I don't have access to the original planning calculations anymore, but 375GB at $1520 would definitely have been a game changer in terms of performance/$, and I suspect be good enough to make the end user feel like the entire dataset was in memory.
These SSDs have situational uses but unless you want 10+ TB in one server you can get a system with >50% as much actual RAM for the same price.
The probabilistic hyperloglog data type is also a game changer compared to say redshift, but again it's only viable if you are dealing with counting (estimating) unique entities across billions of rows and super-wide dimension sets.
If you are doing a general purpose analytics store, Redshift is hard to beat because of reliability and ease of implementation.
Druid is a purpose-built race car. Redshift is a good cross-over - far less headache and can do almost any job good enough, but you won't have the tuning or performance (when tuned right) at scale. Although, I'm continuously impressed with what redshift actually can do, dispite the humble feature set.
Druid's main weakness is lack of SQL support, so it's not a great analyst datastore. You pretty much have to wrap it into a reporting app.
If I'm going to take on a similar project, I may POC memSQL or Citus DB, and possibly Big Query (if the project is built on Google Cloud as opposed to AWS or raw iron).