BudgetML is ideal for practitioners who want to quickly deploy models to an endpoint but don't want to waste time, money and effort figuring out how to do it end-to-end. BudgetML was created because it is difficult to find a way to quickly and inexpensively get a model into production. Cloud functions have limited memory and are expensive at scale. Kubernetes clusters can be overkill for a single model. Deploying from the ground up requires a data scientist to learn too many concepts, such as SSL certificate generation, Docker and REST, Uvicorn/Gunicorn servers, backend servers etc. BudgetML is the answer to this problem. It's supposed to be quick, easy, and developer friendly. It is not intended to be used as a fully-fledged, production-ready setup. It is a way to get a server running as quickly as possible at the lowest cost.