Pixop vs RestoraX
Pixop
pixop.comRestoraX
github.comSide by side
| Figure | Pixop | RestoraX |
|---|---|---|
| Price from | $0.02/mo | Free |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 9 | 1 |
| Spec score · AI Video Restoration Software | 8.9 (#2) | 7.5 (#20) |
| Integrations | Official integrations include TVU MediaMesh, Mimir from Fonn Group, Fonn Group workflows and Silverdraft hardware. | The documented stack uses Celery and Redis for background jobs, PostgreSQL or SQLite for data storage, and MinIO in the production Docker setup. |
| Purpose | Pixop provides machine-learning video enhancement for broadcasters, streamers, and media companies. | RestoraX is an open-source AI video and audio restoration toolkit for old films, home videos, and archival footage. |
Plans
Pixop
- AWS Marketplace Pixop creditFree
- Storage$0.02 a month
- Pixop Super Resolution$0.08, paid once
- Download$0.09, paid once
- Pixop Denoiser$0.41, paid once
- Pixop Deep Restoration 2$0.49, paid once
- Pixop FILE direct billingPriced on request
- Pixop FILE via AWS MarketplacePriced on request
- Pixop PrimePriced on request
In their own words
RestoraX
RestoraX is a free, open-source toolkit for restoring old films, home videos, and archival footage, as well as anime, VHS, and newsreels. It describes 25 models for tasks including super-resolution, colorization, face restoration, frame interpolation, scratch and dust removal, deinterlacing, stabilization, SDR-to-HDR conversion, and audio restoration. A visual pipeline builder connects processing steps in a DAG, with typed ports, parallel branches, merge strategies, retry policies, and per-branch progress. RestoraX provides a React web interface, a FastAPI REST API with WebSocket progress, a command-line interface, and a ComfyUI node pack. It is self-hostable, with Docker Compose configurations documented for development and production. Third-party restorers can be added through separate PyPI plugins. Model weights download from HuggingFace Hub on first use. The stated minimums are Python 3.11, 8 GB RAM, 5 GB disk, and FFmpeg; CPU works, while CUDA 12.1 or later with at least 8 GB GPU VRAM is recommended. It is released under the MIT License.