Python vs Kotlin

Python and Kotlin are both modern, expressive programming languages, but they come from different traditions and excel in different domains. Python is a dynamically typed, interpreted language that has become the dominant choice for data science, machine learning, scripting, and rapid prototyping. Kotlin is a statically typed, compiled language developed by JetBrains that has become the preferred language for Android development and is gaining significant traction in server-side and multiplatform development.

Python's greatest strengths lie in its simplicity and ecosystem. Its syntax is clean and readable, often described as "executable pseudocode." The language has an enormous standard library and an even larger ecosystem of third-party packages accessible through pip. For data science and machine learning, Python is unmatched: libraries like NumPy, Pandas, scikit-learn, TensorFlow, and PyTorch have no equivalents in most other languages. Python's interactive development experience through Jupyter notebooks has become the standard workflow for data exploration and scientific computing. The rise of AI and large language models has further cemented Python's dominance, as virtually all major AI frameworks are Python-first.

Kotlin was designed to address the pain points of Java while maintaining full interoperability with the existing Java ecosystem. It features null safety built into the type system (eliminating the notorious NullPointerException), concise syntax with features like data classes, extension functions, and destructuring declarations, and powerful coroutines for asynchronous programming. In 2019, Google declared Kotlin the preferred language for Android development, and since then, the vast majority of new Android apps are written in Kotlin. The K2 compiler, stabilized in Kotlin 2.0 (released in 2024), delivers significantly faster compilation times, improved type inference, and a more robust foundation for future language features.

In terms of type systems, the difference is fundamental. Python uses dynamic typing, where type checking happens at runtime. While Python 3.5+ introduced type hints and tools like mypy for optional static type checking, these are not enforced by the interpreter. Kotlin's static type system catches type errors at compile time, which is a significant advantage for large codebases where refactoring confidence and IDE-assisted navigation are important. Kotlin's type inference means that despite being statically typed, the code is often as concise as Python's.

Performance differs significantly between the two. Python has historically been one of the slower mainstream languages due to its interpreter overhead and the Global Interpreter Lock (GIL), which limits true parallel execution of threads. However, Python 3.13 introduced an experimental free-threaded mode (no-GIL) that enables true multi-threaded parallelism, a significant development for CPU-bound workloads. CPython also continues to receive performance improvements through the Faster CPython initiative. Still, Kotlin compiles to JVM bytecode and benefits from the JIT compilation of the HotSpot JVM, delivering performance comparable to Java, which is typically 10-100x faster than pure Python for CPU-bound tasks. Kotlin/Native can also compile directly to machine code for platforms where the JVM is not available.

For web development, both languages have strong frameworks. Python offers Django (batteries-included, opinionated) and FastAPI (modern, async, type-hint driven). Kotlin offers Spring Boot (the enterprise standard on the JVM, with first-class Kotlin support), Ktor (a lightweight, Kotlin-native async framework by JetBrains), and integration with the entire Java web ecosystem. Server-side Kotlin benefits from the JVM's mature tooling for profiling, monitoring, and deployment, including virtual threads (Project Loom) for highly concurrent applications.

Kotlin Multiplatform (KMP) is a distinctive capability that Python does not match. It allows sharing code between JVM (Android, server), iOS (via Kotlin/Native), web (via Kotlin/JS or Kotlin/Wasm), and desktop targets from a single codebase. KMP reached stable status in 2023, and adoption has grown rapidly, particularly among mobile teams that want to share business logic between Android and iOS applications without maintaining separate implementations.

For scripting and automation, Python remains the more practical choice. Its ubiquity on Linux and macOS, the vast library of packages for system administration, its dominance in DevOps tooling (Ansible, Salt), and the simplicity of writing quick scripts make it the go-to language for automation tasks. Kotlin scripting exists but has not achieved the same level of ecosystem support for these use cases.

The choice between Python and Kotlin often comes down to the domain. For data science, machine learning, AI, scientific computing, and scripting, Python is the clear winner due to its unrivaled ecosystem. For Android development, Kotlin is the definitive choice. For server-side development, both are excellent, with Kotlin offering better performance and type safety while Python offers faster prototyping and a lower barrier to entry. Notably, both languages benefit from strong open-source foundations and community-driven development, ensuring that developers are not locked into a single vendor's toolchain or platform. For teams already invested in the JVM ecosystem, Kotlin is a natural fit. For teams that value rapid iteration and breadth of library support, Python excels. Many organizations use both, choosing the right tool for each specific task.

Java, Go, Swift, Python, WASM