Data pipeline engineers waste 40% of compute budget on interpreter overhead alone when using default Python runtimes—a cost that adds up to $12k/month for teams running 1k daily batch jobs. But the gap between Python 3.13’s new adaptive interpreter and PyPy 7.4’s mature JIT has never been narrower, or more confusing to navigate. Marketing claims from both camps contradict each other: PyPy promises 5x speedups, while CPython core contributors claim the new adaptive interpreter closes 80% of the JIT gap. We cut through the noise with 12 benchmark workloads, production case studies, and open-sourced code you can run yourself.

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