ONNX Runtime vs spaCy
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 for processing and analyzing text, with pretrained models for 70+ languages.
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 spaCy if you need…
- Python developers building text-processing features into an application
- Data scientists who need fast, production-oriented NLP pipelines
- Teams that want to train or customize their own text classification or entity models
- Students learning practical natural language processing with a well-documented library
Side-by-side
| ONNX Runtime | spaCy | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 22.2k | 34k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | C++ | Python |
| Last code update | 4 days ago | 10 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.
spaCy
spaCy is a Python library that helps programs read and understand written text. It can split text into words, tag parts of speech, parse sentence structure, and recognize named entities such as people or places. It ships with pretrained pipelines and also supports training your own neural network models, which is why teams use it in real products.
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
spaCy
- Python developers building text-processing features into an application
- Data scientists who need fast, production-oriented NLP pipelines
- Teams that want to train or customize their own text classification or entity models
- Students learning practical natural language processing with a well-documented library
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
spaCy
- Built for production use, with a training system and model packaging described in the README
- Broad language coverage (70+ languages) and pretrained pipelines
- Released under the permissive MIT license
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
spaCy
- People who need a no-code tool; spaCy is a code library that requires Python skills
- Projects needing a language spaCy does not cover; the README cites support for 70+ languages, so check your specific language
- Those looking only for large chat-style language models; the README mentions LLM integration but the core focus is pipelines for NLP tasks
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
spaCy
- Requires Python programming knowledge; there is no graphical interface described in the README
- Training custom models and using transformers adds setup and compute requirements not detailed in this excerpt
- The README is truncated here, so detailed performance and hardware needs are not confirmed
Getting started
ONNX Runtime
Clone the repository
git clone https://github.com/microsoft/onnxruntimeFrequently asked questions
What is the difference between ONNX Runtime and spaCy?
ONNX Runtime: A cross-platform library for running and training machine learning models quickly. spaCy: Python library for processing and analyzing text, with pretrained models for 70+ languages. ONNX Runtime suits developers who want to deploy a trained model inside an application or service, while spaCy suits python developers building text-processing features into an application.
Are ONNX Runtime and spaCy free?
ONNX Runtime: Free and open source. spaCy: Free and open source.
Is ONNX Runtime or spaCy more popular?
spaCy has more GitHub stars (34k) than ONNX Runtime (22.2k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to ONNX Runtime and spaCy?
Other options people compare them with include Petals, Weights & Biases, Silero VAD, 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.