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What makes Python a poor choice for large-scale full-stack development? 1

What makes Python a poor choice for large-scale full-stack development?

Millions of developers prefer Python as a programming language; thus, it is one of the most loved open-source programming languages with a strong developer community. Still, some developers view Python as one of the anomalies of software development. They believe Python is strictly a “glue” scripting language more suitable for routine system automation or connecting two applications. That’s not true; Python as a programming language is powerful enough for industrial-strength software development.

When we look under the hood, Python has every quality that makes it suitable for several software development projects. It follows an object-oriented approach and is open-source. Also, hundreds of thousands of modules and libraries further extend the scope of what can be achieved with Python.

However, Python still fails to penetrate one computing segment, and that’s enterprise development. Python is still viewed as just a scripting language for enterprise-level software development.

This article assesses the shortcomings of Python that make it a poor choice for large-scale full-stack development.

1. Absence of GUI and teamwork tools

The primary reason Python may not be fit for full-stack development at an enterprise level is that it’s unnecessarily complex to create graphical user interfaces in Python. For GUI, Python is shipped with Tkinter, which is most often used by Python developers to create interfaces but is not capable enough to meet the requirements of large-scale projects fully. Modern software development is highly graphic, and lacking a proper GUI tool in Python puts it at a disadvantage.

Another reason why Python is not usually preferred for large-scale full-stack development is the lack of tools for teams to collaborate. In enterprise software development, this shortcoming is unacceptable and viewed as a near-fatal flaw. Programming languages like Java and C++ are preferred for enterprise-level full-stack development because they have dedicated tools to help teams collaborate while working on large-scale projects. It can be risky when working with multiple programmers and using a language with no such support tool. While Python addresses this issue using modules and namespace resolution to ensure no conflict within a multi-programmer environment, it is still not convincing enough for teams to include it in large-scale projects.

2. Database access layer limitations

Large-scale software development projects have substantial data requirements. Modern applications produce or consume data by terabytes, and such data management requires established technologies such as ODBC and JDBC. Unfortunately, Python’s database access layer is less robust than these technologies. It might not be understated to categorize Python’s DB layer as primitive and underdeveloped. This is again a significant flaw that makes Python unfit for any full-stack developer to use for enterprise-level applications.

Modern software development is highly agile, and organizations look for programming languages that can interact immediately and smoothly with complex legacy data or adopt robust and rapid SQL database access. Python doesn’t comply with these requirements, and as there are no words on it in the near term, it is not suitable for large-scale development stacks.

3. Python is slow at runtime

Another primary reason to avoid Python in your stack is slower runtime compared to other programming languages like PHP, JavaScript, C++, or Java. While it might not be a problem when you have a few hundred lines of code, it becomes a primary concern when a full-stack developer has to write giant programs with hundreds of thousands of lines of code.

Python is not very sensitive to hardware resources as a high-level programming language. Python code requires an interpreter instead of a compiler. As an interpreter compiles the code line by line, it is not difficult to assume how it can be a problem when you’re working with a giant code base. Another reason why Python is slow at runtime is that it’s a dynamically typed language. In dynamically typed languages, variable data types are not specified when we use any variable. The data type is assigned at the runtime. As a result, the interpreter checks the data type every time a variable is read, written, or referenced to allocate memory accordingly. It slows down Python code at runtime.

4. Inadequate documentation 

Suppose you have any experience working on large-scale projects. In that case, you understand how important it is to have comprehensive documentation available on technology before you decide to include it in your development stack. As compared to its rival programming languages, such as Java, Perl, and PHP, Python severely suffers from the lack of extensive documentation. Furthermore, there are not as many books available on Python. For comparison, PHP has twice as many titles as compared to Python on the market. While the online Python documentation is largely well organized and a good starting point, it only acts as sparse reference material. If your team does not have experienced coders, you can be stuck for a long time without enough learning resources for Python developers to help you fix issues.


While a powerful high-level programming language, Python has its limitations with large-scale full-stack development. Although the highly active Python community and dedicated development team across the globe are working on rectifying the limitations of Python to make it suitable for enterprise-level development, at present, it is better suited for small-scale software development projects.

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Satya Prakash Sharma

Satya Prakash Sharma

DevOps engineer at Talent500. Helping maintain security and infrastructure. Loves to develop applications. Lives for adventure!

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