Use one big server

… my providers single AZ SLA is less than my company's intended availability goals.

(IMO our goals are also nuts, too, but it is what it is.)

Our provider, in the worse case (a VM using a managed hard disk) has an SLA of 95% within a month (I … think. Their SLA page uses incorrect units on the top line items. The examples in the legalese — examples are normative, right? — use a unit of % / mo…).

You're also assuming a provider a.) typically meets their SLAs and b.) if they don't, honors them. IME, (a) is highly service dependent, with some services being just stellar at it, and (b) is usually "they will if you can prove to them with your own metrics they had an outage, and push for a credit. Also (c.) the service doesn't fail in a way that's impactful, but not covered by SLA. (E.g., I had a cloud provider once whose SLA was over "the APIs should return 2xx", and the APIs during the outage, always returned "2xx, I'm processing your request". You then polled the API and got "2xx your request is pending". Nothing was happening, because they were having an outage, but that outage could continue indefinitely without impacting the SLA! That was a fun support call…)

There's also (d) AZs are a myth; I've seen multiple global outages. E.g., when something like the global authentication service falls over and takes basically every other service with it. (Because nothing can authenticate. What's even better is the provider then listing those services as "up" / not in an outage, because technically it's not that service that's down, it is just the authentication service. Cause God forbid you'd have to give out that credit. But the provider calling a service "up" that is failing 100% of the requests sent its way is just rich, from the customer's view.)

You can see high efficiency setups like stackexchange & hackernews are orders of magnitude more efficient.

You've now scaled out, but you now have to ask each node when searching by secondary index. If you're asking every node for your queries, you haven't really scaled horizontally. You've just increased complexity.

Now, maybe 95% of your queries can be handled with a clustering key and you just need secondary indexes to handle 5% of your stuff. In that case, Cassandra does offer an easy way to handle that last 5%. However, it can be problematic if people take shortcuts too much and you end up putting too much load on the cluster. You're also putting your latency for reads at the highest latency of all the machines in your cluster. For example, if you have 100 machines in your cluster with a mean response time of 2ms and a 99th percentile response time of 150ms, you're potentially going to be providing a bad experience to users waiting on that last box on secondary index queries.

This isn't to say that Cassandra isn't useful - Cassandra has been making some good decisions to balance the problems engineers face. However, it does come with trade-offs when you distribute the data. When you have a well-defined problem, it's a lot easier to design your data for efficient querying and partitioning. When you're trying to figure things out, the flexibility of a single machine and much cheaper secondary index queries can be important - and if you hit a massive scale, you figure out how you want to partition it then.

I wonder how many of them violate GDPR and similar laws in other countries in regards to personal data processing by processing everything in the US.

Customers who have invested millions of dollars into making their stack multi-region, multi-cloud, or multi-datacenter aren't going to calmly accept the excuse that "AWS Went Down" when you can't deliver the services you contractually agreed to deliver. There are industries out there where having your service casually go down a few times a year is totally unacceptable (Healthcare, Government, Finance, etc). I worked adjacent to a department that did online retail a while ago and even an hour of outage would lose us $1M+ in business.