How important is programming language performance in real life?
Simple benchmarks can be misleading. We analyze how differences change in complex architectures and what really matters in real projects.
When talking about programming languages, performance debates are common. Which language is faster—Python or Go? JavaScript or Rust? Benchmarks abound, often showing that in simple tasks some languages outperform others.
Simple benchmarks: numbers that mislead
It’s true: in small, well-defined algorithms, compiled languages like Go, Rust or C are usually much faster than interpreted ones like Python or JavaScript. These differences can be huge and are often the main argument for favoring one language over another.
Real systems: architecture’s impact
Modern systems are much more complex. In large architectures, whether monoliths or microservices, system performance rarely depends solely on the language’s execution speed.
Why?
- I/O, networks and databases: Most bottlenecks in complex systems appear when accessing external resources, not on the CPU.
- Parallelism and scalability: Scaling horizontally often has more impact than the underlying language.
- Global optimization: Architecture decisions, API design, caching, load balancing and other factors are usually more decisive for final performance.
In fact, well-designed systems written in “slower” languages (like Python) can perform on par with those in supposedly faster languages because infrastructure, database and network are the real limits.
What should we look at?
When choosing a language for a real project, consider:
- Ease of development and maintenance
- Ecosystem and library support
- Team productivity
- Ability to scale and monitor the system
- Previous experience and available talent
Raw speed matters but is rarely the only key factor in complex projects.