This paper presents a study on the deployability of named-entity recognition (NER) models on-device. It compares the accuracy, cost, reliability, and confidence of nine systems across three paradigms, including classical taggers, bidirectional-encoder specialists, and generative LLMs. The study finds that encoder-based models are more deployable due to their lower size, latency, and malformed output rates compared to generative models.