Instead of ONNX Runtime
ONNX Runtime alternatives
ONNX Runtime focuses on inference: it runs models from frameworks such as PyTorch, TensorFlow/Keras, TFLite and scikit-learn, then optimizes execution through graph changes, accelerator partitioning and computation kernels. Its execution providers connect models to CPUs, GPUs, FPGAs and specialized NPUs. Deployment spans servers, edge and mobile devices, and browsers, and the runtime is free across desktop, mobile, web and self-hosted platforms. It also supports on-device training and custom web or mobile builds when package size matters.
- 1
Apache TVM
Free plan Open source AndroidAPIiPhone+5Compiling pretrained models for varied deployment backends — TVM compiles pretrained models into deployable modules, while ONNX Runtime focuses on optimizing and running inference.
Free plan Visit site - 2
Paperspace Gradient
Free plan Open source APIExtensionLinux+4Browser-based GPU notebooks and hosted model serving — Gradient combines collaborative GPU notebooks with API model deployments, unlike ONNX Runtime’s inference-engine focus.
Free plan · paid from $8/mo Visit site - 3
TensorFlow
Free plan AndroidAPIiPhone+5Building, training, and deploying models across environments — TensorFlow is an end-to-end model creation platform with distributed training and production tools, while ONNX Runtime centers on inference.
- 4
Ludwig
Free plan APILinuxMac+2Declarative multimodal model building and LLM fine-tuning — Ludwig configures model training and fine-tuning in YAML and exports to formats including ONNX, unlike ONNX Runtime’s inference focus.
- 5
MATLAB Grader
by MATLAB GraderFree plan LinuxMacWeb+1Instructors creating and grading MATLAB assignments — MATLAB Grader creates and automatically grades coding assignments, a teaching workflow distinct from ONNX Runtime inference.
Free plan Visit site - 6
MegEngine
Free plan AndroidiPhoneLinux+3Deep learning training and inference with one model — MegEngine combines automatic differentiation, training, and inference in one framework, whereas ONNX Runtime focuses on model execution.
- 7
tinygrad
Free plan LinuxMacSelf-hosted+2An end-to-end tensor, compiler, and training stack — tinygrad includes tensor operations, autodiff, a JIT, and training tools, while ONNX Runtime is an inference runtime.
- 8
fastai
Free plan LinuxMacSelf-hosted+2 - 9
Horovod
Free plan APILinuxMac+1 - 10
DeepSpeed
Free plan LinuxMacSelf-hosted - 11
JAX
Free plan LinuxMacSelf-hosted+1 - 12
Chainer
Free plan APILinuxMac+2 - 13
Lightning AI Studios
Free plan APILinuxMac+3 - 14
Kubeflow Trainer
Free plan APILinuxSelf-hosted - 15
MLX
Free plan iPhoneLinuxMac - 16
NVIDIA cuDNN
Free plan LinuxSelf-hostedWindows - 17
NVIDIA Triton Inference Server
Free plan APILinuxSelf-hosted+1 - 18
OpenVINO
Free plan LinuxMacWeb+1 - 19
MindSpore
Free plan LinuxMacSelf-hosted+1 - 20
NVIDIA TensorRT
Free plan LinuxSelf-hostedWindows - 21
Ray Train
Free plan LinuxMacSelf-hosted+1 - 22
Keras
Free plan LinuxMacWindows - 23
NVIDIA TAO Toolkit
Free plan LinuxSelf-hosted - 24
PaddlePaddle
Free plan APILinuxMac+2 - 25
PyTorch
Free plan AndroidAPIiPhone+4 - 26
Tarantella
Free plan LinuxSelf-hosted - 27
Determined
Free plan LinuxWeb
A different tool may suit you better if your priority is compiling models, creating them, training them, or teaching MATLAB rather than optimizing inference. TVM compiles pretrained models; TensorFlow and MegEngine cover model development as well as inference; Gradient adds hosted GPU notebooks and API serving. Ludwig and tinygrad offer broader model-building workflows, while MATLAB Grader is designed for coding assignments. Compare the model workflow, deployment targets and platform fit you need. For Windows projects, factor in ONNX Runtime’s guidance to use WinML for new projects because DirectML is in sustained engineering.
← Back to ONNX Runtime The whole deep learning software list