frigate vs ComfyUI
Two of the top projects in Image and video, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
frigate
Local NVR with realtime object detection for IP cameras
ComfyUI
Node-graph editor for running diffusion image, video, audio and 3D workflows
Verdict
frigate ranks higher, 91 to 86. Of the 4 score parts we measure for both, frigate leads on 2, ComfyUI on 1 and 1 is even.
- AdoptionComfyUI leads by 13 points, 99 to 86.
- FreshnessEven at 100.
- Maintenancefrigate leads by 19 points, 95 to 76: a wide gap.
- Agent-readyfrigate leads by 40 points, 70 to 30: a wide gap.
Pick frigate if you are
Home Assistant users running local AI detection on IP cameras
- Runs detection only where motion is found, which limits CPU use
- Retention rules based on detected objects, plus 24/7 recording
Pick ComfyUI if you are
Creators and engineers building local image, video and audio pipelines
- Native support for many models: SDXL, Flux, Wan 2.x, LTX-Video, HunyuanVideo, ACE-Step
- README claims large models run on 4GB VRAM with 8GB RAM via weight streaming
| What we compare | frigate | ComfyUI |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 86, widely used | 99, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 95, healthy | 76, fair |
| Agent-ready | 70, partly | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 37.1k | 144.4k |
| License | MIT (permissive) | GPL-3.0 (copyleft) |
| Last commit | Nov 2026 | Nov 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | Python |
| Docker | Yes | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Not stated | Mentioned |
frigate
Frigate is a network video recorder that runs object detection locally on IP camera streams using OpenCV and TensorFlow. It uses low-overhead motion detection to decide where to run detection, records video with retention based on detected objects, and re-streams over RTSP. It integrates with Home Assistant through a custom component and publishes events over MQTT.
Who it is for: Home Assistant users running local AI detection on IP cameras
Strengths
- Runs detection only where motion is found, which limits CPU use
- Retention rules based on detected objects, plus 24/7 recording
- RTSP re-streaming, plus WebRTC and MSE for low-latency live view
- MQTT and a Home Assistant custom component for integration
Weaknesses
- README strongly recommends a GPU or AI accelerator for detection
- Tightly oriented to Home Assistant; other setups rely on MQTT
- Frigate name and logo are trademarks excluded from the MIT license
- GPU optional
- Docker + Compose
- Needs MQTT, Home Assistant (optional), IP cameras (RTSP)
- Models: TensorFlow
ComfyUI
ComfyUI is a node-graph interface and execution engine for diffusion and other generative models, covering image, video, audio, 3D and text workflows. It runs on NVIDIA, AMD, Intel, Apple Silicon and Ascend hardware, and uses weight streaming and offloading to fit large models in small VRAM. Workflows save as JSON and can be driven through a local API, with custom nodes for extension.
Who it is for: Creators and engineers building local image, video and audio pipelines
Strengths
- Native support for many models: SDXL, Flux, Wan 2.x, LTX-Video, HunyuanVideo, ACE-Step
- README claims large models run on 4GB VRAM with 8GB RAM via weight streaming
- Desktop app, Windows portable builds, comfy-cli and manual install; NVIDIA, AMD, Intel, Apple GPUs
- Workflows save as JSON, are recoverable from generated media, and can be called through an API
Weaknesses
- Node graph has a learning curve compared with form-based generation UIs
- Commits between stable tags may be unstable and break many custom nodes
- Partner/API nodes for closed models are paid; --offline disables them
- Desktop app only for Windows and macOS; Linux needs manual install
- RAM ≥ 8 GB
- GPU optional
- Needs PyTorch, Python 3.13 recommended
- Models: Stable Diffusion 1.5, SDXL, SD3.5, Flux.1, Flux.2
More in Image and video
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.