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I've (unfortunately) run scientific computing applications on aws. The experience is awful.

1 - aws is very very very expensive for sustained load.

2 - aws offers highly variable performance characteristics, both cpu and networking. It's a best practice after creating a set of ec2 machines to immediately spend 10 minutes perf testing them and dropping slow ones, either cpu or network.

2a - machines in aws that didn't start slow may become slow, particularly for networking. What you really want for many applications is a dedicated rack with very high speed TOR switches. You do not get this in AWS.

3 - Designing ML applications for variable tradeoffs between cpu and network is extremely ugly. Detecting and dealing with network links that can suddenly become extremely slow is awful.