AI applications rely on generative models, such as LLAMA3, Mistral, Deepseek, and Qwen2.5, which are pre-trained models on vast datasets to capture complex patterns and knowledge. These models serve as building blocks for various AI tasks, including natural language processing and image generation. To effectively deploy and manage AI applications, several services are needed to ensure the proper functioning of Large Language Models (LLMs). These services include quantization for resource optimization, inference servers for model execution, API core for load balancing, and observability for data collection and trace management. By fine-tuning and optimizing these models on specific datasets, their performance and accuracy can be enhanced for specialized tasks. This foundational step enables developers to leverage sophisticated models, reducing the time and resources required to build AI applications from scratch.