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ONNX-C

Lightweight C library for ONNX model inference optimized for performance and portability.

Status
Not yet determined
Last checked
Unverified
License
Open source
Pricing
Free tier · from $0
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What it is

ONNX-C is a lightweight C library designed to perform ONNX model inference with high performance and portability across various platforms. It is ideal for developers looking to integrate machine learning models into their applications without the overhead of larger frameworks.

ONNX-C stands out by offering a lightweight and portable C library specifically optimized for ONNX model inference, making it ideal for developers working in environments where resource efficiency and performance are critical.

When it fits — and when it doesn’t

A good fit for

  • Teams developing lightweight applications requiring high-performance ONNX model inference on resource-constrained devices.

  • Projects that need to integrate machine learning into embedded systems or IoT devices with minimal overhead.

Look elsewhere if

  • Applications requiring complex model training capabilities, as ONNX-C is focused solely on inference.

  • Developers looking for a full-stack framework with extensive features beyond model inference.

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Limited support for advanced ONNX features

    ONNX-C may not fully support all operators and functionalities available in the full ONNX specification, limiting its use for complex models.

  2. 02

    Small community and limited third-party integrations

    Due to its niche focus on C language and lightweight inference, ONNX-C may have fewer community contributions and less third-party tooling compared to larger frameworks like TensorFlow or PyTorch.

  3. 03

    Debugging and error handling can be cumbersome

    As a low-level C library, debugging issues and understanding error messages may require deeper knowledge of both the ONNX model and C programming.

What it does well

  • Lightweight and portable C library for ONNX model inference.

  • Optimized performance for various platforms.

  • Minimal dependencies, making it easy to integrate into existing projects.

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

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Set up ONNX-C

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →