Feature vectors do tend to get incorporated in relevance tuning (regardless of the engine), but from what I've heard of Vespa, features (and ML in general) are first-class citizens, whereas with Elasticsearch and Solr, text statistics are your first-class citizens, and you're adding in additional features and integrating ML at the periphery.