ONNX

What ONNX does

ONNX (Open Neural Network Exchange) is an open-source specification and ecosystem for representing machine learning models in a vendor-neutral format. Operated as a Graduate Project under the Linux Foundation AI (LFAI) Foundation, ONNX defines a common set of operators, a common file format, and an extensible acyclic computation graph model with standard data types, enabling AI developers to move models across frameworks (PyTorch, TensorFlow, Keras, SciKit Learn, MXNet, PaddlePaddle, MindSpore, and others), inference runtimes, compilers, and hardware accelerators. The core product portfolio includes the ONNX format specification, the ONNX Runtime inference engine (maintained by Microsoft), the onnx-mlir compiler built on MLIR infrastructure that compiles ONNX models to native code on x86, Power, s390x, and other architectures with Python, C/C++, and Java runtimes, the ONNX Optimizer for model size/accuracy/performance tuning, and the ONNX Model Zoo of pre-trained Vision and Language models. The project also documents visualization tooling (Netron, VisualDL, Zetane) and hardware support spanning NVIDIA, Intel, Qualcomm, Habana, Groq, Hailo, Kalray, Bitmain, Cadence, Ceva, Rockchip, Synopsys, IBM z16 NNPA, and others.

The business model is open-source with no direct revenue. ONNX is provided free of charge under open-source licensing, with no pricing tiers, subscription fees, or usage-based charges; the project is funded and governed through the LFAI Foundation under an open governance model with Special Interest Groups (SIGs) and Working Groups. Go-to-market is community-led: awareness and adoption are driven through the GitHub repository, the LFAI Slack workspace, SIG/Working Group participation, documentation at onnx.ai, and ecosystem partnerships with framework and hardware vendors. The primary customer segments are ML developers and AI practitioners who need framework- and hardware-agnostic model deployment, and ML framework/tool vendors who integrate ONNX support to broaden their own interoperability; the segmentation approach is horizontal across the ML toolchain.

Ownership and corporate structure reflect the foundation governance model: ONNX is a community project under LFAI, a non-profit foundation, with no venture funding, no parent company in the commercial sense, and no publicly traded entity. There are no disclosed management team members, no funding rounds, no M&A activity, and no subsidiaries in the input data. Operating geographies are described as global, with no geographic restrictions.

Name

ONNX

Legal name

ONNX

Website

https://onnx.ai

Company type

Private

Operating status

Operating

Short description

ONNX is an open-source specification and ecosystem under the Linux Foundation AI that enables interoperability of machine learning models across frameworks, inference runtimes, and hardware accelerators. It serves ML developers, AI practitioners, and ML tool vendors, with no direct revenue model.

Ownership category

akta.pro rank

Product category

Open-Source Machine Learning Interoperability Framework

NAICS

Custom Computer Programming Services (541511), Other Computer Related Services (541519), Computer Systems Design Services (541512)

SIC

Services-Computer Programming Services (7371), Services-Computer Programming, Data Processing, Etc. (7370), Services-Computer Integrated Systems Design (7373)

akta.pro primary industry

On-Device Inference Runtimes & SDKs (mobile/embedded) (HDAAAJAB)

akta.pro secondary industry

On-Device/Edge Foundation Models (Mobile/Embedded LLMs) (HDAAACAN)

Keywords

• Open source machine learning
• Model interoperability format
• Deep learning inference runtime
• Neural network compiler
• ML framework portability

GTM type

B2B

Offering type

Software

Cost components

Personnel, Technology or R&D, Infrastructure, Operations

Revenue model

1. Open Source / No Direct Revenue: ONNX is an open-source, community-driven project under the Linux Foundation AI (LFAI). There is no direct revenue model. The project is funded through the LFAI Foundation and maintained by community contributions.

Pricing tiers

Go-to-market motion1 record

Distribution channels2 records

Marketing channels4 records

Core offering

ONNX is an open standard format for representing machine learning and deep learning models, defining a common set of operators and a common file format that enables AI developers to use models across a variety of frameworks (PyTorch, TensorFlow, Keras, etc.), tools, runtimes, and compilers. The project ships an ecosystem of open-source tools including the ONNX specification, the ONNX Runtime inference engine, the onnx-mlir compiler, the ONNX Optimizer, and the ONNX Model Zoo of pre-trained models. ONNX is offered as free open-source software under the Linux Foundation AI (LFAI) Foundation.

Product overview

ONNX is a unified open format and ecosystem for machine learning model interoperability, consisting of the core ONNX specification, the ONNX Runtime inference engine, the onnx-mlir compiler for native code generation, the ONNX Optimizer for model optimization, and the ONNX Model Zoo of pre-trained models. These products work together to enable developers to train models in any framework, export to ONNX format, optimize and compile the models, and deploy them across diverse frameworks, tools, runtimes, and hardware platforms. ONNX is a community-driven project under the LFAI Foundation.

