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Makerkit.dev VS NumPy

Compare Makerkit.dev VS NumPy and see what are their differences

Makerkit.dev

MakerKit is a SaaS Starter Kit for Next.js/Tanstack Start and with Supabase/Drizzle/Prisma variants. Build unlimited SaaS products in record time with the best SaaS Boilerplate.

Rating
0 reviews
Pricing
Free $349 / One-off
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?
PromptBrake
Automated AI security testing for agents, chatbots, and APIs. Find prompt injection, data leaks, and unsafe behavior before launch. 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 a lot more popular than Makerkit.dev. While we know about 122 links to NumPy, we've tracked only 2 mentions of Makerkit.dev.

social mentions
2 vs 122
Developer Tools popularity
100% vs 0%
alternatives listed
63 vs 189

Base details

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

Makerkit.dev
NumPy
Website makerkit.dev numpy.org
Pricing
Free $349 / One-off Official pricing
Open source
Company Startup from Singapore · 1 - 9 employees · 2022 —
Listed in
Developer Tools Boilerplate SaaS Starter Kit Templates Nextjs +2
Data Science And Machine Learning Data Science Tools Python Tools Software Libraries +1

About Makerkit.dev and NumPy

In their own words, as submitted to SaaSHub.

Makerkit.dev
NumPy

Makerkit is a production-ready SaaS starter kit built with Next.js App Router and Supabase that helps developers launch faster. It provides a robust foundation with built-in authentication, team management, billing integration, and Super Admin - all powered by a modular architecture that makes...

Read more about Makerkit.dev

No description of NumPy yet.

Features and specs

What each product offers, as listed by its team.

Makerkit.dev 8 features
NumPy 5 features
  • Marketing Pages
    Landing page, pricing, FAQ, and other marketing pages included
  • Blog and Documentation
    Full-featured blog/documentation system with CMS integration
  • Authentication
    Complete auth system with email, OAuth, and MFA support
  • Billing
    Integrated payment system with Stripe and Lemon Squeezy support
  • Super Admin
    Admin dashboard to manage users, subscriptions and content
  • Translations (i18n)
    Multi-language support
  • Organizations/Teams
    Team management with roles and permissions system
  • Plugins
    Non-core functionality included as plugins: Testimonials, Roadmap, AI Chatbot, Waitlist
  • 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.

Makerkit.dev
NumPy

Overall verdict

  • Makerkit.dev is a solid, well-built SaaS starter kit that helps developers skip weeks of boilerplate setup by providing production-ready authentication, billing, and multi-tenancy features out of the box.

Why this product is good

  • Provides pre-built, production-ready SaaS boilerplate covering authentication, subscriptions, and team/organization management
  • Supports popular modern stacks like Next.js, Remix, Supabase, and Firebase
  • Saves significant development time by eliminating repetitive setup and configuration work
  • Comes with documentation, active maintenance, and community support
  • Includes billing integration with providers like Stripe and Lemon Squeezy
  • Built with TypeScript and modern best practices for maintainable, scalable code

Recommended for

  • Solo developers and indie hackers looking to launch a SaaS product quickly
  • Startups wanting to validate ideas without building infrastructure from scratch
  • Development teams needing a reliable, well-structured foundation for multi-tenant apps
  • Developers already familiar with Next.js, Remix, Supabase, or Firebase
  • Anyone wanting to avoid reinventing authentication and billing systems

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.

Makerkit.dev 0 videos + Add
NumPy 3 videos + Add

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

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
Makerkit.dev
NumPy
100% 100%
Developer Tools
0% 0%
0% 0%
Data Science And Machine Learning
100% 100%
100% 100%
Boilerplate
0% 0%
0% 0%
Data Science Tools
100% 100%

Questions & Answers

As answered by people managing Makerkit.dev and NumPy.

How would you describe the primary audience of your product?

Makerkit.dev's answer

Indie Hackers and Companies who want to launch quickly, without compromising on quality.

Which are the primary technologies used for building your product?

Makerkit.dev's answer

Makerkit uses Next.js 15 (App Router), Supabase, React.js, Typescript and Stripe.

What makes your product unique?

Makerkit.dev's answer

Makerkit stands out by offering a truly modular architecture built with Turborepo, where core features like auth, billing, and notifications live in their own packages for better maintainability.

While most starters lock you into specific patterns or providers, Makerkit gives you flexibility with a multi-account system supporting both B2B and B2C scenarios, provider-agnostic billing, and edge-ready deployment options.

Beyond the basics, it includes production-ready features like multi-factor auth, real-time notifications, and team permissions - all built with Supabase, TypeScript, React Query, and modern tooling to make development a genuine pleasure.

Why should a person choose your product over its competitors?

Makerkit.dev's answer

While other starters give you basic auth and a dashboard, Makerkit provides a genuinely modular foundation with the real features SaaS products need - like multi-factor auth, team permissions, real-time notifications, and provider-agnostic billing, all organized in clean, maintainable packages using Turborepo.

You get a first-class developer experience with TypeScript, React Query, and modern tooling, plus the flexibility to support both B2B and B2C scenarios, different payment providers, and edge deployment options.

Best of all, Makerkit is actively maintained with regular updates and responsive support, so you're building on a foundation that grows with your needs rather than painting yourself into a corner.

User comments

Share your experience with using Makerkit.dev 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.

Makerkit.dev no reviews yet
NumPy no reviews yet

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Social recommendations and mentions

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

Makerkit.dev 2 mentions
NumPy 122 mentions

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Alternatives to Makerkit.dev and NumPy

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