For me Medium, Product Hunt, Digital Ocean or even Twitch are not "toy sites".
Algolia is great a known-item searches. Searches that are like looking up a contact in your phone list. This is a common use case with search, but one of only many.
Your use case is closer to grasping at straws because you don't know the language to use. You can't quite remember the name for something. This is also very common, and not handled well with purely string matching. Another example is in this blog article[1] where I talk about searching for "manual lawn mower" not realizing the right terminology is a "reel mower." Mapping vernacular is a hard problem, and I feel like Solr/ES while harder are better equipped for the problem.
(I also often find instant search distracting)
[1]http://opensourceconnections.com/blog/2014/06/10/what-is-sea...
I have not used Algolia much but that's not a particularly compelling selling point, most search engines tend to get it right if you know exactly what to query. The problem is this is, like, 1% of the difficulty of making a good search engine.
Great article by the way!
In addition to being good at known item searches, my pitch for Algolia would be
(1) it's Easier to perform relevance tuning. You'd be surprised how annoying the Solr/ES defaults are. And the API is more advanced. (2) it has much better typo tolerance (again this gets at "known item" where you fat finger something)
site:producthunt.com mac voice control
Algolia comes with a lot of pre-defined tuning that are good for most use cases (see https://blog.algolia.com/inside-the-algolia-engine-part-3-qu... for more details).
That said there is always some tuning to have perfect result for a specific use case. There is no engine that provide perfect results out of the box without any tuning.
Further the more words you remove, the less constrained the results get possibly creating a lot of noise. Though I assume you remove based on document frequency, which helps.
This would be a problem with this strategy regardless of search engine (ES or Algolia)
* mono & multi-words synonyms,
* typo-tolerance is compliant with synonyms,
* matching synonyms are highlighted,
* prefix search works also on synonyms (even on multi words).
* dedicated API endpoints (leveraging existing synonyms but also Algolia's "alternative corrections" and "placeholders")
* new edition UI (with search & filtering capabilities)
* one/bi direction synonymsThis is actually an interesting dividing line. As you get more sophisticated modeling fuzzier concepts over just synonyms (synonyms are just one tool for this) then I think you get out of algolias sweet spot. When text pushed into a feature space (which happens quite a lot when you're mapping vernaculars) you begin to gain more from Solr and ESs depth of customizability.