The emergence of Large Language Models (LLMs) has sparked significant interest in the field of Natural Language Processing (NLP). These models use deep learning techniques and vast amounts of data to understand, summarize, generate, and predict new content. LLMs possess remarkable abilities such as generating text that closely resembles human language, offering prompt answers to queries, and engaging in conversations. However, they often require fine-tuning to reach their full potential. Fine-tuning involves taking a pre-trained LLM and training it further on a smaller, task-specific dataset, refining its predictions and making it more specialized in delivering accurate results for specific use cases. Challenges associated with fine-tuning include complex setups, memory constraints, GPU costs, and the lack of standardized methodologies. MonsterAPI's LLM FineTuner addresses these challenges effectively by providing simplified setups, optimized memory utilization, low-cost GPU access, and standardized practices. The platform simplifies the intricate fine-tuning process, making it easy, scalable, and cost-effective for developers to tackle. With MonsterAPI's no-code LLM finetuner, users can effortlessly fine-tune a large language model like LLaMA 7B with DataBricks Dolly 15k for 3 epochs using LoRA, all while staying within a budget of less than $20.