ONNX Runtime vs Weights & Biases
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A cross-platform library for running and training machine learning models quickly.
Open source · MIT
Python library to track, visualize, and manage machine learning experiments and models.
The short answer
Choose ONNX Runtime if you need…
- Developers who want to deploy a trained model inside an application or service
- Teams running the same model across Windows, Linux, macOS, or ARM devices
- Projects that need to speed up inference on available hardware accelerators
- Engineers with existing PyTorch training scripts who want to test the README's training acceleration
Choose Weights & Biases if you need…
- Machine learning engineers who want to compare training runs over time
- Researchers tuning hyperparameters across many experiments
- Teams that need to share experiment results with colleagues
- Developers using PyTorch, TensorFlow, or Keras who want tracking with minimal code changes
Side-by-side
| ONNX Runtime | Weights & Biases | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 22.2k | 11.3k |
| Setup difficulty | Intermediate | Beginner |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | C++ | Python |
| Last code update | 4 days ago | 4 days ago |
What each one is
ONNX Runtime
ONNX Runtime is a library that runs machine learning models on many kinds of hardware and operating systems. It supports models built in frameworks such as PyTorch and TensorFlow/Keras, as well as classical libraries such as scikit-learn, LightGBM and XGBoost. According to the README, it aims for faster performance and lower costs by using hardware accelerators and graph optimizations, and it also offers a training feature for transformer models on NVIDIA GPUs.
Weights & Biases
Weights & Biases (W&B) is a Python library and hosted platform for keeping track of machine learning work. You add a few lines to a training script to record hyperparameters and metrics like accuracy and loss, then view and compare those runs in a web dashboard. The README also describes covering the wider pipeline from datasets to production models, and offers cloud or self-hosted deployment.
Best for
ONNX Runtime
- Developers who want to deploy a trained model inside an application or service
- Teams running the same model across Windows, Linux, macOS, or ARM devices
- Projects that need to speed up inference on available hardware accelerators
- Engineers with existing PyTorch training scripts who want to test the README's training acceleration
Weights & Biases
- Machine learning engineers who want to compare training runs over time
- Researchers tuning hyperparameters across many experiments
- Teams that need to share experiment results with colleagues
- Developers using PyTorch, TensorFlow, or Keras who want tracking with minimal code changes
Strengths
ONNX Runtime
- Supports many hardware and operating system targets, according to the README
- Works with models from several major frameworks through the ONNX format
- Open source under the MIT license with active development and published releases
Weights & Biases
- Simple API: a few lines like wandb.init() and run.log() add tracking to existing code
- Broad framework integrations listed in the README
- Open source under the MIT license with cloud and self-managed hosting options
Not ideal for
ONNX Runtime
- People who only want to train or experiment with models in a notebook, since the README focuses on production inference and training acceleration rather than beginner workflows
- Readers without some programming background, because the README assumes familiarity with ML frameworks and model formats
Weights & Biases
- People who only need to run a single script once with no need for records, since the setup adds overhead
- Teams that cannot send data to a third-party service and do not want to self-host, because the default workflow uses the hosted W&B account
- Users looking only for LLM app monitoring, since the README points to a separate product called Weave for that
Trade-offs
ONNX Runtime
- The README is truncated and gives few concrete setup details, so getting started requires reading the linked documentation
- Performance benefits depend on hardware and model, and the README does not give specific numbers
- Training acceleration as described targets NVIDIA GPUs and transformer models
Weights & Biases
- Full value depends on the hosted W&B account and dashboard, which adds a service dependency
- The README is truncated, so details of offline use and data privacy controls are not covered here
- Some hosting options (Dedicated Cloud, Self-Managed) are described only briefly in the README
Getting started
ONNX Runtime
Clone the repository
git clone https://github.com/microsoft/onnxruntimeWeights & Biases
Install the wandb library
pip install wandbOptionally configure your API key on this machine
wandb loginFrequently asked questions
What is the difference between ONNX Runtime and Weights & Biases?
ONNX Runtime: A cross-platform library for running and training machine learning models quickly. Weights & Biases: Python library to track, visualize, and manage machine learning experiments and models. ONNX Runtime suits developers who want to deploy a trained model inside an application or service, while Weights & Biases suits machine learning engineers who want to compare training runs over time.
Are ONNX Runtime and Weights & Biases free?
ONNX Runtime: Free and open source. Weights & Biases: Free and open source.
Which is easier to set up, ONNX Runtime or Weights & Biases?
Weights & Biases is easier to get started with (rated beginner); ONNX Runtime is rated intermediate and usually needs more setup.
Is ONNX Runtime or Weights & Biases more popular?
ONNX Runtime has more GitHub stars (22.2k) than Weights & Biases (11.3k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to ONNX Runtime and Weights & Biases?
Other options people compare them with include Petals, Silero VAD, spaCy, PyG, Nano-vLLM.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.