Survey: Which C++ Frameworks Are Chosen for Production in 2025

Top C++ Frameworks in Production: Results of a Fresh Survey

Which C++ libraries and frameworks are truly in demand in commercial development today? To answer this question, the PythonLib editorial team conducted a survey among 500 professional developers working on live projects. The results proved revealing and useful for choosing a technology stack.

Survey Methodology

Developers from Russia, the CIS, and Europe took part in the survey. Participants selected up to three frameworks that they actually use in a production environment over the last 12 months. "For fun" projects and pet projects were excluded.

Results: Who's on Top?

• Qt (84% of respondents) — the undisputed leader. Cross-platform GUI applications, embedded systems, and even backend (Qt WebEngine). Especially popular in fintech and industrial automation.
• Boost (71%) — the "Swiss Army knife" of C++. Used for serialization, networking, regular expressions, and smart pointers. Often serves as a foundation for custom frameworks.
• POCO C++ Libraries (43%) — a surprisingly high result. Compact, fast, and not functionally overloaded. Leads in IoT and microservices in C++.
• OpenCV (39%) — computer vision and image processing. Actively used in robotics, security, and medical systems.
• nlohmann/json (32%) — a simple and popular library for working with JSON. Chosen for its convenience and speed.
• fmtlib (28%) — string formatting, an alternative to std::format. Valued for performance and code readability.

What Else is in the Top 10?

• Crow (22%) — a lightweight web framework for REST APIs. Gaining traction in startups.
• Intel TBB (19%) — parallel computing. Used in high-load systems.
• SFML (15%) — multimedia and games. Surprisingly, it's used in simulators and data visualizations.
• libcurl (14%) — working with HTTP/HTTPS. The standard for network requests.

Trends and Conclusions

The main trend is a move away from monolithic frameworks towards a combination of small, specialized libraries. Developers are increasingly assembling a stack "for the task": Qt for graphics + POCO for networking + fmtlib for logging. This lowers the entry barrier and speeds up builds.

There is also a noticeable growing interest in C++20/23: built-in support for coroutines, ranges, and modules reduces the need to pull in external dependencies. However, time-tested solutions still dominate in production.

If you are choosing a framework for a new project — start with Qt or Boost as a base, add POCO for microservices and nlohmann/json for data. And for high-load systems — Intel TBB. This combination covers 90% of typical tasks.

The full list of survey participants and detailed statistics are available upon request from the PythonLib editorial team. Stay tuned — in the next article, we will break down each framework in detail with code examples.