cONNXr

What it is

cONNXr is an ONNX runtime written in pure C99, designed to run inference on machine learning models across various frameworks. It's ideal for developers working with constrained hardware due to its minimal footprint and compatibility with older devices.

cONNXr stands out as a pure C99 implementation with zero dependencies, making it uniquely suited for environments where resource constraints are critical.

When it fits — and when it doesn’t

Known weaknesses

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

1. 01

   Limited language support

   cONNXr is primarily written in C99 and lacks native bindings or extensive documentation for other languages, making it less accessible to developers not familiar with C.

2. 02

   Complex setup process

   The tool requires manual configuration of the ONNX runtime environment which can be error-prone and time-consuming without detailed setup guides.

3. 03

   Performance may degrade with complex models

   While optimized for constrained hardware, cONNXr's performance can suffer when running more complex machine learning models due to its pure C99 implementation and lack of advanced optimization techniques.

4. 04

   Small community and limited support

   Being an open-source project with a niche focus, cONNXr has a relatively small user base which can lead to slower issue resolution and fewer contributions compared to larger projects.

What it does well

• Zero dependencies

• Pure C99 implementation

• Compatibility with older devices

• ONNX model inference support

• Minimal footprint

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.