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I didn't want to reply to the PyPy comment and be negative, but I haven't really gotten speedup from the few times I've tried PyPy. In fact I've generally gotten slowdowns. Definitely not 10x improvements. I'm not sure what the reason might be though, because the rest of the world seems to think differently.

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It's been a while since I used PyPy, but JITs in general warm up over time. If you have hot loops with heavy arithmetic and no branches, that's usually best case scenario for JITs. If you have branchy non-uniform control flow, that's the worse scenario. So it really depends on your usage - you may be paying the JIT overhead costs with little benefit.

Spot on, and the difference between a tracing JIT vs a method JIT can be night and day too.

Really depends on the kind of work you're doing. As mentioned above, numerical and looping code will see the most benefit. I used it heavily when doing Project Euler problems, and those would easily see 10-100x speedups, taking some problems from 5-10m run time to seconds.

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