BudgetML is an ideal solution for practitioners who aim to swiftly deploy their models to an endpoint without investing excessive time, money, or effort in navigating the complexities of end-to-end deployment. Our motivation for creating BudgetML stems from the difficulty of finding a straightforward and economical method for quickly putting a model into production. Utilizing cloud functions often results in memory constraints and high costs when scaled, while Kubernetes clusters can be unnecessarily complex for deploying a single model. Starting from scratch demands familiarity with numerous concepts such as SSL certificate creation, Docker, REST APIs, Uvicorn/Gunicorn, and backend servers, which are typically beyond the expertise of an average data scientist. BudgetML addresses this issue by offering a solution that prioritizes speed, simplicity, and ease of use for developers. While it is not intended for comprehensive production environments, BudgetML serves as an effective tool for rapidly establishing a server at minimal costs. By streamlining the deployment process, BudgetML empowers data scientists to focus on their models rather than the intricacies of deployment infrastructure.