XGBoost is a highly efficient and flexible gradient boosting library designed to provide fast and efficient solutions for several data science and machine learning problems. It has quickly become the state-of-the-art machine learning algorithm for solving tasks with structured data due to its high speed and exceptional performance. Options for serving XGBoost models include cloud-hosted platforms, but these can be expensive and only work within their own ecosystems. Manually deploying machine learning models from concept to production is complex and time-consuming, so several frameworks are used to deploy XGBoost in production. In this article, two frameworks, Flask and Ray Serve, are highlighted as options for deploying XGBoost models. Flask is considered an exceptional deployment framework due to its ease of setup, efficiency with REST endpoints, and lack of dependencies on external libraries. However, it has drawbacks such as being unsuitable for large applications and lacking login and authentication capabilities. Ray Serve provides a simple web server that leverages complex routing, scaling, and testing logic necessary for production deployments, making it easier to scale out models on a multi-node cluster without changing the code. With Ray Serve, XGBoost models can be deployed successfully with just a few lines of code, allowing for high-efficiency and high-performance production deployments.