Viseron vs Frigate: AI-Powered NVR Compared

Want AI Surveillance Without Depending on Home Assistant?

Both Frigate and Viseron use AI to detect objects in your camera feeds. Both run locally with no cloud dependency. But they target different users. Frigate is built as a Home Assistant companion — deeply integrated, tightly focused, and optimized for the HA ecosystem. Viseron is a standalone platform with its own web UI, built-in face recognition, and support for multiple AI backends. If Home Assistant is your smart home hub, Frigate is the obvious pairing. If you want AI surveillance that stands on its own, Viseron is worth a serious look.

Updated March 2026: Verified with latest Docker images and configurations.

Feature Comparison

Feature	Viseron	Frigate
AI backends	TensorFlow, YOLO, Darknet, DeepStack	TensorFlow Lite (SSD MobileNet)
Hardware acceleration	CUDA (NVIDIA), Coral EdgeTPU	Coral TPU, Intel OpenVINO, NVIDIA
Face recognition	Built-in	No (use Double Take add-on)
Web UI	Built-in React dashboard	Built-in React dashboard
Home Assistant	Integration available	Native MQTT, deeply integrated
MQTT	Yes	Yes
Recording	Event-based	Continuous + event-based
License	MIT	MIT
Language	Python + TypeScript	Python + Go
RTSP support	Yes	Yes
Zones	Yes	Yes (AI-aware)
Object tracking	Yes (cross-camera)	Yes (single camera)
Post-processing	Custom scripts, webhooks	MQTT events, HA automations
Latest version	v3.4.1	v0.14.1
GitHub stars	~1.7K	~19K

Overview

Frigate is the most popular self-hosted NVR, built specifically to work with Home Assistant. It uses Google Coral TPUs for efficient object detection, supports continuous recording with event-based review, and communicates detection events via MQTT. The project has a massive community and is under active development.

Viseron takes a different approach. It’s designed as a standalone surveillance platform — you don’t need Home Assistant to use it. Viseron supports multiple AI backends (TensorFlow, YOLO, Darknet), includes built-in face recognition, and has cross-camera object tracking. Its web UI shows live feeds, recordings, and event clips without any external integration.

Installation Complexity

Frigate requires a YAML config, Coral TPU for optimal performance, and an MQTT broker:

services:
  frigate:
    image: ghcr.io/blakeblackshear/frigate:0.17.0
    restart: unless-stopped
    privileged: true
    shm_size: 256mb
    ports:
      - "5000:5000"
      - "8554:8554"
    volumes:
      - ./config:/config
      - ./storage:/media/frigate
    devices:
      - /dev/bus/usb:/dev/bus/usb  # Coral TPU
    environment:
      FRIGATE_RTSP_PASSWORD: changeme

Viseron uses a YAML config file and optional GPU passthrough:

services:
  viseron:
    image: roflcoopter/viseron:3.4.1
    restart: unless-stopped
    privileged: true
    ports:
      - "8888:8888"
    volumes:
      - ./config:/config
      - ./recordings:/recordings
      - /etc/localtime:/etc/localtime:ro
    # For NVIDIA GPU:
    # runtime: nvidia
    # environment:
    #   - NVIDIA_VISIBLE_DEVICES=all

Both require a YAML config defining cameras and detection settings. Frigate’s config is more focused — cameras, detectors, zones. Viseron’s config is broader — it covers multiple detector backends, face recognition settings, post-processing scripts, and notification hooks. Frigate is slightly easier to get started with because the Coral TPU path is well-documented. Viseron’s flexibility means more config options to navigate.

Full setup guides: Self-Host Frigate | Self-Host Viseron

Performance and Resource Usage

Metric	Viseron	Frigate
Idle RAM (1 camera)	~400–700 MB	~300–500 MB
Under load (4 cameras)	~1.5–3 GB	~1–2 GB
CPU (with Coral TPU)	Low (if using EdgeTPU backend)	Very low
CPU (with NVIDIA GPU)	Low (CUDA offloading)	Low (if using NVIDIA)
CPU (software detection)	Very high (YOLO is heavy)	Very high
Startup time	~15–30 seconds	~10–20 seconds
Disk per camera/day	2–10 GB (event clips)	5–20 GB (continuous + events)

Frigate is leaner because its architecture is more focused — Go for stream handling, Python for detection coordination. Viseron’s Python-heavy stack uses more RAM, especially when running TensorFlow models or face recognition. The difference is negligible on anything with 4+ GB of RAM.

