Database LIKE queries stop being search at surprisingly small scale: they cannot rank results, do not handle typos or word variants, and slow down linearly as data grows. Elasticsearch is built for the actual problem — analysing text into terms, scoring relevance, and returning ranked results fast.
It also does aggregation well, which is what makes faceted filtering feasible across a large catalogue.
Why we use it
We use Elasticsearch where search is a primary feature over substantial data: product catalogues, document repositories, or any interface where finding is the main job.
For smaller datasets we prefer Meilisearch — far less operational weight for a similar experience. Elasticsearch earns its complexity at volume, with analytics needs, or where relevance must be tuned precisely.
Planning a Elasticsearch project?
We will tell you honestly whether it is the right choice for what you are building.
