BudgetML

BudgetML is an open-source Python library published on GitHub that deploys a machine-learning inference service on a budget in fewer than ten lines of code. The project has gathered around 1,300 GitHub stars.

Overview

Serving a model usually means hand-rolling a FastAPI app, writing a Dockerfile and wiring up cloud infrastructure. BudgetML collapses that into a tiny wrapper: point it at your trained model and a predict function, and it builds and deploys a production-style inference API for you, aimed squarely at cheap cloud instances rather than expensive managed endpoints. It is built on FastAPI, so the resulting service gets automatic request validation and interactive API docs, and it handles the packaging and deployment plumbing that side projects and prototypes usually skip. The code was last updated in 2024 and stays deliberately small — there is no platform lock-in, no control panel and no extra moving parts, just a fast path from a model file on your laptop to an HTTP endpoint your app can call.

Key Features

• Deploy an ML inference API in under ten lines of Python
• FastAPI-based service with automatic validation and docs
• Packaging and deployment handled for low-cost cloud instances
• Works with models from scikit-learn, PyTorch, TensorFlow and similar
• No platform lock-in — the generated service is yours to run anywhere
• Minimal, readable codebase that is easy to customise

💳 Pricing

BudgetML is free and open source under the Apache 2.0 license. You only pay for whatever inexpensive server you deploy to.

Who Is It For

Data scientists, students and indie developers who need to put a model behind an API quickly and cheaply without learning a full MLOps stack.