AI Detection Comparison

Frigate’s Approach

Frigate uses TensorFlow Lite with SSD MobileNet models, optimized for Google Coral EdgeTPU inference. The detection pipeline is highly efficient — the Coral processes detections in ~10ms per frame. You can define zones where detection matters and set minimum/maximum object sizes to filter false positives. Detection classes: person, car, dog, cat, bird, bicycle, motorcycle, and more.

Viseron’s Approach

Viseron supports multiple detection backends:

• YOLO (Darknet) — higher accuracy, heavier compute
• TensorFlow — flexible model loading
• Coral EdgeTPU — via TensorFlow Lite, similar to Frigate
• DeepStack — external AI server integration

Additionally, Viseron has built-in face recognition — it can identify specific people, not just detect “a person.” This opens up automations like “unlock the door when Family Member is detected” without needing external tools. Frigate relies on the separate Double Take project for face recognition.

AI Capability	Viseron	Frigate
Object detection	Multiple backends	TFLite (Coral-optimized)
Face recognition	Built-in	External (Double Take)
License plate recognition	No	External (CodeProject.AI)
Custom models	Yes (YOLO/TF)	Limited (TFLite only)
Detection speed (Coral)	~15ms	~10ms
Model flexibility	High	Low (opinionated)

Community and Ecosystem

Metric	Viseron	Frigate
GitHub stars	~1.7K	~19K
Contributors	~20	~200+
Release cadence	Occasional	Monthly
Documentation	Basic (GitHub wiki)	Comprehensive (docs.frigate.video)
Community	Small (GitHub issues)	Very large (Discord, forums)
Integrations	MQTT, webhooks	HA, MQTT, extensive ecosystem

Frigate’s community is an order of magnitude larger. More contributors means faster bug fixes, better documentation, and more third-party integrations. Viseron’s smaller community means you’re more likely to hit undocumented edge cases.

Use Cases

Choose Viseron If…

• You want built-in face recognition without adding external services
• You don’t use Home Assistant and want a standalone NVR with a web UI
• You have an NVIDIA GPU and want CUDA-accelerated YOLO detection
• You need flexible AI backend options (TensorFlow, YOLO, Darknet)
• Cross-camera object tracking is important to your setup
• You want to run custom YOLO models for specialized detection

Choose Frigate If…

• You use Home Assistant and want native, deep integration
• You have or plan to buy a Google Coral TPU
• Continuous recording with event-based review matters
• You want the largest community and best documentation
• You prefer an opinionated, well-tested detection pipeline over maximum flexibility
• You need reliable, production-ready surveillance with regular updates

Final Verdict

For most self-hosted surveillance setups, Frigate is the better choice because it has the larger community, better documentation, more frequent updates, and an extremely efficient Coral TPU detection pipeline. If you use Home Assistant, it’s the default answer.

Viseron earns its place when you need built-in face recognition, want to run custom YOLO models, or don’t use Home Assistant and want a fully standalone NVR. Its flexibility in AI backends is genuinely useful for advanced users who need more than basic object detection.

The practical recommendation: start with Frigate. If you hit a limitation — needing face recognition, custom models, or independence from Home Assistant — evaluate Viseron as your next step.

FAQ

Can Viseron use a Google Coral TPU?

Yes. Viseron supports EdgeTPU inference through its TensorFlow Lite backend. Configuration is similar to Frigate’s, though Frigate’s Coral integration is more mature and better documented.

Does Frigate support face recognition?

Not natively. The community uses Double Take alongside Frigate to add face recognition. It works well but is a separate service to install and configure. Viseron has face recognition built in.

Which handles more cameras better?

Frigate, due to its Go-based streaming architecture and optimized Coral TPU pipeline. Viseron’s Python-heavy stack can become a bottleneck at 8+ cameras without GPU acceleration. Both handle 4–6 cameras without issues on reasonable hardware (4+ CPU cores, 8+ GB RAM).

Related

• How to Self-Host Frigate NVR
• How to Self-Host Viseron NVR
• Frigate vs ZoneMinder: NVR Comparison
• Frigate vs Shinobi: NVR Comparison
• Frigate vs Motion: Video Surveillance
• Best Self-Hosted Video Surveillance
• NVR Hardware Guide
• Self-Hosted NVR Setup Guide