DeepStream is NVIDIA's platform for building highly optimized video processing pipelines accelerated by NVIDIA's hardware, taking full advantage of TensorRT for accelerated inference and CUDA for parallel processing. It targets many of the same business problems as Inference, including monitoring security cameras, smart cities, and industrial IoT.
DeepStream has a reputation for being difficult to use with a steep learning curve. It requires familiarity with NVIDIA tooling and while it is highly configurable, it's also highly complex. It's focused on video processing, without deep integrations with other tooling. DeepStream is not open source; ensure that the license issuitable for your project.
Choose DeepStream if: you're an expert willing to invest a lot of time and effort into optimizing a single project and high throughput is your primary objective.
The PyTorch ecosystem's equivalent of Tensorflow Serving is TorchServe. It's optimized for serving PyTorch models across several domains including vision, NLP, tabular data, and audio.
Like Tensorflow Serving, it is designed for large-scale cloud deployments and can require custom configuration for things like pre- and post-processing and deploying multiple models. Because of its wide mandate it lacks many vision-specific features (like video streaming).
Chose TorchServe if: you're looking for a way to scale and customize the deployment of your PyTorch models and don't need vision-specific functionality.
Inference turns any computer or edge device into a command center for your computer vision projects.