  Recent advances in Natural Language Processing (NLP) have largely pushed deep
transformer-based models as the go-to state-of-the-art technique without much
regard to the production and utilization cost. Companies planning to adopt
these methods into their business face difficulties because of the lack of
machine, data, and human resources to build them. We compare both the
performance and the cost of classical learning algorithms to the latest ones in
common sequence and text labeling tasks. In our industrial datasets, we find
that classical models often perform on par with deep neural ones despite the
lower cost. We show the trade-off between performance gain and the cost across
the models to give more insights for AI-pivoting business. Further, we call for
more research into low-cost models, especially for under-resourced languages.
