Software Alternatives, Accelerators & Startups

NumPy VS Bryntum

Compare NumPy VS Bryntum and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

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.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • 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

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

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 NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

NumPy videos

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

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 NumPy 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 NumPy 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 NumPy and Bryntum

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Bryntum Reviews

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

Social recommendations and mentions

Based on our record, NumPy seems to be more popular. It has been mentiond 121 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.

NumPy mentions (121)

  • 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
  • Your 2025 Roadmap to Becoming an AI Engineer for Free for Vue.js Developers
    AI starts with math and coding. You donโ€™t need a PhDโ€”just high school math like algebra and some geometry. Linear algebra (think matrices) and calculus (like slopes) help understand how AI models work. Python is the main language for AI, thanks to tools like TensorFlow and NumPy. If you know JavaScript from Vue.js, Pythonโ€™s syntax is straightforward. - Source: dev.to / about 2 months ago
  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 8 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / about 1 year ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. Itโ€™s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / about 1 year ago
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Bryntum mentions (0)

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

What are some alternatives?

When comparing NumPy and Bryntum, you can also consider the following products

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

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

OpenCV - OpenCV is the world's biggest computer vision library

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

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

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.