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this isn't really true. It was very expensive when it was first released but now it's pretty cost competitive with EC2, especially when you consider easier scale down/up.

Honestly the FSTs themselves were actually really cool, it's very much GOFAI. It automatically creates lots of permutations, i.e. `play taylor swift`, `please play taylor swift`, play taylor swift now`. etc. And once the FST is built it always works deterministically. It's compiled to a graph and an incoming command is pushed through the state machine, if you get to an end state it "matched the fst" and some specific behaviour would be triggered.

the rule were really just strings and we had efficient matching against it. I didn't work on that, I would assume some sort of LHS.

Throwaway, used to work at the NLU unit of Alexa about 5 years ago. There is some ML going on but as with all ML projects I have worked on people want control. This means you add rules for the "important" stuff. You also add test cases to make sure the ML works. But if you already have those test cases, why not just match on them directly? There are also advanced techniques for generating examples (FST for example).

What this culminated in is a platform where 80% of request, and pretty much 99% of "commands" are served by rules built with a team of linguists.