AI on the Edge

AI on the edge means running AI inference directly on a local device rather than sending your data to a remote server. The "edge" is your own hardware, sitting on your network, doing the work itself.

Most AI services work by shipping your data to a data centre where powerful computers process it and send back a result. Edge AI reverses that. Processing happens on a small dedicated chip attached to your local hardware. The data never leaves your network.

For home automation, this distinction matters. If you want a camera to detect when a person walks into your driveway, the traditional cloud approach streams that footage to a remote service, has it analysed elsewhere, and delivers a notification back. That works, but it means your footage is leaving your network every time the camera fires, you are dependent on an internet connection, and you are paying a provider for the privilege.

Edge AI removes all three problems. A dedicated inference chip like the Google Coral USB Accelerator can analyse a camera frame in around 10 milliseconds, locally, with no cloud involvement. The result is faster alerts, complete privacy, and no ongoing subscription cost.

The trade off is raw capability. Edge hardware cannot match the processing power of a cloud AI service, but for common home automation tasks it does not need to. The Coral handles object detection at hundreds of frames per second on 2 watts of power.

The category is developing quickly. The Raspberry Pi AI HAT+ 2, released in January 2026, uses the Hailo-10H chip to deliver 40 TOPS of inference with 8GB of dedicated onboard memory, opening the door to small generative AI models running entirely on device.

For privacy-focused home automation, edge AI is not a compromise. It is the right tool for the job. Frigate is the piece of software that brings this to life on a self hosted camera setup.