Quality estimation (QE) of machine translation (MT), the task of predicting the quality of an MT output without human references, is particularly suitable in dynamic translation workflows, where translations need to be assessed continuously with no specific reference provided. In this paper, we investigate sentence-level neural QE and its applicability in an industry use case. We assess six QE approaches, which we divide into two-phase and one-phase approaches, based on quality and cost. Our evaluation shows that while two-phase systems perform best in terms of the predicted QE scores, their computational costs suggest that alternatives should be considered for large-scale translation production.