Elasticsearch Agency

Elasticsearch pays off once your database full-text search hits its limits: large catalogues, many filters and the need for relevant results under load. We typically use it for product search, faceted filters and autocomplete, where plain SQL search becomes too slow or too imprecise. It is also a strong choice for observability and log analysis.

We start with an index blueprint: mappings, analyzers and the fields that genuinely need to be searchable or filterable. Then comes relevance tuning with synonyms and German stemming, so typos and word variants still return matching results. For filters and metrics we use aggregations instead of rebuilding that logic in the frontend.

Elasticsearch sits alongside your data source as a search index, not as a replacement for it. We build an ingest and data pipeline that writes catalogue or PIM data into the index idempotently, so updates stay consistent and no duplicates creep in. For Shopware and similar stacks we design the index to match your products, properties and variants.

Yes, that is a common scenario. We migrate from Solr, Algolia or a database full-text search with a clear cutover plan, so the old search stays live until the switch. Using a blue/green reindex and an alias switch we then cut over with no downtime and the option to roll back if anything goes wrong.

The main cost drivers are data volume, update frequency and the performance you need at peak, which is why we plan capacity, shards and memory upfront. In operation we set up monitoring and rely on patterns like blue/green reindex and alias switch, so reindexing runs without outages. For very small catalogues or rare searches a database search can be cheaper, and we will tell you that honestly.