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Python vs. Java: Performance, Scalability, and Stability

Ofer Dekel

Product Manager, Intel Granulate
What Is Python? What Is Java? 
Python is a high-level programming language that abstracts many aspects from the programmer, such as memory management, process, pointers, and threads. Abstraction simplifies programming work and improves productivity.

Because Python has a higher level of abstraction, it is significantly slower than low-level languages like Java. However, there are various ways to optimize the performance of Python programs.
Java is a popular programming language, commonly used in enterprise software projects, and also for Android smartphone applications, edge devices, and Internet of Things (IoT) technology.

Java provides a similar experience to the C++ programming language, but is simpler to use. It has an object-oriented programming model and is suitable for creating complete applications that run on a single computer or distributed across clients and servers in a network.

This is part of our series of articles about Java performance.

In this article:

Python vs. Java: Head to Head


Comparing Java and Python is challenging because performance depends on the environment. The results may differ in unpredictable ways between applications written using object-oriented or functional programming. 

For example, the tests you run might prefer one language, but the software might perform differently in the real world. Additional factors to consider are the application libraries and programming style— this is especially important for Python development because it supports multiple programming paradigms.

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Different versions of the same language can also affect performance. In a real-world context, Java applications are often two or three updates behind, so it doesn’t make sense to compare the latest releases. Python 3.x is usually faster than 2.x, but many programmers prefer Python 2.x because of the libraries they use. The Python community is attempting to migrate to 3.x and is dropping support for version 2.

With both Java and Python, you compile bytecode and run it in virtual machines, making them useful ​​cross-platform languages. There are no operating system differences, so it would appear that both languages offer the same performance. However, there are some important differences between them:


  • Java uses a static typing syntax that makes compilation faster and easier, is less error-prone, and fits different platforms well.
  • Python uses dynamically typed syntax, so the code isn’t verbose enough for all platforms.


  • Java has a JIT compiler that compiles bytecode into a separate machine, allowing you to call the compiled code directly. This feature helps increase the language’s speed and efficiency. 
  • Python code is processed during the compilation.


  • Java code is often faster but is usually long and difficult to understand.
  • Python code is typically tested at runtime, meaning that issues in the code can slow down the whole application. Thus, Python has become a slower, less efficient language.

The differences between Python and Java can be significant. For instance, a binary tree test might run ten times slower in Python than in Java. However, performance is not a straightforward metric. The language that performs best depends on the environment, how you write the code, how you use it, how it runs, and how external factors like libraries influence it. 


Scalability is when an application can handle growing processing demands and overhead while expanding to handle changing workloads in real time. Java is more scalable than Python based on this definition:


  • Java is an object-oriented language, making it easy to scale with less computing overhead.
  • Python requires more overhead during runtime because it’s a scripting language, so the programs may be slower. 

Resource utilization:

  • Java follows stringent syntax rules like hard definitions for every variable. The result is a higher code volume and more lines you need to debug, but this syntax has benefits. For example, Java doesn’t require as many resources at runtime, enabling more scalable applications. 
  • Python lacks the strict coding syntax and multithreading of Java, so it cannot run parallel tasks to scale quickly. 


  • Java requires you to write code based on strict syntax rules with more variables. Thus, you need to create, check, and fix more code, but it also means that the software will likely be stable and less error-prone. 
  • Python programs are not unstable. But they are less frequently used in mission critical enterprise applications, and have a shorter track record for these uses.
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Is Performance Everything? 

If performance were the only consideration, everyone would write in Assembly or C. However, these languages are becoming outdated because their coding takes a long time. Performance might be your priority if the software must be fast to work properly. For example, video games, heavy video editing software, and operating systems require high performance. 

On the other hand, performance might be a lesser consideration when you need to build a minimum viable product (MVP) quickly. In this case, fast development is more important than fast execution. 

In some cases, speed is more important than precision. For example, you might write an operating system with Rust, not JavaScript, to handle heavy operations. 

Some fields, such as data science, require a programming language that handles data well, enabling fast, agile development. Python would be a better choice for such applications than a language that cannot handle large amounts of data as effectively (i.e., C). Other languages might have faster execution but require more development time and might be less precise than Python. 

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