AI Mastering vs Matchering
AI Mastering
aimastering.comMatchering
github.comSide by side
| Figure | AI Mastering | Matchering |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | Yes |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | Yes | — |
| Android | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 1 |
| Spec score · AI mastering tools | 9.1 (#1) | 8.6 (#6) |
| What it does | AI Mastering is an online service that automatically improves music and balances loudness and dynamic range. | Matchering processes a target recording against a reference recording and returns a master intended to match the reference’s loudness, frequency response, peak amplitude and stereo width. |
Plans
In their own words
AI Mastering
AI Mastering is an online service that uses AI to master audio with the aim of improving music sound quality. Its limiter can balance loudness and dynamic range, while the web interface lets users set target loudness and mastering level. It also supports drag-and-drop uploads, output-format customization, spectrum analysis, and loudness analysis. The API supports audio upload and download, mastering, and changes to target loudness and mastering level; its documentation links JavaScript (Node) and Ruby client libraries and tutorials. An offline desktop version is available, and the maker says its focus is shifting toward that version rather than the API. Offline file size depends on the computer’s memory, while the online service has file limits. The maker says online waiting time cannot be shortened and suggests using the offline version to reduce it. A bass-preservation option can reduce low-frequency attenuation. Supported input formats are WAV, MP3, and MP4, and WAV export is available. The free plan lists unlimited mastering, and users retain control over their mastered tracks without service restrictions.
Matchering
Matchering is open-source software for matching and mastering audio. Give it a TARGET track and a REFERENCE track, and it produces a mastered TARGET that follows the reference’s RMS, frequency response, peak amplitude, and stereo width. Matchering 2.0 comes as a containerized web application, Python library, and ComfyUI node, and is integrated into the UVR5 Desktop App. The Python library also provides a command-line application and can connect with the Python ecosystem. It requires Python 3.8.0 or later and a machine with 4 GB of RAM. WAV and MP3 are supported input formats, though MP3 loading needs a separate FFmpeg installation. The project says the algorithm uses digital signal processing rather than a neural network, and works well across nearly all genres, especially EDM, with an exception for experimental music with a very specific form. The free plan is listed at free under GPLv3, with no published paid plans. The maker advises against exposing its web application to the public Internet without security and scalability changes.