This is a good distillation of what it actually takes to run Python at scale in 2026. The free-threaded execution model is finally starting to deliver real gains, but as you noted, it introduces new failure modes — race conditions, stale caches, and compilation latency — that weren’t as visible in the GIL era.
I’d add one more layer to the stack: observability. Without tracing and structured logging tied to each execution path, even the most optimized architecture becomes opaque under load. You can't fix what you can't see.
The shared memory pool pattern is especially underrated. If you're passing data between workers, the overhead of serialization/deserialization often exceeds the cost of the computation itself. Eliminating that step is a force multiplier.
Thanks for the write-up — it's a solid high-level map for teams moving from prototype to production in Python.