Differentiator

Problem solved

Functional benefit

Brands

• onnx-mlir: Representation and Reference Lowering of ONNX Models in MLIR Compiler Infrastructure - an open-source project for compiling ONNX models into native code on x86, Power, s390x and other architectures
• ONNX Dialect
• OMTensor Runtime API

Products and services

• ONNX (Open Neural Network Exchange) Open format for representing machine learning and deep learning models, defining a common set of operators and a common file format that enables AI developers to use models with a variety of frameworks, tools, runtimes, and compilers.
• ONNX Runtime A runtime engine for executing ONNX models, designed to accelerate inferencing across various hardware platforms including CPU and GPU.
• onnx-mlir Open-source compiler that compiles ONNX models into native code using MLIR infrastructure, supporting x86, Power, s390x and other architectures with Python, C/C++, and Java runtime APIs for inference.
• ONNX Optimizer Optimization tool for ONNX models that fine-tunes models for size, accuracy, resource utilization, and performance.
• ONNX Model Zoo Collection of pre-trained models in ONNX format provided by the ONNX community, including Vision Models and Language Models for quick model deployment.

Segments2 records

Ideal customer profiles2 records

Technology focussed Yes

API detail

Has API

No

API docs

API detail

Core technology

AI maturity

App detail

Integration1 record

AI capability3 records

Feature6 records

Partnerships

Nine partnerships are on record, tiered core and flagship.

• PyTorchcoreTechnology or IntegrationFirst-class ONNX export support in PyTorch, enabling PyTorch users to export models in ONNX format for deployment across different inference engines and hardware platforms.
• TensorFlowcoreTechnology or IntegrationTensorFlow-ONNX converter enables conversion between TensorFlow models and ONNX format, supporting interoperability for TensorFlow users.
• Microsoft ONNX RuntimecoreTechnology or IntegrationONNX Runtime is the primary inference runtime for executing ONNX models, providing optimized performance across CPU and GPU platforms.
• Intel OpenVINOcoreTechnology or IntegrationOpenVINO toolkit supports ONNX model import and optimization for Intel hardware acceleration.
• NVIDIA TensorRTcoreTechnology or IntegrationNVIDIA TensorRT provides ONNX model parsing and optimized inference on NVIDIA GPU hardware.
• Linux Foundation AI (LFAI)flagshipStrategic or Co-development PartnerONNX is an LFAI Graduate Project, governed under the Linux Foundation's open governance structure with transparency and inclusion as core principles.
• Azure Cognitive ServicescoreTechnology or IntegrationAzure Cognitive Services supports ONNX model format for custom vision and other AI services.
• HuggingFace OptimumcoreTechnology or IntegrationHuggingFace Optimum provides ONNX export capabilities for transformer models from the HuggingFace hub.
• IBM zDNN (NNPA)coreTechnology or IntegrationIBM Z Deep Neural Network Library enables ONNX model acceleration on IBM z16 processors through the NNPA accelerator.

Scale indicators2 records

Recent moves6 records

Expansion highlights5 records

Others

• Hugging Face: Model hub and Optimum library that provides ONNX export for transformers; an ecosystem partner in the model distribution and deployment space.
• ONNX Runtime (Microsoft): Microsoft-led inference engine built specifically to execute ONNX models; a core ecosystem partner and adjacent runtime layer rather than a competitor.
• MLIR / LLVM: Compiler infrastructure that onnx-mlir is built upon; an enabling/adjacent technology in the ML compilation space rather than a direct competitor.

Broad incumbents

• TensorFlow: Major ML framework with its own model formats (SavedModel, TFLite) and deployment tooling that overlaps with ONNX's cross-framework portability role.
• PyTorch: Dominant ML framework that both consumes ONNX (via export) and offers its own deployment/compilation paths, making it a partner and a potential substitute.
• NVIDIA TensorRT: High-performance inference optimizer/runtime that parses ONNX for NVIDIA GPUs; overlaps on deployment optimization while being vendor-specific.

Direct peers

• NNEF (Khronos Group): Khronos's Neural Network Exchange Format is a directly competing open standard for representing and exchanging trained neural network models across tools and hardware.
• Intel OpenVINO: Toolkit for model optimization and cross-hardware inference that imports ONNX; overlaps with ONNX's deployment-optimization and hardware-access value proposition.
• Apache TVM: Open-source ML compiler stack that ingests models (including ONNX) and compiles them for diverse hardware, overlapping with ONNX's compiler and deployment interoperability goals.

Emerging players

• Apache MXNet: Open-source deep learning framework with model serialization and cross-platform deployment ambitions in the same ML tooling category as ONNX.

Market position

Strengths4 records

Weaknesses3 records

Competitive moat4 records

Key risks4 records

Key highlights6 records

Customer concentration

• GitHubgithub.com/onnx/onnx
• Xx.com/onnxai
• LinkedInlinkedin.com/company/onnx

Revenue estimate

Valuation estimate

Number of profiles

Funding overview

Funding rounds

Investors

Funding detail is available on the Subscription and Enterprise plan.Contact sales →

M&A

Investments

M&A and investment is available on the Subscription and Enterprise plan.Contact sales →

Frequently asked questions about ONNX