In this paper, we study the translation abilities of Large Language Models (LLMs) for business IT texts.We are strongly interested in domain adaptation of translation systems, which is essential for accurate and lexically appropriate translation of such texts.Among the open-source models evaluated in a zero- and few-shot setting, we find Llama-2 13B the most promising for domain-specific translation fine-tuning.We investigate the full range of adaptation techniques for LLMs: from prompting, over parameter-efficient fine-tuning to full fine-tuning, and compare to classic neural machine translation (MT) models trained internally at SAP.We provide guidance how to use training budget most effectively for different fine-tuning approaches.We observe that while LLMs can translate on-par with SAP’s MT models on general domain data, it is difficult to close the gap on SAP’s domain-specific data, even with extensive training and carefully curated data.