Expose Local Ollama Models with an ASP.NET API

8/11/2025

Want to Run Your Own AI? Here's How to Expose Local Ollama Models with an ASP.NET API

Hey everyone, hope you're doing awesome. So, let's talk about something that's been a HUGE topic of conversation lately: running large language models (LLMs) locally. It's a game-changer, honestly. Instead of relying on cloud services, you can have these powerful AI brains working right on your own machine. This is massive for privacy, security, & just having full control over your data.

One of the coolest tools to emerge in this space is Ollama. It’s an open-source framework that makes it ridiculously easy to run models like Llama 3.1, Mistral, & others. No need for a Ph.D. in machine learning; you can get started pretty quickly.

But here's the thing: once you have Ollama running locally, how do you actually use it in your applications? How do you connect your own software to it? That's where a RESTful API comes in, & specifically for the .NET crowd, building one with ASP.NET Core is the way to go.

In this guide, I'm going to walk you through EVERYTHING you need to know to expose your local Ollama models via a RESTful API using ASP.NET. We'll cover the setup, the code, & the "why" behind it all. By the end, you'll have a working API that can talk to your local AI. Pretty cool, right?

First Off, Why Bother with a Local AI & an API?

Before we dive into the code, let's get on the same page about why this is such a powerful setup.

Privacy & Security: This is the big one. When you run a model locally, your data isn't being sent off to some third-party server. For businesses handling sensitive customer information, this is non-negotiable. It's your AI, on your hardware.
Cost-Effectiveness: Cloud-based AI services can get expensive, especially as you scale. Running models locally with Ollama eliminates that dependency. The only cost is your own hardware.
Flexibility & Control: You're in the driver's seat. You can choose which model to run, switch them out on the fly, & fine-tune them for your specific needs. The
1appsettings.json
file in your ASP.NET project will let you easily specify which model you want to use, like 'llama3.1' or 'qwen2.5-coder'. You can even see all your available models by running
1ollama ls
in your terminal.
Scalability: Building a RESTful API around your local model means you can scale your application efficiently. REST APIs are designed for this, handling multiple requests without needing a system overhaul.
Language Agnostic: The beauty of a REST API is that almost any programming language can talk to it. Whether you're building a web front-end in JavaScript, a mobile app in Swift, or another service in Python, they can all connect to your C#/.NET-powered AI backend.

What You'll Need to Get Started

Alright, let's get our hands dirty. Before we start building, make sure you have the following ready to go. Think of this as our pre-flight checklist.

.NET 9 (or later): The examples we'll use are based on modern .NET, so make sure your SDK is up to date.
Ollama Installed & Running: This is crucial. You need to have Ollama installed on your machine (macOS, Windows, or Linux). You can grab it from their official website. Once it's installed, you'll want to pull a model. A good one to start with is Llama 3.1. You can do this by running
1ollama run llama3.1
in your terminal.
An IDE: Visual Studio 2022 or Visual Studio Code are both excellent choices.
Docker (Optional but Recommended): While you can run Ollama directly, running it inside a Docker container is a clean way to manage it. The Ollama server typically runs on
1localhost
at port
111434
.
A REST Client: Something to test your API endpoints. Tools like Postman, Insomnia, or even the REST Client extension for VS Code are perfect. One tutorial even suggests a tool called Scalar, which is noted as being a bit more developer-friendly, similar to Postman.

Step 1: Setting Up Your ASP.NET Core Web API Project

First things first, let's create the foundation of our project. Open up your terminal or command prompt & run the following commands: