Software Alternatives, Accelerators & Startups

Plausible.io VS NumPy

Compare Plausible.io VS NumPy and see what are their differences

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.

Plausible.io logo Plausible.io

Plausible Analytics is a simple, open-source, lightweight (< 1 KB) and privacy-friendly web analytics alternative to Google Analytics. Made and hosted in the EU, powered by European-owned cloud infrastructure 🇪🇺

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Plausible.io Landing page
    Landing page //
    2020-07-07

Plausible Analytics is not designed to be a clone of Google Analytics. It is meant as a simple-to-use replacement and a privacy-friendly alternative that can help many site owners.

  • It's quick, simple to use and understand with all the metrics displayed on one page. Doesn't track hundreds of metrics like Google Analytics does

  • Lightweight script of less than 1 KB so sites load fast. The script is 45 times smaller script than the Google Analytics one

  • Doesn't use cookies so there's no need to worry about cookie banners

  • Doesn't track personal data so it's compliant with GDPR out of the box and you don't need to worry about asking for data consent

  • It's open source with the code available on GitHub so you can even self host exactly the same product free as in beer

  • Unlike Google Analytics, the cloud product is not free as in beer because the business model is subscriptions rather than selling the data of your visitors. Plausible Analytics is bootstrapped without any external funding so the subscription fees help cover the costs and time spent on development.

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

Plausible.io

$ Details
paid Free Trial $9.0 / Monthly (10,000 pageviews)
Platforms
Web Browser Google Chrome Firefox Safari Wordpress
Release Date
2019 April

Plausible.io features and specs

  • Privacy-focused
    Plausible does not collect personal data about your visitors and is fully compliant with GDPR, CCPA, and PECR.
  • Simple to Use
    The user interface is intuitive and easy to navigate, making it accessible for users without technical expertise.
  • Lightweight
    Plausible's script is under 1 KB in size, making it fast to load and reducing the impact on site speed.
  • Open-Source
    The platform is open-source, which allows for community contributions and transparency in how data is handled.
  • Real-Time Data
    Plausible provides real-time analytics, which can be useful for monitoring live events and activities on your site.
  • Affordable Pricing
    Offers competitive pricing models that can be more budget-friendly for small to medium-sized businesses compared to other analytics platforms.

Possible disadvantages of Plausible.io

  • Limited Features
    Lacks some advanced features found in more comprehensive analytics tools like Google Analytics, such as multi-channel funnels and detailed demographic information.
  • No Free Tier
    Plausible does not offer a free tier, which could be an obstacle for very small websites or individual users on a tight budget.
  • Basic Reporting
    The reporting may be too basic for larger enterprises that require more granular and customizable analytics.
  • No App Integration
    Currently, Plausible does not offer integrations with mobile app analytics, limiting its use to web applications.
  • Smaller User Base
    As a relatively new and smaller player in the market, it may not have the extensive user community or third-party support seen with more established platforms.

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.

Plausible.io videos

Cardano Blackboard Series #5: What is plausible deniability?

More videos:

  • Review - How Plausible is the Balkanized America from Crimson Skies? (A Map Analysis)
  • Review - Movie Review - How Plausible is The Martian?

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

Category Popularity

0-100% (relative to Plausible.io and NumPy)
Analytics
100 100%
0% 0
Data Science And Machine Learning
Web Analytics
100 100%
0% 0
Data Science Tools
0 0%
100% 100

User comments

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

These are some of the external sources and on-site user reviews we've used to compare Plausible.io and NumPy

Plausible.io Reviews

  1. Happy Paying User :)

    I've been using plausible since Sep 2019 and never had any doubts about it. It provides me with everything I need related to visitor stats while keeping privacy in first place.

    It doesn't slow down my website loading speed (it's amazing, it's less than 1KB in size!), is not blocked by adblockers since it's not really a tracker tracker, and owners are super cool and they actually respond to every inquiry you could possibly have.

    If you're looking for de-googling your stuff, you can start with Plausible :)

    🏁 Competitors: Google Analytics, Matomo, Woopra
    👍 Pros:    Loading speed|Clean ui|Privacy concisous|Custom domain|Affordable prices|Easy integration|Super simple
  2. Makis
    · Senior Software Engineer ·
    Plausibly simple analytics!

