5 Free Ways to Host a Python Application

# Introduction

So, you are a student or someone who is just starting to learn the practical side of building applications. You've already taken the first step by building and testing your app locally. Now, you want to host it in the cloud so that it can be accessed from anywhere. The problem is that cloud hosting can feel complicated and expensive when you're just starting out.

In this article, we'll look at some of the simplest free platforms that allow you to host your own Python web or application programming interface (API) application without paying upfront. Although these services come with a limited computer, they are usually more than enough for a first toy project, a personal demo, or just trying to run, monitor, and manage basic applications.

# 1. Share AI Apps and Face-Huggable Spaces

Multiple Face Spaces is one of my favorite options for hosting Python applications, especially when working on artificial intelligence projects. It's very beginner friendly and makes deployment feel less intimidating. You can introduce a Gradio request by uploading your files, by pressing i Git you do, or use the Hugging Face command line interface (CLI).

It is particularly useful for machine learning and large-scale language modeling (LLM) projects, but it also supports Broadcast and Docker-based applications. That gives you some flexibility depending on how simple or custom your application is.

The free default hardware in Hugging Face Spaces gives you 2 CPU cores, 16 GB of RAM, and 50 GB of unlimited disk space, which is more than enough for most demos, prototypes, class projects, and small experiments.

One thing to keep in mind is that Spaces in the free CPU-basic tier will automatically sleep after 48 hours of inactivity, but they resume when someone visits the app again.

# 2. Use Data Applications with Public Cloud Broadcasting

Stream Public Cloud it was one of the first platforms I used when I learned how to use Python web applications. On the side Herokuit made the whole process sound so easy to understand. It's a great starting point for beginners because you can go from a local project to a live app without having to deal with a lot of setup.

Although many people still think of Streamlit as just a dashboard tool, it has become a versatile way to build data applications, internal tools, and lightweight interactive web applications in Python. The user experience is one of its greatest strengths because of you GitHub The repository acts as a source of truth, and the push to the repository is reflected in the application automatically.

For the free tier, Streamlit says all Public Cloud users share the same resource pool, with limits ranging from 0.078 to 2 CPU cores, 690 MB to 2.7 GB of memory, and up to 50 GB of storage. One important thing to know is that applications without traffic for 12 hours go to sleep, but they can be woken up again when someone visits the application.

# 3. Use Backend APIs with Render

Give is a very complete hosting platform that allows you to run all kinds of web applications, including Python, Node.js, Ruby on Rails, and Docker-based services. It's a solid choice if you want to host ia A flask or FastAPI backend without setting up servers yourself.

The shipping flow is very simple. You connect to a GitHub repository – although Render also supports it GitLab again Bitbucket – and the platform handles the build and deployment process for you. That makes it a very interesting way to find the Python API online.

Render offers a free class of web services, useful for testing ideas, hobby projects, and small demos. One important thing to know is that free web services go back down after 15 minutes of inactivity, and when someone visits again, the service can take a minute to wake up.

# 4. Run Python Applications Modally

Modal is one of my favorite modern platforms for Python programming, especially if the project is more advanced than a simple demo. I've used it for Model Context Protocol (MCP) backends, AI agents, and complex applications where I wanted something fast without having to manage the infrastructure myself. One of the best parts is that you define the infrastructure in Python, so the whole developer experience feels very natural if you're already working in the Python ecosystem.

It is highly robust to machine learning workloads, back-end operations, and back-end services. You can use Python functions, scheduled functions, and web endpoints, which makes it flexible enough for APIs, async processing, and model definition.

The free tier is great to get started. Modal's Starter plan includes $30 per month in free credits, along with limited web endpoints and cron jobs, usually enough for small tests, personal projects, and early prototypes.

# 5. Set up full Python applications on PythonAnywhere

Python Anywhere is one of the most popular hosting environments for Python. It feels very old school compared to the newer tools, but it still gets the job done. One of the reasons people keep coming back to it is that it was designed specifically for Python, so you can write code, manage files, open consoles, and run web applications from a browser without setting up your own server.

It's a great option for a simple Flask as well Django projects, especially if you want the whole environment in one place instead of connecting many different services. For beginners, that can make the learning curve feel much easier.

A free account is used for learning and small projects. Currently, free accounts include:

• One web application with one web worker.
• Two consoles.
• 512 MiB of disk space and 100 CPU seconds.
• Applications apply to a yourusername.pythonanywhere.com subdomain, and free accounts that limit outgoing Internet access.

# Wrapping up

Here's a quick side-by-side comparison to help you choose the right platform based on the type of Python program you want to use.

Abid Ali Awan (@1abidiawan) is a data science expert with a passion for building machine learning models. Currently, he specializes in content creation and technical blogging on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His idea is to create an AI product using a graph neural network for students with mental illness.