A Step-by-Step Guide to Hosting Your Fine-Tuned LLM on a Website
Z
Zack Saadioui
8/12/2025
So You've Fine-Tuned an LLM. Now What? Your Step-by-Step Guide to Hosting It on a Website
Alright, so you’ve done the hard part. You’ve spent countless hours, or maybe just a few with some of the newer tools, fine-tuning a Large Language Model (LLM). It’s smart, it’s specialized, & it’s ready to do some amazing things. But here's the thing about a brilliant LLM—if it’s just sitting on your local machine, it’s not really doing anyone any good. It’s like a genius with no one to talk to.
To UNLEASH its potential, you need to get it on a website where users can interact with it. This is where things can get a little fuzzy for a lot of people. The path from a saved model file to a live, interactive web application isn't always super clear. But honestly, it's more accessible than you might think.
We're going to break it all down, step-by-step. We'll start with the easier, more managed options & then get into the nitty-gritty of doing it all yourself. Think of this as your roadmap from "model saved" to "app deployed."
First Things First: What Does "Hosting" an LLM Even Mean?
Before we dive in, let's get on the same page. "Hosting" an LLM means putting your fine-tuned model on a server or cloud infrastructure so it can be accessed over the internet. This usually involves a few key pieces:
The Model: Your fine-tuned weights & architecture.
An API: A way for other applications (like a website) to send requests to your model (e.g., "translate this text") & get a response.
The Server: The computer (or virtual computer) where your model & API live.
A Frontend: The actual website or user interface that people will interact with.
The goal is to connect all these pieces so that a user can type something into a box on a website, have that input sent to your model for processing, & see the model's output displayed back to them.
Option 1: The "Let's Get This Done Fast" Approach - Managed Inference Endpoints
Honestly, if you're not a DevOps expert or you just want to get your model up & running with minimal fuss, managed services are your best friend. These platforms handle all the messy infrastructure stuff for you.
Hugging Face Inference Endpoints: The Crowd Favorite
If your model is on the Hugging Face Hub (which is a pretty common practice), their Inference Endpoints service is a game-changer. It’s a managed service that lets you deploy your model with just a few clicks.
Here's the gist of it:
Find Your Model: Go to your model's page on the Hugging Face Hub.
Click Deploy: You'll see a "Deploy" tab. Click it & select "Inference Endpoints."
Configure: You'll need to choose a cloud provider (like AWS or Google Cloud), select the right hardware (they usually recommend a GPU setup based on your model's size), & configure some basic settings. They even have options for auto-scaling, which means if your app suddenly gets a lot of traffic, it will automatically scale up, & when it's idle, it can scale down to zero so you're not paying for unused resources.
Launch! Once you create the endpoint, Hugging Face provisions everything for you & gives you a URL. That's your API endpoint.
Now you can send requests to this URL from your website using simple HTTP requests. It's pretty amazing how much complexity this abstracts away. You don't have to worry about server maintenance, security, or scaling. It's a serverless approach, which is perfect for many projects.
Other Managed Platforms
Hugging Face is super popular, but other platforms offer similar one-click deployment services. MonsterAPI is another one that simplifies the process, especially if you fine-tuned your model directly on their platform. These services are all about speed & convenience.
Option 2: The "I Want More Control" Approach - Self-Hosting with a Web Framework
Maybe you want more control over your environment, or perhaps you want to keep costs as low as possible. In that case, you'll want to roll up your sleeves & host the model yourself. This usually involves creating a web application that wraps your model.
This is where things get a bit more technical, but it’s a super valuable skill to have.
Step 1: Building an API with Flask
The most common way to expose your model to the web is by building a simple API. Flask is a lightweight Python web framework that's PERFECT for this. It’s simple, flexible, & has a huge community.
Here’s a basic rundown of how you'd do it:
Set Up Your Project: Create a new project folder. It's a good idea to set up a Python virtual environment to keep your dependencies clean.
Install the Goods: You'll need to install Flask & a library for running your model, like