More importantly, "pre-cloud" means years ago and therefore older hardware, but also yes, software mostly isn't written to be high performance.
Modern NVMe drives get 1000x the performance of hard disks 10 years ago. You can buy one that can fit the entire reddit text database for $150 now. 10 years ago you'd be looking at a high six-figure SAN appliance from IBM or EMC to get the kind of performance my desktop has now. You can have TBs of RAM and 100+ cores in a server now. You can get 400 Gb/s networking now, and some people even have 10 Gb/s home internet. You could basically run some of the biggest sites from 10 years ago out of your closet these days.
Some software has also improved a lot in the last 10 years. Things like io_uring are great. Green threads are great. Postgres is super fast these days, and it keeps getting faster. My old quad core computer with a SATA drive can already do ~60-70k requests/second with a Scala web app and postgres. That's without even using graal or loom or trying to screw around with core affinity.
If anything, the cloud scales poorly. People in practice end up using vastly underpowered VMs, and then horizontally scale them, which introduces a ton of overhead (computationally and management-wise). RDS gets you like 3000 IOPS baseline and increasing that to the level of a single NVMe drive will cost one employee's firstborn child each month, so people end up with this mistaken belief that RDBMSs are slow or don't scale. AWS will provide you with reference architectures to use lambdas for web requests and advertise their "scalability" [0], but the API only lets you serve 1 request/lambda at a time, and according to their docs, you can only have up to "tens of thousands" of concurrent lambdas[1]. That would also require 10s of thousands of connections to your db, which kills it, and doesn't let you batch things unless you first put your work onto something like an SQS queue and have a separate db worker lambda pick up batches. More infra to manage (and more $$$) instead of writing a dozen lines of code to add an in-memory work queue, and you end up needing to write more code to deal with sending work/status across the system anyway. So my old i5 with an SSD ends up scaling better than their "well-architected" "scalable" serverless solution. AWS will happily give you plenty of this kind of advice that will lead to a slow, expensive, large system.
The one big upside of AWS is that if you do need to manage a lot of servers (like you are in the IoT space and need to handle millions of requests per second), they have good tools for doing that. Multi-region redundancy is also a click of a button if you need that. But they normalize overbuilding (and thus needing that management) way before it's necessary.
[0] https://docs.aws.amazon.com/wellarchitected/latest/serverles...
[1] https://docs.aws.amazon.com/lambda/latest/dg/gettingstarted-...
