Python powers Netflix's recommendation engine, processes petabytes at Spotify, and handles financial calculations worth trillions of dollars. Yet, developers constantly debate whether Python is "too slow." This paradox reveals a fundamental misunderstanding about Python's true superpower: orchestration over computation. The real question isn't "Is Python fast enough?" It's "What should Python be doing?"

Many developers want to use Python when building high-performance systems, but often lack clear guidance on where Python is the right choice and where it may become a bottleneck. Without a decision framework, developers either prematurely rewrite working Python code in C++/Rust (adding complexity without proportional benefit) or keep everything in Python and hit performance walls, leading to systems that are neither fast nor maintainable. This talk cuts through the performance mythology to reveal where Python excels and when it struggles. 

By the end of this session, attendees will leave with a clear decision framework and practical architectural patterns they can immediately apply to their own projects. Whether you're evaluating an existing "slow" Python service or designing a new system from scratch, you'll have concrete tools to determine where Python belongs and where computation should move to compiled languages. You'll know how to structure these boundaries effectively and avoid the common pitfalls that add complexity without delivering real performance gains.

Key Takeaways:
1. Decision framework for orchestration vs. computation: Four-question evaluation to determine what belongs in Python versus compiled languages 
2. Three architectural patterns for integrating Python with high-performance code: When and how to use each pattern effectively (with code examples)
3. Anti-pattern recognition: Build intuition for justified complexity versus technical debt