Edge Impulse is a platform focused on deploying ML models to low-power edge devices and embedded systems. It supports both vision and other models like audio, time-series, and signal processing. Edge Impulse is uniquely good at working withmicrocontrollers and has SDKs for single-board computers and mobile devices.
The design focus on TinyML makes it less suited for high-resource, general-purpose tasks like video processing and running modern, state-of-the-art ML models. It also requires some familiarity with embedded systems. It typically requires custom coding your application logic to run on the embedded board.
Chose Edge Impulse if: you're working on an IoT or wearable device that's not capable of running more powerful models, framework, and logic.
LitServe is a lightweight and customizable inference server focused on serving models with minimal overhead. It is fairly minimalistic but flexible and self-contained.
Like Triton, LitServe is task-agnostic, meaning it is designed to balance the needs of vision models with NLP, audio, and tabular models. This means it's not as feature-rich for computer vision applications (for example, it doesn't have any built-in features for streaming video). It is also highly focused on model serving without an abstraction layer like Workflows (offered by Roboflow Inference) for model chaining and integrations with other tools.
Choose LitServe if: you are working on general-purpose machine learningtasks and were previously considering rolling your own server but wanta more featureful starting point.
Inference turns any computer or edge device into a command center for your computer vision projects.