Yes, Python is Slow, but it doesn't matter for AI SaaS (fastro.ai)

The article argues that while Python is objectively slower than Rust or C++ on raw benchmarks, that speed difference rarely matters for AI SaaS. Typical AI-backed APIs spend almost all their time waiting on I/O—network latency, database reads/writes, and third‑party model calls—rather than CPU work. A breakdown shows Python orchestration taking ~1–5 ms versus I/O (DB + OpenAI) dominating at ~1,000–5,000+ ms (text generation 500 ms–3 s, image gen 10–30 s). In practice Python accounts for ~0.1–0.5% of end‑to‑end latency, so shaving Python exec time yields negligible user benefit. Modern async Python (FastAPI, async/await) handles concurrency well, letting a server overlap many I/O waits and avoid being limited by single-thread CPU speed. For engineers the takeaway is: profile and fix architecture, not language. Real performance killers are N+1 DB queries, missing indexes, lack of caching, blocking sync code, and unbatched API calls—these yield 10–100x improvements and are language-agnostic. Use Python to iterate fast and hire easily; when you truly hit CPU-bound hotspots (local model inference, heavy preprocessing, real‑time systems) either migrate specific components to Rust/Go or call performant native libraries (many Python libs already wrap Rust/C++). Choose based on measured bottlenecks and opportunity cost, not benchmark envy.