Introduction
Artificial Intelligence is becoming a part of many modern applications, from chatbots and recommendation systems to image recognition and document processing. While many developers use cloud-based AI services, they are not always the best choice. Internet connectivity, API costs, latency, and data privacy can become challenges, especially for enterprise or offline applications.
This is where ONNX Runtime comes in. It allows you to run AI models directly inside your .NET application without calling external cloud APIs. This means your application can perform AI inference locally, resulting in faster responses, better privacy, and lower operational costs.
In this article, you'll learn what ONNX Runtime is, why it's useful, and how to use it in a .NET application with practical examples.
What Is ONNX Runtime?
ONNX Runtime is an open-source inference engine developed to run machine learning models in the Open Neural Network Exchange (ONNX) format.
Instead of depending on a cloud service, your application loads a trained model and performs predictions locally.
A typical workflow looks like this:
Train a machine learning model.
Export the model to the ONNX format.
Add the model to your .NET application.
Use ONNX Runtime to load the model.
Pass input data to the model.
Receive prediction results.
This approach works across Windows, Linux, macOS, and many edge devices.
Why Use ONNX Runtime?
Running AI models locally offers several advantages.
Some of the biggest benefits include:
No dependency on cloud APIs
Lower inference latency
Better data privacy
Reduced API costs
Offline support
Cross-platform compatibility
High-performance inference
These benefits make ONNX Runtime suitable for desktop applications, mobile apps, web APIs, IoT devices, and enterprise software.
Installing ONNX Runtime
The easiest way to get started is by installing the official NuGet package.
dotnet add package Microsoft.ML.OnnxRuntime
Once installed, your application can load and execute ONNX models.
Loading an ONNX Model
Loading a model requires only a few lines of code.
using Microsoft.ML.OnnxRuntime;
var session = new InferenceSession("sentiment-model.onnx");
The InferenceSession loads the model into memory and prepares it for inference.
In a production application, you should reuse the session instead of creating a new one for every request.
Running an AI Prediction
Once the model is loaded, prepare the input data and execute inference.
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
var input = new DenseTensor<float>(new[] { 0.8f, 0.2f, 0.6f }, new[] { 1, 3 });
var inputs = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor("input", input)
};
using var results = session.Run(inputs);
foreach (var result in results)
{
Console.WriteLine(result.Name);
}
In a real application, the input tensor contains the data expected by the model, such as image pixels, text embeddings, or numerical values.
Common AI Scenarios
ONNX Runtime supports a wide range of AI use cases.
Some common examples include:
Image classification
Object detection
Face recognition
Text classification
Sentiment analysis
Language translation
Speech recognition
Recommendation systems
Anomaly detection
Many pre-trained models are already available in the ONNX format, making it easier to integrate AI into existing applications.
Using ONNX Runtime in an ASP.NET Core API
A common approach is to expose AI predictions through an API.
app.MapPost("/predict", (PredictionRequest request) =>
{
// Prepare input tensor
// Run inference
// Return prediction
return Results.Ok();
});
Instead of calling a cloud AI service, the API processes the request locally using the ONNX model.
This reduces network latency and provides more consistent response times.
Performance Tips
ONNX Runtime is already optimized, but following a few best practices can improve performance even further.
Load the model only once during application startup.
Reuse the InferenceSession across requests.
Batch predictions when processing large datasets.
Avoid unnecessary memory allocations.
Use hardware acceleration when available.
Monitor CPU and memory usage in production.
These practices help applications scale efficiently under heavier workloads.
Limitations to Consider
Although ONNX Runtime is powerful, it is not the right solution for every AI project.
Keep the following in mind:
Models must first be converted to the ONNX format.
Some model features may not be fully supported after conversion.
Large models can consume significant memory.
Updating models requires deploying a new model file.
Training models still happens outside ONNX Runtime.
ONNX Runtime focuses on inference, not model training.
Best Practices
When building AI applications with ONNX Runtime, follow these recommendations:
Choose models that match your application's performance requirements.
Validate all input data before running inference.
Keep the model file outside your source code when possible for easier updates.
Reuse InferenceSession instances instead of creating them repeatedly.
Benchmark inference performance with realistic workloads.
Monitor resource usage in production environments.
Keep ONNX Runtime packages updated to benefit from performance improvements and bug fixes.
Conclusion
ONNX Runtime makes it easy to bring AI capabilities directly into .NET applications without relying on cloud APIs. By running models locally, developers can reduce latency, improve data privacy, lower operational costs, and support offline scenarios.
Whether you're building an ASP.NET Core API, a desktop application, or an edge solution, ONNX Runtime provides a fast and reliable way to perform AI inference. With the right model and a few performance best practices, you can integrate intelligent features into your applications while maintaining full control over your data and infrastructure.