There's a lot of edge cases. Since we spend so much time in this part of the tech stack compared to 10-15 years ago, I think it'd be similar to knowing Java v.

There's a lot of edge cases. Since we spend so much time in this part of the tech stack compared to 10-15 years ago, I think it'd be similar to knowing Java v. C++ 10-15 years ago. There's a lot of devil in those details (what was: what's your opinion on template metaprogramming in C++? Or how do you optimize garbage collection in Java? Or do you know Effective Java v. Effective C++? -- is now, how do you deal with cold starts in AWS Lambda? How do you represent infrastructure as code in a nimble way? When you're setting up a load balancer what kind of things get stuck? How do you ensure blue/green deployments are optimal?) If this part of the stack weren't so complicated for many sets of applications would there not be companies like Vercel that attempt to abstract these layers and make a lot of money on top of that? So yes the general patterns are similar. But everything about the implementation is different. And there are smooth parts (with ML colabs etc in GCP) in one, and smooth parts in others (QuickSight, OpenSearch integration, AWS SDK once you understand the building blocks, etc.).