The most interesting bit here is not the “2.4x faster than Lambda” part, it is the constraints they quietly codify to make snapshots safe. The post describes how they run your top-level Python code once at deploy, snapshot the entire Pyodide heap, then effectively forbid PRNG use during that phase and reseed after restore. That means a bunch of familiar CPython patterns at import time (reading entropy, doing I/O, starting background threads, even some “random”-driven config) are now treated as bugs and turned into deployment failures rather than “it works on my laptop.”

In practice, Workers + Pyodide is forcing a much sharper line between init-time and request-time state than most Python codebases have today. If you lean into that model, you get very cheap isolates and global deploys with fast cold starts. If your app depends on the broader CPython/C-extension ecosystem behaving like a mutable Unix process, you are still in container land for now. My hunch is the long-term story here will be less about the benchmark numbers and more about how much of “normal” Python can be nudged into these snapshot-friendly constraints.