--- title: "onnxruntime-server vs server" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kibae-onnxruntime-server-vs-triton-inference-server-server" tools: ["kibae-onnxruntime-server", "triton-inference-server-server"] --- # onnxruntime-server vs server *GraphCanon updated Sep 20, 2026* ## Verdict Pick onnxruntime-server if the ONNX Runtime Server delivers ONNX model inference services over TCP and HTTP/HTTPS through its versatile server framework; pick server if triton Inference Server is an open-source solution for streamlining AI inferencing, supporting multiple deep learning and machine learning frameworks, and enabling deployment across various devices and environments. [onnxruntime-server](https://github.com/kibae/onnxruntime-server) reports 199 GitHub stars, 19 forks, and 8 open issues, last pushed Sep 11, 2026. [server](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html) has 11k stars, 1.8k forks, and 894 open issues, last pushed Sep 17, 2026. Figures are from public GitHub metadata via [onnxruntime-server's repository](https://github.com/kibae/onnxruntime-server) and [server's repository](https://github.com/triton-inference-server/server). | | [onnxruntime-server](/tools/kibae-onnxruntime-server.md) | [server](/tools/triton-inference-server-server.md) | | --- | --- | --- | | Tagline | ONNX Runtime Server for TCP and HTTP/HTTPS REST APIs | Optimized cloud and edge inferencing solution | | Stars | 199 | 10,995 | | Forks | 19 | 1,837 | | Open issues | 8 | 894 | | Language | C++ | Python | | Adopt for | The ONNX Runtime Server delivers ONNX model inference services over TCP and HTTP/HTTPS through its versatile server framework. | Triton Inference Server is an open-source solution for streamlining AI inferencing, supporting multiple deep learning and machine learning frameworks, and enabling deployment across various devices and environments. | | Persona | - | - | | Runtime | - | - | | License | MIT | BSD-3-Clause | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [onnxruntime-server](/tools/kibae-onnxruntime-server.md) | [server](/tools/triton-inference-server-server.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 8d | 0d | | Open issues (now) | 8 | 894 | | Stars delta | +4 (30d) | +110 (30d) | | Open issues delta | 0 (30d) | -11 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/kibae-onnxruntime-server/trust.md) | [trust report](/tools/triton-inference-server-server/trust.md) | ## Decision facts: onnxruntime-server - **Requirements:** Requires Docker; Install ONNX Runtime, Boost, CMake, and pkg-config.; CUDA is optional for Nvidia GPU support; OpenSSL is required only if HTTPS is needed. - **Adopt for:** The ONNX Runtime Server delivers ONNX model inference services over TCP and HTTP/HTTPS through its versatile server framework. ## Decision facts: server - **Adopt for:** Triton Inference Server is an open-source solution for streamlining AI inferencing, supporting multiple deep learning and machine learning frameworks, and enabling deployment across various devices and environments. ## Choose when ### Choose onnxruntime-server if… - onnxruntime-server is primarily C++; server is Python. - License: onnxruntime-server is MIT, server is BSD-3-Clause. - Requirements: Requires Docker; Install ONNX Runtime, Boost, CMake, and pkg-config.; CUDA is optional for Nvidia GPU support; OpenSSL is required only if HTTPS is needed.. - Tags unique to onnxruntime-server: ai, cuda, inference-server, onnx. - You need a flexible serving solution where models can be accessed via both TCP and HTTP/HTTPS REST APIs simultaneously, allowing for diverse application integration scenarios. ### Choose server if… - server is primarily Python; onnxruntime-server is C++. - License: server is BSD-3-Clause, onnxruntime-server is MIT. - Tags unique to server: cloud, datacenter, edge, gpu. - Use Triton Inference Server when you need to deploy AI models across multiple frameworks such as TensorRT, PyTorch, ONNX, and OpenVINO. ## When NOT to use onnxruntime-server - If your deployment environment only supports package managers without official onnxruntime-server packages like Debian or MacOSbrew (requires manual installation). - When your architecture lacks support for CUDA and you cannot utilize its performance benefits, as other frameworks might offer more straightforward CPU-only setups. ## When NOT to use server - Avoid Triton Inference Server if your project is not compatible with the NVIDIA AI Enterprise ecosystem and you do not require support for NVIDIA GPUs. - Do not use Triton Inference Server if your deployment environment does not support the frameworks it is optimized for, such as TensorRT, PyTorch, ONNX, and OpenVINO. - If your application does not require dynamic batching or sequence batching, Triton Inference Server might not be the best choice. - If you are looking for a solution that does not integrate with the KServe protocol for HTTP/REST and GRPC inference, Triton Inference Server may not be suitable. ## Common questions ### What is the difference between onnxruntime-server and server? onnxruntime-server: ONNX Runtime Server for TCP and HTTP/HTTPS REST APIs. server: Optimized cloud and edge inferencing solution. See the comparison table for live GitHub stats and shared categories. ### When should I choose onnxruntime-server over server? Choose onnxruntime-server over server when onnxruntime-server is primarily C++; server is Python; License: onnxruntime-server is MIT, server is BSD-3-Clause; Requirements: Requires Docker; Install ONNX Runtime, Boost, CMake, and pkg-config.; CUDA is optional for Nvidia GPU support; OpenSSL is required only if HTTPS is needed.; Tags unique to onnxruntime-server: ai, cuda, inference-server, onnx; You need a flexible serving solution where models can be accessed via both TCP and HTTP/HTTPS REST APIs simultaneously, allowing for diverse application integration scenarios. ### When should I choose server over onnxruntime-server? Choose server over onnxruntime-server when server is primarily Python; onnxruntime-server is C++; License: server is BSD-3-Clause, onnxruntime-server is MIT; Tags unique to server: cloud, datacenter, edge, gpu; Use Triton Inference Server when you need to deploy AI models across multiple frameworks such as TensorRT, PyTorch, ONNX, and OpenVINO. ### When should I avoid onnxruntime-server? If your deployment environment only supports package managers without official onnxruntime-server packages like Debian or MacOSbrew (requires manual installation). When your architecture lacks support for CUDA and you cannot utilize its performance benefits, as other frameworks might offer more straightforward CPU-only setups. ### When should I avoid server? Avoid Triton Inference Server if your project is not compatible with the NVIDIA AI Enterprise ecosystem and you do not require support for NVIDIA GPUs. Do not use Triton Inference Server if your deployment environment does not support the frameworks it is optimized for, such as TensorRT, PyTorch, ONNX, and OpenVINO. If your application does not require dynamic batching or sequence batching, Triton Inference Server might not be the best choice. If you are looking for a solution that does not integrate with the KServe protocol for HTTP/REST and GRPC inference, Triton Inference Server may not be suitable. ### Is onnxruntime-server or server more popular on GitHub? server has more GitHub stars (10,995 vs 199). Stars measure visibility, not whether either tool fits your constraints. ### Are onnxruntime-server and server open source? Yes - both are open-source projects on GitHub (onnxruntime-server: MIT, server: BSD-3-Clause). ### Where can I find alternatives to onnxruntime-server or server? GraphCanon lists graph-backed alternatives at [onnxruntime-server alternatives](/tools/kibae-onnxruntime-server/alternatives) and [server alternatives](/tools/triton-inference-server-server/alternatives) ([onnxruntime-server markdown twin](/tools/kibae-onnxruntime-server/alternatives.md), [server markdown twin](/tools/triton-inference-server-server/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/kibae-onnxruntime-server-vs-triton-inference-server-server.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, onnxruntime-server or server? onnxruntime-server: Active. server: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for onnxruntime-server and server? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [onnxruntime-server trust report](/tools/kibae-onnxruntime-server/trust); [server trust report](/tools/triton-inference-server-server/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kibae-onnxruntime-server`](/api/graphcanon/graph?tool=kibae-onnxruntime-server) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_