In this paper, we investigated the task of hate-speech classification in the closely related Turkic language pair, Turkish-Azerbaijani. Transformer models can achieve strong hate-speech classification in Turkish, but their performance does not reliably transfer to closely related low-resource languages without careful evaluation. We study Turkish–Azerbaijani hate speech detection and introduce the first manually annotated Azerbaijani benchmark, comprising 1,112 YouTube comments from major news channels with severe class imbalance. We compare XLM-RoBERTa and a compact BERT-Tiny model against a TF–IDF + logistic regression baseline under monolingual training, zero-shot Turkish→Azerbaijani transfer, low-resource balanced subsampling, bilingual mixed fine-tuning, and translation-based augmentation using machine-translated Turkish data. XLM-R attains high macro-F1 in Turkish and achieves moderate zero-shot transfer to Azerbaijani, but native Azerbaijani training is fragile for the hate class. Mixed bilingual training improves robustness for both languages, whereas TF–IDF generalizes poorly to Azerbaijani.