I'd say it's more about elasticity. Scaling is just very narrow aspect of elasticity.

To give you some specific example, there's a company (that I won't name) that by law has to have all data on premises. They have local cloud in their own datacentre. Part of that cloud is a set of powerful servers with ton of GPUs. Daytime they spin up VMs that employees can log into and have remote desktop for graphically intensive tasks.

Now you might be thinking "wait a second, they can't easily add GPUs in the morning as employees log in, there is no scaling and thus no cloud!" And by that definition you'd be right. But what they do with their cloud is that as the demand for VDI drops in the evening, they will start allocating the GPU and CPU resources to completely different kind of VMs that do overnight data crunching. (think geospatial data) It's completely different OS, the servers are in server subnet, not VDI network, etc.. So they are using the elasticity, but it's not just scaling.

Another counterexample is pretty frequent issue on AWS, where they momentarily run out of specific instance type in specific region. AWS support "will do their best" but you're often looking at hours of wait time before you get your instance. Now depending where you live you could go buy a server and deploy it in your own DC faster than that. Has AWS stopped being cloud provider? No, you can use the elasticity and either spawn different instance type (if your workload allows that) or in different region/AZ. You might have been just trying to replace one instance with another, not even trying to scale up, it's just the capacity for replacement wasn't there.