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Kasm VS NumPy

Compare Kasm VS NumPy and see what are their differences

Kasm

Kasm Workspaces is a workspace streaming platform for delivering browser, desktop, and application workloads to the web browser.

Rating
0 reviews
Pricing
Open source Freemium Free trial
Alternatives Is it good?
NumPy

NumPy is the fundamental package for scientific computing with Python

Rating
0 reviews
Pricing
Open source
Alternatives Is it good?
SetMe
Remote support software that just works. Securely access attended or unattended computers from anywhere. sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Which is more popular?

Based on our record, NumPy should be more popular than Kasm. It has been mentioned 122 times since March 2021.

social mentions
27 vs 122
Business & Commerce popularity
100% vs 0%
alternatives listed
129 vs 189

Base details

Website, pricing, platforms and company facts side by side.

Kasm
NumPy
Website trust.kasm.com numpy.org
Pricing
Open source Freemium Free trial Official pricing
Open source
Platforms
Linux Google Chrome Firefox Browser Docker Windows MacOS AWS Azure +6
—
Company 2019 —
Listed in
Office & Productivity Security & Privacy DaaS Cyber Security Web Browsers Browser Extensions Virtual Desktop Remote Desktop Remote Work +6
Data Science And Machine Learning Data Science Tools Python Tools Software Libraries +1

About Kasm and NumPy

In their own words, as submitted to SaaSHub.

Kasm
NumPy

Kasm is changing the way that businesses deliver digital workspaces using our open-source web-native container streaming technology to establish a modern devops delivery of Desktop as a Service (DaaS), application streaming, and browser isolation. Kasm is not just a service, it is a highly...

Read more about Kasm

No description of NumPy yet.

Features and specs

What each product offers, as listed by its team.

Kasm 5 features
NumPy 5 features
  • Security
    Kasm provides a secure environment by keeping web browsing and application usage isolated from the local machine, reducing the risk of malware and other security threats.
  • Ease of Deployment
    Kasm offers an easy deployment process, allowing organizations to set up and manage virtualized workspaces quickly.
  • Scalability
    The platform can scale according to the organization's needs, supporting a large number of simultaneous users.
  • Cross-Platform Compatibility
    Kasm supports access from various devices and operating systems, providing flexibility for users.
  • Centralized Management
    Administrators can easily manage and monitor users and workspaces from a centralized console, simplifying administrative tasks.
  • 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

  • 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.

Analysis

An editorial look at what each product does well and who it suits.

Kasm
NumPy

Overall verdict

  • Kasm is considered a good solution for organizations seeking secure, scalable, and flexible browser-based workspaces and application streaming. Its strong emphasis on security, combined with its user-friendly interface and compatibility, makes it a favorable choice for many businesses.

Why this product is good

  • Kasm is a platform that provides containerized workspaces for browser-based web desktops and application streaming. It offers features such as secure access, scalability, and flexibility, making remote work and web application deployment efficient and straightforward. It is particularly noted for its robust security and ease of integration with existing systems.

Recommended for

  • Organizations requiring secure remote access solutions
  • IT professionals looking for scalable deployment options
  • Businesses wanting to streamline application delivery through web-based platforms
  • Teams needing flexible workspace solutions with minimal infrastructure changes

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.

Videos

Walkthroughs and reviews on video.

Kasm 3 videos + Add
NumPy 3 videos + Add

Kasm Workspaces v1.15 Release

More videos

  • - Kasm Workspaces - Configuration and Platform Overview
  • - Kasm Workspaces - Overview

Learn NUMPY in 5 minutes - BEST Python Library!

More videos

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

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Kasm
NumPy
100% 100%
Business & Commerce
0% 0%
0% 0%
Data Science And Machine Learning
100% 100%
100% 100%
Backup & Sync
0% 0%
0% 0%
Data Science Tools
100% 100%

User comments

Share your experience with using Kasm and NumPy. For example, how are they different and which one is better?

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Reviews and articles

External articles and on-site reviews we used to compare the two products.

Kasm no reviews yet
NumPy no reviews yet

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

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Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Kasm 27 mentions
NumPy 122 mentions
  • I Want Everything Local – Building My Offline AI Workspace
    Playing with local LLMs is indeed fun. I use Kasm workspaces[0] to run a desktop session with ollama running on the host. Gives me the isolation and lets me experiment with all manner of crazy things (I tried to make a computer-use AI... - Source: Hacker News / about 1 year ago
  • Show HN: Lapdev, a new open-source remote dev environment management software
    Looks like Kasm Workspaces [1] for developers, I'll give it a try. [1] https://kasmweb.com/. - Source: Hacker News / over 2 years ago
  • Calibre Docker installation issue
    [migrations] started [migrations] no migrations found Usermod: no changes ─────────────────────────────────────── ██╗ ███████╗██╗ ██████╗ ██║ ██╔════╝██║██╔═══██╗ ██║ ███████╗██║██║ ██║ ██║ ... Source: almost 3 years ago

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Alternatives to Kasm and NumPy

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

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