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With caution advised when it comes to scaling. You might, or might not, pay a pretty hefty premium in the amount of hardware needed.

My experience has been that Python will give you more than enough rope to hang yourself with, and lots of apps that are meant to scale have some homegrown crap of an architecture, or people try to get fancy when they don't need to and try to write go or scala in Python.

If you have a clear understanding of your data model, Python backends scale quite well vertically. Run one instance of your app per core you have available and use IPTables to round robin (or whatever. you can get fancier if you feel like it) to each app instance. Boom. You are now banging on all 4 or 8 or however many cores your server has without the context switch overhead of the multiprocessing module and also without the headaches that go along with the shared memory space of the multithreading libs. Not to mention cleaner, nicer code.

If you build your product to work well in this way, then scaling horizontally also becomes a non-issue as far as Python code is concerned. You just put haproxy in front of multiple machines, set up health checks, and for the vast majority of use cases, you're done until you need to scale the storage horizontally. Then you have some serious decisions to make. But that's a different topic.

But back to my original point: every case where I've seen Python being all slow has really been an artifact of a crap architecture, or bolting on crap front end frameworks, or depending too much on an ORM like SQLAlchemy without really understanding your data and depending on it to do everything for you instead of thinking rationally about what your data is. Or some combination of all three above.

Yes, I know that there are languages that execute much faster than Python. I work with C#/.NET/SQL Server almost as much as I use the Python/Pyramid/PostgreSQL stack. I can write a crappy, slow, messy architecture on either stack. And in fact, when I was first getting to know C# and still writing Python code in that language, I did that.

The bottom line is that if you understand the strengths, weaknesses, and idioms of the language you are working with right now, almost every high-level language in common use today is sufficient for massive-scale deployments for the most common types of web apps. Heavy computing or scientific purposes obviously excepted. But most web apps that scale to hundreds of millions of users are basically "window-on-data" type applications.

I can't really think of a case where some other language besides Python would be noticeably cheaper hardware-wise.

Maybe you have to know your entire stack a bit better if you want scalable Python apps, maybe you have to do a little more Jiggery-Pokery with your Linux servers, but if you are serving at massive scale, you are going to have to go there anyway, especially when you start dealing with storage. That's part of the game.