It’s the most tired argument in engineering. "Python can’t scale." "The GIL is a bottleneck." "Switch to Go or Rust for real performance."
Here is what people are missing: In 2026, developer velocity > millisecond execution.
I recently optimised a Flask-based AI inference engine on AWS. We didn’t switch languages. We changed the architecture:
- Async Evolution: We moved to the latest FastAPI/Pydantic V3 wrappers.
- AWS Lambda Powertools: Optimised cold starts by 40% using LLM-based caching.
- Database Bottlenecks: It’s almost never the Python code. It’s your unindexed MySQL queries and lack of connection pooling.
Execution is cheap. Engineering time is expensive.
Unless you are building a HFT platform, Python isn’t your problem. Your system design is.
Build f…
It’s the most tired argument in engineering. "Python can’t scale." "The GIL is a bottleneck." "Switch to Go or Rust for real performance."
Here is what people are missing: In 2026, developer velocity > millisecond execution.
I recently optimised a Flask-based AI inference engine on AWS. We didn’t switch languages. We changed the architecture:
- Async Evolution: We moved to the latest FastAPI/Pydantic V3 wrappers.
- AWS Lambda Powertools: Optimised cold starts by 40% using LLM-based caching.
- Database Bottlenecks: It’s almost never the Python code. It’s your unindexed MySQL queries and lack of connection pooling.
Execution is cheap. Engineering time is expensive.
Unless you are building a HFT platform, Python isn’t your problem. Your system design is.
Build for clarity first. Scale for performance second.
Are you still choosing languages based on "speed benchmarks" or "shipping speed"? Let’s debate in the comments. 👇