Easiest database to scale is a pretty low bar. Databases are typically really hard to scale and Elasticsearch is no exception. Aside from the issue of ease, one thing that has been universally true for me is that Elasticsearch is incredibly expensive to scale in terms of compute costs.
Elasticsearch has built in horizontal scaling abilities, unlike Postgres/other SQL databases. It also has integrations with cloud providers for peer discovery, or can use DNS. Once a new data node is detected and reachable, the masters will start sending it shards of data, distributing the load. This all happens without any user intervention. I can't really speak to cost, it is somewhat easy to blow up the memory usage in Elastic for sure, but I can't say its been more expensive than similarly sized Postgres clusters.
Right, GB for GB ES is much easier to scale than Postgres (or any other DB) but probably also more expensive since ES is much more memory and compute hungry. But I can't say I have an apples-to-apples comparison since the use case for ES is usually "dump massive amounts of raw data in and index everything" which you wouldn't typically do with a Postgres instance. But in places where we have run large ES clusters my experience has not really been that it works without any user intervention (at least once you reach a certain scale) and that it involved a lot of operational support. Not that any other solution with comparable features would have been easier necessarily but still not easy in any absolute sense.
