NVIDIA Triton Inference Server vs ONNX Runtime
NVIDIA Triton Inference Server
developer.nvidia.comONNX Runtime
onnxruntime.aiSide by side
| Figure | NVIDIA Triton Inference Server | ONNX Runtime |
|---|---|---|
| Price from | $375/mo | Free |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| Android | — | Yes |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 3 | 1 |
| Spec score · Deep Learning Software | 8.0 (#13) | 8.4 (#6) |
| Integrations | It integrates with Kubernetes for scaling and Prometheus for monitoring, and NVIDIA lists availability through AWS, Azure, and Google Cloud marketplaces with NVIDIA AI Enterprise. | The ecosystem documentation lists integrations with Azure Machine Learning, Azure Custom Vision, Azure SQL Edge, Azure Synapse Analytics, ML.NET, and NVIDIA Triton Inference Server. |
| Purpose | Dynamo-Triton is open-source inference-serving software for deploying, running, and scaling AI models from multiple frameworks on GPU- or CPU-based infrastructure. | ONNX Runtime is a cross-platform machine-learning model accelerator with interfaces for hardware-specific libraries. |
Plans
NVIDIA Triton Inference Server
- Open-source developmentFree
- NVIDIA AI Enterprise cloud productionPriced on request
- NVIDIA AI Enterprise subscription$4,500 a year
In their own words
NVIDIA Triton Inference Server
NVIDIA Triton Inference Server is open-source software for deploying and serving AI models on GPU- or CPU-based infrastructure. It supports TensorRT, PyTorch, ONNX, OpenVINO, Python and RAPIDS FIL, with model formats including TensorRT Plan, ONNX, TensorFlow GraphDef and SavedModel, PyTorch TorchScript and PyTorch 2.0. Triton serves real-time, batched, ensemble and audio/video streaming workloads. Dynamic batching and concurrent model execution are among its performance features. Requests can use HTTP/REST, gRPC or the C API, with a Java API for in-process use. Triton integrates with Kubernetes for scaling and Prometheus for monitoring. Deployment options include NVIDIA GPUs, other accelerators, x86 and ARM CPUs, cloud, data centers, edge and embedded systems. NVIDIA lists Linux containers for x86 and Arm, plus Windows and Jetson JetPack binaries. Development code and containers are free; production options include a cloud marketplace plan billed pay as you go and a subscription listed at $4,500 a year per GPU. NVIDIA says deployers are responsible for security, and Triton does not sandbox arbitrary model or backend code.
ONNX Runtime
ONNX Runtime is a free engine for machine-learning inference and training within existing technology stacks. It can run models from frameworks including PyTorch, TensorFlow/Keras, TFLite, and scikit-learn. For inference, it optimizes latency, throughput, memory use, and binary size through graph transformations, accelerator-aware partitioning, and optimized computation kernels. Its Execution Providers connect ONNX models to hardware acceleration libraries for CPUs, GPUs, FPGAs, and specialized NPUs. Listed options include NVIDIA CUDA and TensorRT, Intel OpenVINO, DirectML, Qualcomm QNN, Android NNAPI, Apple CoreML, and WebGPU. Deployment spans cloud servers, edge and mobile devices, and browsers; its web runtime supports browser models and its mobile runtime supports Android and iOS. Supported languages include Python, C#, C++, Java, JavaScript, and Rust. Developers can create smaller web or mobile packages by including only the operators and opsets their models need. The open-source runtime is available on Android, iOS, Linux, macOS, self-hosted environments, web, and Windows.