Think we have to be a bit more precise here. ArangoDB supports documents, key/value, and graph. It is not really optimized for large timeseries use cases which might need windowing or other features. Influx or Timescale might provide better characteristics here. However, for the supported data models we found a way to combine them quite efficiently.

Many search engines access data stored in JSON format. Hence integrating a search engine like ArangoSearch as an additional layer on top of the existing data models is no magic but makes a lot of sense. Allowing to combine models with search is then rather an obvious task for us.

Specialized databases have the advantage of being, well, specialized...

For example, a specialized OLAP database which knows about the schema of the data can employ much more streamlined storage and query operators, so it should have a "natural" performance advantage.

However, a very specialized database may later lock you in to something, and in case you need something different, you will end up with running multiple different special-purpose databases.

Not saying this is necessarily bad (or good), but it is at least one aspect to consider how many different databases to you want to operate & manage in your stack.