    I tried several analytics tools prior to Plausible, namely Google Analytics and later on Matomo. I found both to be fairly complicated for my usage which is a personal blog. Complicated in the way I had to install and use them. Plausible's simple to set up approach combined with a very clean and inviting user interface was a breath of fresh air. It's simple and clean enough that it actually makes me want to check and analyse my traffic which is a feeling I never thought I'd have having tried alternatives.

  3. Cesar Reyes
    · CEO at Reyes.Pro ·
    Excellent alternative to google analytics

    It offers clear information about what I really need, without distractions, without advertising and does not slow my site.

    🏁 Competitors: Google Analytics

Top 5 Plausible Analytics Alternatives in 2024
Looking for an excellent Plausible Analytics alternative? Read on as in this blog we will be exploring the best Plausible alternatives in 2024.
Source: www.putler.com
Top 9 Plausible Analytics alternatives in 2024
Plausible is an analytics platform focused on delivering clear insights into website traffic. By offering essential metrics like page views and referral sources, Plausible aids businesses in making informed decisions to optimize their online presence.
Source: usermaven.com
Top 5 Self-Hosted, Open Source Alternatives to Google Analytics
Use Case Example: An educational blog opts for Plausible to track user engagement metrics without impacting site performance or user privacy.
Source: zeabur.com
Top 5 open source alternatives to Google Analytics
Plausible is a newer kid on the open source analytics tools block. It’s lean, it’s fast, and only collects a small amount of information — that includes numbers of unique visitors and the top pages they visited, the number of page views, the bounce rate, and referrers. Plausible is simple and very focused.
Source: opensource.com
Privacy-oriented alternatives to Google Analytics
I learned about Plausible just recently, but they deserve to be on top of this list for me. Their platform is completely Open Source on GitHub under the MIT license. I personally also like that it’s written in Elixir.

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

Social recommendations and mentions

Based on our record, Plausible.io should be more popular than NumPy. It has been mentiond 200 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.

Plausible.io mentions (200)

  • 10 of the Best Web Analytics Tools for React Websites
    Plausible is a privacy-focused website analytics tool that provides simple, actionable insights into website traffic and visitor behavior. It prioritizes data privacy by offering transparent analytics without cookies, tracking scripts, or personal data collection. - Source: dev.to / 2 months ago
  • Top 10 European Open-Source Projects to Watch in 2025
    Perfect for companies running under tight EU privacy regulations. Find more: Plausible analytics. - Source: dev.to / 2 months ago
  • Meet Marko Saric, Co-founder of Privacy-friendly Plausible Analytics
    In this interview, Marko Saric shared his thoughts on privacy and running a bootstrapped SaaS business. Plausible integration is already available in Open SaaS as a privacy-friendly alternative to Google Analytics. We hope this interview helps you understand the value of such a product, and the nature of running an open source business. - Source: dev.to / 3 months ago
  • 5 Side Project Ideas for Developers to Monetize as Micro-SaaS in 2025
    Plausible Analytics (https://plausible.io/) is a lightweight, privacy-focused analytics tool that’s designed to be simple and easy to use. Unlike Google Analytics, Plausible gives you just the metrics you need—without the bloat. - Source: dev.to / 3 months ago
  • Umami is a simple, fast, privacy-focused alternative to Google Analytics
    It is not entirely clear who wrote these descriptions. Maybe it was not the vendor. At least their website https://plausible.io/ has a much better wording. > No need for cookie banners or GDPR consent. - Source: Hacker News / 3 months ago
View more

NumPy mentions (119)

  • 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 / 4 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 / 8 months 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. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 8 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 9 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 9 months ago
View more

What are some alternatives?

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

Google Analytics - Improve your website to increase conversions, improve the user experience, and make more money using Google Analytics. Measure, understand and quantify engagement on your site with customized and in-depth reports.

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

Fathom Analytics - Simple, trustworthy website analytics (finally)

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

Matomo - Matomo is an open-source web analytics platform

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