepts and application.
  • Data scientists and engineers looking for a robust and efficient toolkit to build and deploy machine learning models.
  • Researchers who need an easy-to-use library that facilitates the experimentation of various algorithms.
  • Developers who require a seamless, Python-based machine learning library that integrates well with other data analysis tools and environments.

Scikit-learn videos

Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

Bryntum videos

Boost your app UX with Bryntum

More videos:

  • Demo - Flight dispatch scheduling demo with Bryntum Scheduler Pro

Category Popularity

0-100% (relative to Scikit-learn and Bryntum)
Data Science And Machine Learning
Project Management
0 0%
100% 100
Data Science Tools
100 100%
0% 0
Javascript UI Libraries
0 0%
100% 100

Questions and Answers

As answered by people managing Scikit-learn and Bryntum.

How would you describe your primary audience?

Bryntum's answer:

Bryntum primarily targets professional software teams - particularly frontend developers, architects, UX, and technical leads who need robust scheduling and project-planning functionality for their web applications. Our products (such as the Scheduler and Gantt components) are designed for organizations that want to integrate sophisticated resource management, timeline visualization, and interactive scheduling into existing or new software solutions.

In practice, these teams often work in industries and use cases where precise scheduling is critical (e.g., project management, construction, healthcare, manufacturing, and IT services). While developers are the day-to-day implementers of Bryntumโ€™s products, managers or product owners (such as PMO leads or development managers) also play a role in evaluating Bryntumโ€™s solutions to ensure they meet the organizationโ€™s technical and business requirements.

What makes your product unique?

Bryntum's answer:

What Makes Bryntum Unique?

Bryntum stands out because of its laser focus on high-performance, enterprise-grade JavaScript componentsโ€”particularly around scheduling and project planning. Here are a few reasons why Bryntum is unique:

  1. Advanced Scheduling Expertise
    Bryntumโ€™s Scheduler and Gantt products are widely recognized for their sophisticated scheduling capabilities. Their tools handle complex resource allocations, dependencies, drag-and-drop reordering, and timeline visualizationsโ€”making them a go-to choice for project and resource management in large-scale applications.

  2. Pure JavaScript (Framework Agnostic)
    All Bryntum components are developed using modern, pure JavaScript. This means they can easily integrate into any tech stack or framework (React, Angular, Vue, etc.) without sacrificing functionality or performance. If you switch frameworks in the future, you can keep using Bryntumโ€™s components with minimal refactoring.

  3. Performance & Scalability
    Bryntum components are designed for high-volume data rendering. Whether itโ€™s thousands of tasks in a Gantt chart or a scheduler loaded with numerous resources, Bryntumโ€™s products can handle heavy data loads smoothly and maintain snappy interactions.

  4. Robust Feature Set
    From critical-path analysis in Gantt charts to resource histograms and timeline overviews, Bryntum packs advanced features that meet enterprise project-planning requirements. This feature depth is one reason many organizations choose Bryntum over more general-purpose grid libraries.

  5. Extensive Documentation & Demos
    Bryntum provides thorough documentation, live examples, and demo apps that showcase how to integrate its components into a variety of environments. This makes it easier for developers to learn the product and quickly build prototypes.

  6. Dedicated Support & Development
    A hallmark of Bryntum is its attentive support. Their engineering and support teams are responsive and highly knowledgeable about both front-end development and project-planning logic, which speeds up troubleshooting and feature requests.

By focusing on scheduling and project-planning tools with high performance, great flexibility, and deep functionality, Bryntum has carved out a niche that sets it apart from other libraries and component vendors.

Why should a person choose your product over its competitors?

Bryntum's answer:

Performance, UX and abundance of features.

Which are the primary technologies used for building your product?

Bryntum's answer:

JavaScript, TypeScript and CSS

What's the story behind your product?

Bryntum's answer:

Bryntum was founded by Mats Bryntse, a software developer from Stockholm, Sweden, who had a deep interest in creating advanced scheduling solutions for web applications. Originally, Bryntum began as a consulting and component-development company centered around Sencha Ext JS, one of the leading JavaScript frameworks in the late 2000s.

Early Days (Ext Scheduler & Gantt)

Mats Bryntse developed the first version of Ext Scheduler, a scheduling component based on Ext JS, in response to a growing demand for an interactive resource-scheduling tool in web applications. Building on the success of Ext Scheduler, Bryntum introduced a Gantt component, allowing developers to visualize and manage project tasks, dependencies, and timelines directly in the browser. Transition to Pure JavaScript

Over time, the JavaScript ecosystem expanded to include many popular frameworks (React, Angular, Vue, etc.). Instead of maintaining separate builds for each, Bryntum decided to make its components framework agnostic, rebuilding them as pure JavaScript libraries. This shift allowed Bryntumโ€™s tools to be integrated into virtually any front-end stack while delivering the same level of performance and scheduling sophistication.

Who are some of the biggest customers of your product?

Bryntum's answer:

  • Apple
  • Netflix
  • SpaceX
  • Intel
  • Disney
  • US Navy
  • Airbus
  • American Airlines
  • AstraZeneca
  • Coca-Cola

Over 5,000 customers in 80 countries: https://bryntum.com/company/customers/

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Scikit-learn and Bryntum

Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...

Bryntum Reviews

We have no reviews of Bryntum yet.
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Social recommendations and mentions

Based on our record, Scikit-learn seems to be more popular. It has been mentiond 35 times since March 2021. 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.

Scikit-learn mentions (35)

  • Top 5 GitHub Repositories for Data Science in 2026
    The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโ€ฆ. - Source: dev.to / 14 days ago
  • What is the Most Effective AI Tool for App Development Today?
    For apps demanding robust machine learning capabilities, frameworks like TensorFlow provide the scalability and flexibility needed to handle large-scale data and models. These tools are essential for developers building features like recommendation engines or predictive analytics. - Source: dev.to / about 2 months ago
  • Your 2025 Roadmap to Becoming an AI Engineer for Free for Vue.js Developers
    Machine learning (ML) teaches computers to learn from data, like predicting user clicks. Start with simple models like regression (predicting numbers) and clustering (grouping data). Deep learning uses neural networks for complex tasks, like image recognition in a Vue.js gallery. Tools like Scikit-learn and PyTorch make it easier. - Source: dev.to / about 2 months ago
  • Predicting Tomorrow's Tremors: A Machine Learning Approach to Earthquake Nowcasting in California
    Scikit-learn Documentation: https://scikit-learn.org/. - Source: dev.to / 3 months ago
  • Must-Know 2025 Developerโ€™s Roadmap and Key Programming Trends
    Pythonโ€™s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether youโ€™re experienced or just starting, Pythonโ€™s clear style makes it a good choice for diving into machine learning. Actionable Tip: If youโ€™re new to Python,... - Source: dev.to / 8 months ago
View more

Bryntum mentions (0)

We have not tracked any mentions of Bryntum yet. Tracking of Bryntum recommendations started around Dec 2024.

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Mobiscroll - UI controls for great web & mobile developers. Use it for progressive web and hybrid apps with plain JS, jQuery, Angular, React and KO.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

DHTMLX - JavaScript Library for cross-platform web and mobile app development with HTML5 JavaScript widgets. Easy integration with popular JavaScript Frameworks.

NumPy - NumPy is the fundamental package for scientific computing with Python

Schedule-X.dev - Modern JavaScript Event calendar for React, Angular, Vue and plain JS. Modern alternative to Fullcalendar. Drag & drop, dark mode, event resizing and more.