Building a simple text search engine isn't that hard. People show them off on HN on a fairly regular basis. Most of those are fairly primitive. Unfortunately building a good search engine isn't that straightforward. There's more to it than just implementing bm25 (the goto ranking algorithm), which you can vibe code in a few minutes these days. The reason this is easy is because this is nineties era research that is all well publicized and documented and not all that hard once you figure it out.

Building your own search engine is a nice exercise for understanding how search works. It gets you to the same level as a very long tail of "Elasticsearch alternatives" that really aren't coming even close to implementing a tiny percentage of its feature set. That can be useful as long as you are aware of what you are missing out on.

I've been consulting companies for a few years with going from in house coded solutions to something proper (typically Opensearch/Elasticsearch). Usually people fight themselves into a corner where their in house solution starts simple and then grows more complicated as they inevitably deal with ranking problems their users encounter. Usual symptoms: "it's slow" (they are doing silly shit with multiple queries against postgres or whatever), "it's returning the wrong things" (it turns out that trigrams aren't a one size fits all solution and returns false positives), etc. Add aggregations and other things to the mix and you basically have a perfect use case for Elasticsearch about 10 years ago before they started making it faster, smarter, and better.

The usual arguments against Elasticsearch & Opensearch:

"Elasticsearch/Opensearch are hard to run". Reality, there isn't a whole lot to configure these days. Yes you might want to take care of monitoring, backups, and a few other things. As you would with any server product. But it self configures mostly. Particularly, you shouldn't have to fiddle with heap settings, garbage collection, etc. The out of the box defaults work fine. Get a managed setup if all this scares you; those run with the same defaults typically. Honestly, running postgres is harder. There's way more to configure for that. Especially for high availability setups. The hardest part is sizing your vms correctly and making sure you don't blow through your limits by indexing too much data. Most of your optimizations are going to be at the index mapping level, not in the configuration.

"It's slow". That depends what you do and how you use it. Most of the simple alternatives have some hard limitations. If you under engineer your search (poor ranking, lots of false positives) it's probably going to be faster. That's what happens if you skip all the fancy algorithmic stuff that could make your search better. I've seen all the rookie mistakes that people make with Elasticsearch that impact performance. They are usually fairly easy to fix. (e.g. let's turn off dynamic mapping and not index all those text fields you never query on that fill up your disk and memory and bloat your indexing performance ...).

"I don't need all that fancy stuff". Yes you do. You just don't know it yet because you haven't figured out what's actually needed. Look, if your search isn't great and it doesn't matter, it's all fine. But if search quality matters and you lose user's interest when they fail to find stuff in your app/website it quickly can become an existential problem. Especially if you have competitors that do much better. That fancy stuff is what you would need to build to solve that.

Unless you employ some hard core search ranking experts, your internally crafted thing is probably not going to be great. If you can afford to run at ~2005 era state of the art (Lucene existed, SOLR & Elasticsearch did not, Lucene was fairly limited in scope), then go for it. But it's going to be quite limited when you need those extra features after all.

There are some nice search products out there other than Elasticsearch & Opensearch that I would consider fit for purpose; especially if you want to do vector search. And in fairness, using a search engine properly still requires a bit of skill. But that isn't any different if you do things yourself. Except it involves a lot less wheel reinvention.

There just is a bit of necessary complexity to building a good search product.