Kotlin vs Python: What to Choose for Backend in 2024?

Kotlin vs Python: what to choose for backend?

Choosing a programming language for the backend is one of the key decisions that affects development speed, performance, and project scalability. For a long time, Python remained the undisputed leader due to its simplicity and huge number of libraries. However, in recent years, Kotlin — a modern language running on the JVM — has been attracting more and more attention. In this article, we will compare Kotlin and Python for backend development in detail, examine their strengths and weaknesses, and help you make an informed choice.

1. Performance and scaling

The first thing to consider is performance. Python, being an interpreted language, often lags in execution speed. Kotlin, running on the JVM (Java Virtual Machine), compiles to bytecode and uses JIT compilation, giving it a significant advantage in computational tasks.

Let's look at a simple example — calculating a factorial:

// Kotlin
fun factorial(n: Int): Long {
    return if (n == 0) 1 else n * factorial(n - 1)
}

fun main() {
    println(factorial(20)) // 2432902008176640000
}

Similar code in Python will run 5-10 times slower. For high-load systems where every millisecond matters (e.g., fintech, online games), Kotlin is preferable. Python, on the other hand, is great for prototypes, small projects, and scripts.

Additionally, Kotlin natively supports coroutines for asynchronous programming:

// Kotlin with coroutines
import kotlinx.coroutines.*

suspend fun fetchData(): String {
    delay(1000) // simulating a long request
    return "Data loaded"
}

fun main() = runBlocking {
    val result = async { fetchData() }
    println(result.await())
}

In Python, asyncio is used for asynchrony, which is harder to understand and less performant in some scenarios.

2. Type safety and code reliability

Python is a dynamically typed language. This speeds up writing code but increases the risk of runtime errors. Kotlin, on the other hand, is statically typed, allowing many bugs to be caught at compile time.

One of Kotlin's key features is null safety. In Python, you might accidentally get None where a value is expected, and this will cause the program to crash. In Kotlin, types are non-null by default:

// Kotlin — safe work with nullable
fun printLength(str: String?) {
    val length = str?.length ?: 0 // safe call with Elvis operator
    println("Length: $length")
}

fun main() {
    printLength("Hello") // 5
    printLength(null)    // 0
}

In Python, such a check needs to be written manually:

# Python
def print_length(s: str | None):
    length = len(s) if s is not None else 0
    print(f"Length: {length}")

For large projects with a development team, Kotlin's static typing significantly reduces the number of bugs and simplifies refactoring. Python with type annotations (typing) also becomes safer, but it still doesn't provide compiler guarantees.

3. Ecosystem and frameworks

Python has a huge ecosystem: Django, Flask, FastAPI — these are powerful frameworks for the backend. However, Kotlin is not far behind. The main frameworks are:

• Ktor — a lightweight asynchronous framework from JetBrains, ideal for microservices and REST APIs.
• Spring Boot — a mature framework with full Kotlin support. Used in enterprise projects.
• http4k — a minimalist framework for functional programming.

Example of a simple REST API in Ktor:

// Kotlin + Ktor
import io.ktor.server.engine.*
import io.ktor.server.netty.*
import io.ktor.server.routing.*
import io.ktor.server.response.*
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