Kavij vs rembg
Kavij
kavij.comrembg
github.comSide by side
| Figure | Kavij | rembg |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | — |
| Web | Yes | — |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · AI Background Removers | 8.8 (#11) | 8.2 (#38) |
| API access | Yes | Yes |
| Batch processing | Multiple images can be processed together and downloaded as one ZIP archive. | Yes |
| Export formats | PNG,JPG | PNG |
| Free plan | Yes | Yes |
| Models | Kavij lists four models: standard, precise, fast, and anime. | Available models include options for general images, portraits, clothing, anime, and object segmentation; bria-rmbg is the default local model. |
| Purpose | Kavij removes image backgrounds with AI segmentation and makes the result transparent. | rembg is a tool for removing image backgrounds. |
| Replacement backgrounds | Yes | No |
Plans
In their own words
rembg
rembg is a free tool for removing image backgrounds. It can run as a command-line program, Python library, HTTP server, or Docker container, with API access and batch processing. The command line handles individual images, folders, watch mode, and RGB24 streams; PNG is the listed export format. Models cover general images, portraits, clothing, anime, and object segmentation, with bria-rmbg as the default local model. Edge refinement options include color decontamination, alpha matting, and ViTMatte. Documentation covers FFmpeg RGB24 output, OpenCV, PIL, NumPy, Docker, and the HTTP API. Installation supports CPU, NVIDIA CUDA GPU, and AMD ROCm backends, and requires Python versions >=3.11 and <3.14. Local ONNX models download on first use into the rembg models directory. An optional withoutBG backend uploads images to its servers, with a documented 20 MB maximum. The tool is MIT-licensed, but model weights have separate licenses; commercial RMBG-2.0 use under the BRIA license requires a paid agreement.