While LLMs are proficient in processing textual information, integrating them with other models presents significant challenges. This study evaluates the effectiveness of various configurations for integrating a large language model (LLM) with models capable of handling multimodal data.\ We explore the advantages of using pre-trained LLMs for generating text embeddings and the benefits of fine-tuning LLMs for specific tasks. Our investigation includes various fine-tuning strategies, such as Low-Rank Adaptation (LoRA), prompt tuning, and full fine-tuning, applied to both smaller and larger language models. Additionally, we analyze different training setups, including sequential and cascaded training of LLMs and downstream architectures. Our comparative analysis evaluates the performance and cost-effectiveness of these methods. The findings indicate that while full fine-tuning achieves the best results, LoRA offers a practical balance between computational efficiency and model performance. We also highlight the correlation between increased LLM size and corresponding increases in cost and performance.