Yes I know for sure. Postgres search is essentially an easier to use regex engine. If you have a recall-only use case and/or a small dataset, then that works great. As soon as you need multiple languages, advanced autocomplete, misspelling detection, large documents, large datasets, custom scoring, etc you need Solr or ES.
While I don't doubt that you know your usecase and weighed/tried the option.
> Postgres search is essentially an easier to use regex engine.
I'm not sure exactly what you meant to convey here, but if you're searching with LIKE or `~` you're not doing Postgres's proper Full Text Search. You should be dealing with tsvectors[0]
> As soon as you need multiple languages
Postgres FTS supports multiple languages and you can create your own configurations[1]
> advanced autocomplete
I'm not sure what "advanced" autocomplete is but you can get pretty fast trigram searches going[2] (back to LIKE/ILIKE here but obviously this is an isolated usecase). In the end I'd expect auto complete results to actually not hit your DB most of the time (maybe I'm naive but that feels like a caching > cache invalidation > cache pushdown problem to me)
> misspelling detection
pg_similarity_extension[3] might be of some help here, but it may require some wrangling.
> large documents, large datasets,
PG has TOAST[4], and obviously can scale (maybe not necessarily great at it) -- see pg_partman/Timescale/Citus/etc.
> custom scoring
Postgres only has basic ranking features[5], but you can write your own functions and extend it of course.
Solr/ES are definitely the right tools for the job (tm) when the job is search, but you can get surprisingly far with Postgres. I'd argue that many usecases actually don't want/need a perfect full text search solution -- it's often minor features that turn into overkill fests and ops people learning/figuring out how to properly manage and scale an ES cluster and falling into pitfalls along the way.
