C++ vs Python: What to Choose for Backend in 2024

C++ vs Python: what to choose for backend?

Choosing a programming language for backend is one of the key questions every developer faces. The market is dominated by two fundamentally different approaches: high-level Python with its conciseness and low-level C++ with maximum control over resources. In this article, we will conduct an objective comparison and help you understand in which projects C++ becomes the only viable choice.

Language philosophy: development speed vs execution speed

Python was created as a language for rapid prototyping. Its syntax is as close to natural language as possible, and dynamic typing allows you to write code 3-5 times faster than in C++. However, this development speed comes at the cost of performance: the Python interpreter spends significant resources on memory management and dynamic type checking.

C++ is a language where the programmer is responsible for every detail: from manual memory management to choosing data containers. Modern compilers (GCC, Clang) generate machine code that can run 10-100 times faster than equivalent Python code. This is achieved through:

• No overhead from interpretation
• Direct access to hardware resources
• The ability to use SIMD instructions and multithreading at the kernel level

Backend performance: when every millisecond counts

Let's consider a typical backend task — processing HTTP requests with JSON. Let's write a simple parser in C++ using the nlohmann/json library:

#include <iostream>#include <string>#include <nlohmann/json.hpp>

using json = nlohmann::json;

int main() {    std::string raw_data = R"({        "users": [            {"id": 1, "name": "Alice", "age": 30},            {"id": 2, "name": "Bob", "age": 25}        ]    })";        json data = json::parse(raw_data);    int total_age = 0;        for (const auto& user : data["users"]) {        total_age += user["age"].get<int>();    }        std::cout << "Total age: " << total_age << std::endl;    return 0;}

This code compiles into a native binary that, when processing 10,000 requests per second, will consume 4-5 times less CPU than equivalent Python code. This is why C++ is chosen for:

• High-load systems — trading platforms, CDNs, game servers
• Real-time systems — telecommunications equipment, autonomous systems
• Microservices with strict SLAs — where latency must not exceed 1-5 ms

Example: high-performance HTTP server in C++

Modern C++ frameworks, such as Drogon or Pistache, allow you to create asynchronous servers with minimal overhead. Here is an example of a simple REST API using Drogon:

#include <drogon/drogon.h>

int main() {    drogon::app()        .registerHandler("/api/hello",            [](const drogon::HttpRequestPtr& req,               std::function<void (const drogon::HttpResponsePtr&)>&& callback) {                auto resp = drogon::HttpResponse::newHttpJsonResponse(                    json{{"message", "Hello from C++ backend!"}}                );                callback(resp);            },            {drogon::Get})        .addListener("0.0.0.0", 8080)        .setThreadNum(8)        .run();        return 0;}

This server can handle over 50,000 requests per second on a single core, which is unattainable for Python even with asynchronous frameworks (aiohttp, FastAPI). At the same time, C++ gives you full control over:

• Thread pools and coroutines
• Memory allocation (pools, arenas, stack allocators)