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NumPy VS KeptPDF

Compare NumPy VS KeptPDF and see what are their differences

NumPy

NumPy is the fundamental package for scientific computing with Python

Rating
0 reviews
Pricing
Open source
Alternatives Is it good?
KeptPDF

Redact, edit, OCR, and sign PDFs entirely in your browser. The file never leaves your device. Free, no account needed.

Rating
0 reviews
Pricing
Freemium $29 / Monthly (Pro, 1 user)
Alternatives Is it good?
you.bot
One API for 80+ AI models — LLM, image, video & music — priced up to 80% below the official APIs. Pay only for successful calls; failed runs refunded; credits never expire. 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 seems to be more popular. It has been mentioned 122 times since March 2021.

social mentions
122 vs 0
Data Science And Machine Learning popularity
100% vs 0%
alternatives listed
189 vs 20

Base details

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

NumPy
KeptPDF
Website numpy.org keptpdf.com
Pricing
Open source
Freemium $29 / Monthly (Pro, 1 user) Official pricing
Company — Startup from the United States · 2026
Listed in
Data Science And Machine Learning Data Science Tools Python Tools Software Libraries +1
PDF Tools Pdf Redaction OCR PDF Editor PDF Security & Privacy PDF Readers And Editors AI Tools +5

About NumPy and KeptPDF

In their own words, as submitted to SaaSHub.

NumPy
KeptPDF

No description of NumPy yet.

KeptPDF is a PDF toolkit that runs entirely in your browser. Nothing uploads: every tool runs on your own device, so the document never touches a server. What it does: Redaction that auto-detects names, SSNs, phone numbers, addresses, and dates, then removes the underlying text instead of drawing...

Read more about KeptPDF

Features and specs

What each product offers, as listed by its team.

NumPy 5 features
KeptPDF 5 features
  • 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.
  • Simple PDF Editing
    KeptPDF offers straightforward tools for editing, merging, splitting, and converting PDF files, making it accessible for users who need quick document management without a steep learning curve.
  • Web-Based Accessibility
    Being a web-based tool, KeptPDF can be accessed from any device with an internet connection, eliminating the need for software installation and allowing use across different operating systems.
  • Multiple File Format Support
    The platform supports conversion between PDF and various other file formats, offering flexibility for users who work with documents in different formats.
  • No Installation Required
    Since it operates in a browser, users can save storage space on their devices and avoid compatibility issues that sometimes arise with desktop software installations.
  • Quick Processing
    KeptPDF is designed to process PDF tasks efficiently, allowing users to complete common document tasks like compression or conversion in a relatively short amount of time.

Analysis

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

NumPy
KeptPDF

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.

No analysis of KeptPDF yet.

Videos

Walkthroughs and reviews on video.

NumPy 3 videos + Add
KeptPDF 0 videos + Add

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

No KeptPDF videos yet. You could help us improve this page by suggesting one.

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
NumPy
KeptPDF
100% 100%
Data Science And Machine Learning
0% 0%
0% 0%
PDF Tools
100% 100%
100% 100%
Data Science Tools
0% 0%
0% 0%
PDF
100% 100%

Questions & Answers

As answered by people managing NumPy and KeptPDF.

What makes your product unique?

KeptPDF's answer:

KeptPDF runs entirely in your browser. Your file, and the text inside it, never leaves your device. Not to an AI, not even to us. You can open your browser's network tab and watch: the document bytes never go out.

Most online PDF tools upload your file to a server first, and some "AI redaction" services quietly send your document text to a third-party model. That is the exact risk people are trying to avoid when they redact something.

KeptPDF also does true redaction. The text is removed from the file, not covered with a black box you can copy out later. Every redaction produces a verification certificate you can share with the file.

Why should a person choose your product over its competitors?

KeptPDF's answer:

Three reasons.

Privacy you can verify, not just a promise. Processing happens locally in the browser, so there is no upload step to trust. We do send anonymous usage counts for quota, and we say so plainly, but never your document.

Real redaction with proof. Removed text is permanently gone, and you get a certificate showing the file was checked for leftover extractable text. Automated detection cannot catch everything, so a final human review is still your job, and the tool says that too.

It works on a phone. Most PDF suites assume a desktop. KeptPDF was built and used on a phone first, so redacting a document while you are standing in a hallway actually works.

How would you describe the primary audience of your product?

KeptPDF's answer:

Anyone who has to hand a document to someone else and needs the sensitive parts gone first.

In practice that is solo attorneys and small law firms, accountants and tax preparers, healthcare and records staff handling requests, HR teams, and individuals dealing with their own medical, legal, or financial paperwork.

The common thread is not an industry. It is a person who cannot upload a confidential file to a random website, and who does not have an enterprise IT budget to solve it.

What's the story behind your product?

KeptPDF's answer:

A family member got seriously ill. We spent most days at the hospital, and straight answers were hard to come by, so we leaned on AI tools to make sense of the records, notes, and lab results.

But you cannot paste a medical record into an AI chat. You have to strip the names, the ID numbers, the diagnoses first. And almost every tool we found either wanted to upload the whole file to a server, or "auto-redacted" by sending the document text to an online AI. That was the exact thing we were trying to avoid.

Most of this was happening on a phone, at a bedside. So I built the tool I needed: redaction that runs on the device, works on mobile, and never sends the file anywhere. That turned into KeptPDF, which is now a full PDF suite with over 20 tools, all local-first.

Which are the primary technologies used for building your product?

KeptPDF's answer:

The app is plain JavaScript with no front-end framework, which keeps it fast and keeps the code auditable.

PDF work happens in the browser using pdf.js for rendering and pdf-lib for writing. Text recognition uses Tesseract running as WebAssembly. Password and encryption handling uses a WebAssembly build of qpdf. It is a Progressive Web App, so it installs and works offline.

The thin server side is Node on Vercel, with Postgres for accounts, Stripe for billing, and Resend for email. None of those ever see a document.

Who are some of the biggest customers of your product?

KeptPDF's answer:

KeptPDF is early and independent, and we do not publish customer names. The product is privacy-first by design: we never see your documents, and we do not track who our users are or what they work on. Publishing a client list would sit badly next to that.

The user base today is mostly solo attorneys, small firms, accountants, and individuals handling their own records.

User comments

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Reviews and articles

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

NumPy no reviews yet
KeptPDF no reviews yet

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

Social recommendations and mentions

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

NumPy 122 mentions
KeptPDF 0 mentions

View more

Tracking KeptPDF since Aug 2026.

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