Deploying large language models to fit a specific use case can be extremely challenging. Building a custom LLM offers advantages such as control, privacy, and customization, but comes with high costs of pre-training and technical expertise. Commercial models provide a cost-effective solution, offering the latest advancements in AI research and eliminating the need for large-scale training. Open-source alternatives offer flexibility and affordability, while optimization strategies like prompt engineering, fine-tuning, and context retrieval capabilities are crucial to achieving successful deployment. Deployment strategies require careful planning around latency, resource management, and security, with tools like MonsterAPI making it easier to deploy custom models in a single click. Continuous monitoring of the model's performance is critical to ensure its continued functionality and efficiency.