I'm involved in a fairly large academic cloud deployment, sited in a 15MW data center built and shared by a few large universities.

There are huge advantages of scale to computer operations in a few areas:

- facility: the capital and running cost of a purpose-built datacenter is far cheaper per rack than putting machines in existing office-class buildings, as long as it's a reasonable size - ours is ~1000 racks, but you might get decent scale at a quarter of that. (also one fat network pipe instead of a bunch of slow ones)

- purchasing: unlike consumer PCs, low-volume prices for major vendor servers are wildly inflated, and you don't get decent prices until you buy quite a few of them.

- operations: people come in integer units, and (assuming your salary ranges are bounded) are only competent in small number of technical areas each. Whether you have one machine or 1000s you need someone who can handle each technology your deployment depends on, from Kubernetes to network ops; multiply 4x for those requiring 24/7 coverage, or accept long response times for off-hours failures.

That last one is probably the kicker. To keep salary costs below 50% of your total, assuming US pay rates and 5-year depreciation since machines aren't getting faster as quickly as they used to, you probably need to be running tens of millions of dollars in hardware.

Note that a tiny deployment of a few machines in a tech company is an exception, since you have existing technical staff who can run them in their spare time. (and you have other interesting work for them to do, so recruiting and retention isn't the same problem as if their only job was to babysit a micro-deployment)

That's why it can be simultaneously true that (a) profit margins on AWS-like services are very high, and (b) AWS is cheaper than running your own machines for a large number of companies.