Maybe. That one distinguishes itself from Elastic and hasn't set its sights on big data. > Elasticsearch is very powerful when it comes to massive datasets; it

Maybe. That one distinguishes itself from Elastic and hasn't set its sights on big data. > Elasticsearch is very powerful when it comes to massive datasets; it can quickly scale horizontally and allows one to build complex queries. The flipside of that is that nothing is easy. Even if all you need is simple search functionality that handles typos, synonyms, or filters, you're looking at hours of training, documentation, and configuration. Spending some hours learning a new tool doesn't seem so bad to me when I'll be working with it as part of my product. > For big tech companies dealing with enormous logs, Elasticsearch might make sense. But when it comes to medium-size datasets (i.e. less than five million rows) and smaller apps, it simply isn’t right for the job. Nonetheless, since no open-source alternative has historically been available, developers still go for Elastic as their default solution and wind up losing a disproportionate amount of time on setup and training. > MeiliSearch is not made to search through billions of large text files or parse complex queries. https://blog.meilisearch.com/why-should-you-use-meilisearch-over-elasticsearch/ https://blog.meilisearch.com/why-should-you-use-meilisearch-... I'm not sure I'd like to assume that my dataset will never grow. This is kind of like using training wheels. Some may find it helpful but I don't think it will grow to the widespread adoption Elastic has seen without a change of vision.