5 Free Ways to Host a Python Application

# Introduction

 
So, you are a student or someone just starting to learn the operational side of building applications. You have already taken the first step by developing and testing your application locally. Now, you want to deploy it to the cloud so it can be accessed from anywhere. The problem is that cloud hosting can feel complicated and expensive when you are just getting started.

In this article, we will look at some of the easiest free platforms that let you host your Python web or application programming interface (API) application without paying upfront. While these services come with limited compute, they are usually more than enough for a first toy project, a personal demo, or simply experimenting with deployment, monitoring, and basic application management.

# 1. Share AI Apps with Hugging Face Spaces

 
Hugging Face Spaces is one of my favorite options for hosting Python applications, especially if you are working on artificial intelligence projects. It is very beginner-friendly and makes deployment feel much less intimidating. You can launch a Gradio application just by uploading your files, pushing the Git commits, or even using the Hugging Face command line interface (CLI).

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

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

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

# 2. Deploy Data Apps with Streamlit Community Cloud

 
Streamlit Community Cloud was one of the first platforms I used when I was learning how to deploy Python web applications. Alongside Heroku, it made the whole process feel much easier to understand. It is a great starting point for beginners because you can go from a local project to a live application without dealing with too much setup.

Even though many people still think of Streamlit as just a dashboard tool, it has become a flexible way to build data applications, internal tools, and lightweight interactive web applications in Python. The deployment experience is one of its biggest strengths because your GitHub repository acts as the source of truth, and pushes to the repository are reflected in the application automatically.

For the free tier, Streamlit says all Community Cloud users share the same pool of resources, with approximate limits of 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 with no traffic for 12 hours go to sleep, but they can be woken up again when someone visits the application.

# 3. Deploy Backend APIs with Render

 
Render is a more complete hosting platform that lets you deploy all kinds of web applications, including Python, Node.js, Ruby on Rails, and Docker-based services. It is a strong option if you want to host a Flask or FastAPI backend without setting up servers yourself.

The deployment flow is very simple. You connect to a GitHub repository — although Render also supports GitLab and Bitbucket — and the platform handles the build and deployment process for you. That makes it a very beginner-friendly way to get a Python API online.

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

# 4. Run Python Apps with Modal

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

It is especially strong for machine learning workloads, background jobs, and backend services. You can run Python functions, scheduled jobs, and web endpoints, which makes it flexible enough for APIs, async processing, and model inference.

The free tier is quite generous for getting started. Modal’s Starter plan includes $30 per month in free credits, along with limited web endpoints and cron jobs, which is usually enough for small experiments, personal projects, and early prototypes.

# 5. Host Full Python Apps on PythonAnywhere

 
PythonAnywhere is one of the most well-known platforms for Python hosting. It does feel a bit more old-school compared with newer tools, but it still gets the job done. One reason people keep coming back to it is that it is built specifically for Python, so you can write code, manage files, open consoles, and deploy a web application from the browser without setting up your own server.

It is a good option for simple Flask and Django projects, especially if you want an all-in-one environment instead of connecting lots of separate services. For beginners, that can make the learning curve feel much easier.

The free account is genuinely usable for learning and small projects. Right now, free accounts include:

• One web application with one web worker.
• Two consoles.
• 512 MiB of disk space and 100 CPU seconds.
• Applications run on a yourusername.pythonanywhere.com subdomain, and free accounts have restricted outbound internet access.

# Wrapping Up

 
Here is a quick side-by-side comparison to help you choose the right platform based on the kind of Python application you want to deploy.

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs 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 vision is to build an AI product using a graph neural network for students struggling with mental illness.