Frigate
Self-HostedOpen-source AI-powered video surveillance system
Overview
Frigate is an open-source, AI-driven video surveillance tool built for self-hosting. It processes footage locally using object detection (persons, cars, animals) to reduce false alerts, ensuring privacy by avoiding cloud dependency. Compatible with RTSP cameras, it integrates seamlessly with Home Assistant, supports Docker deployment, and offers features like motion-triggered recording, alert notifications (MQTT/Webhooks), and customizable detection zones. Optimized for hardware acceleration (Coral, NVIDIA), it works on devices from Raspberry Pi to powerful servers.
Self-Hosting Resources
Below is a reference structure for docker-compose.yml.
⚠️ Do NOT run blindly. Replace placeholders with official values.
version: '3'
services:
frigate:
image: <OFFICIAL_IMAGE_NAME>:latest
container_name: frigate
ports:
- "8080:<APP_INTERNAL_PORT>"
volumes:
- ./data:/app/data
restart: unless-stopped Key Features
- AI-powered object detection to minimize false alerts
- Local footage processing for maximum privacy
- Seamless Home Assistant integration
- Docker-based deployment for easy setup
Frequently Asked Questions
? Is Frigate hard to install?
Frigate is easy to deploy via Docker, which simplifies setup. However, configuring hardware acceleration (e.g., Coral USB, NVIDIA GPU) for smooth AI performance may require additional technical steps. Official docs provide detailed guides for most setups.
? Is it a good alternative to Nest Cam?
Yes—Frigate is a strong alternative to Nest Cam. It offers local processing (no cloud fees/privacy risks), AI-driven alerts, and Home Assistant integration. Unlike Nest, it requires self-hosting but gives full control over your data and setup.
? Is it completely free?
Absolutely—Frigate is open-source (MIT license) and free to use. There are no hidden costs; you only need your own cameras, server hardware, and storage for recordings.
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Tool Info
Pros
- ⊕ No subscription fees or cloud costs
- ⊕ Highly customizable detection rules
- ⊕ Supports hardware acceleration for efficient AI processing
Cons
- ⊖ Requires technical setup for hardware acceleration
- ⊖ Limited support for non-RTSP cameras
- ⊖ Needs dedicated hardware for multiple camera streams