Overview

Provide a TARGET and a REFERENCE, and Matchering produces a mastered target intended to match the reference’s RMS, frequency response, peak amplitude and stereo width. That makes it a focused option when a particular recording is the benchmark; it is less suited to users who want a broad mastering environment rather than a reference-driven process.

Matchering 2.0 is available as a containerized web application, Python library and ComfyUI node, and it is integrated into the UVR5 Desktop App. Songmastr, MVSEP and Moises host the project for use without installation. The FAQ describes the algorithm as digital signal processing rather than neural-network based, and says users may use their mastered tracks wherever they want.

Key features

Reference matching is the core capability. It can help bring material toward a chosen track’s loudness, tonal response, peak level and stereo width, but the choice of reference matters: the algorithm follows that target rather than creating a direction from scratch. The project describes it as working well with almost all genres, especially EDM, while cautioning against experimental music with a very specific musical form.

The Python library has a command-line application and can connect to the Python ecosystem, which makes it the most flexible form for developers. That flexibility comes with a setup threshold: the library requires Python 3.8.0 or higher and a machine with 4 GB of RAM. Loading MP3 files also requires installing FFmpeg separately, so WAV is the more direct route for users who do not want an extra dependency.

ComfyUI and UVR5 integrations offer ways to use Matchering alongside those tools, while the hosted integrations provide an installation-free route. Those options broaden access, but the self-hosted web application carries an important caveat: its maker says it is intended for home and in-house use, not public Internet hosting as-is. The cited risks include DEBUG being enabled, no production web server, non-scalable SQLite, and Django, SQLite, Redis and the Matchering worker running in one container. Operators need security and scalability changes before exposing it publicly.

Pricing

The Open-source software plan is free and uses the GPLv3 license. It includes reference-track matching, WAV export, and WAV and MP3 input; MP3 loading depends on separately installed FFmpeg. There are no published paid plans, so Matchering is a strong fit for users comfortable with open-source software and its setup requirements, rather than buyers looking for a supported paid tier or a tiered service quota.

Platforms

Matchering supports Linux, macOS and Windows, as well as web and self-hosted use. Its forms range from the containerized web application to the Python library and ComfyUI node, with UVR5 Desktop App integration and hosted access through Songmastr, MVSEP and Moises. This range accommodates both self-directed technical users and people who prefer to try a hosted version, though the public-hosting warning applies to operating its web application on the Internet.

Who it's for

Choose Matchering if you have a reference recording in mind and want to pursue a similar balance, level and stereo width without paying for the open-source software. It also makes sense for developers who can meet its Python requirements and want a command-line or ecosystem integration. Look elsewhere if your music depends on a highly specific experimental form, you need public web hosting without additional engineering, or you want a broad set of mastering controls rather than automated reference matching.

Pros and cons

Pro: It targets four concrete properties—RMS, frequency response, peak amplitude and stereo width—making the reference-led workflow well defined.
Pro: The GPLv3 software is free, with a Python library, command-line application, ComfyUI node and desktop integration to suit different workflows.
Pro: WAV and MP3 input and WAV export cover common file needs, and the FAQ says users may use mastered tracks wherever they want.
Con: MP3 loading requires a separate FFmpeg installation, adding setup work.
Con: The Python route requires Python 3.8.0 or higher and 4 GB of RAM, which may rule it out on constrained or older systems.
Con: Experimental music with a very specific musical form is outside the genres the project says it handles well, and the web app is not safe for public hosting without changes.

Alternatives

For a different mastering workflow, DSP-Quattro is a macOS freemium option with a free trial; its new-license plan is 99.00 USD per month, while its upgrade plan is 49.00 USD per month. Pick it instead if a macOS option with a trial and paid license route better fits your buying preference.

IK Multimedia ReSing is a freemium option for macOS and Windows, with a free plan and a one-time paid plan. Consider it instead if you want a different software category and its free or paid plan fits your needs.

Tunr is free for macOS and Windows; its free option allows three masters before a licence key and offers 16-bit and 24-bit output. Choose it instead if you want a free desktop option and that initial master allowance suits you.

FUSER is a paid macOS and Windows option with a free trial and a 59.00 GBP one-time price. Consider it instead if you prefer a paid desktop product and its host requirements fit your setup.

Sonoris DDP Creator is a paid option for iOS, macOS and Windows with a free trial; its Standard and Pro plans cost 249.00 EUR and 349.00 EUR per once, respectively. Choose it instead if its platform support and license options suit your workflow.

StudioZIO Mastering Suite is a free macOS option that supports AU, VST3, AAX and standalone use, with no licence key or account. It is a reasonable alternative if you want that plugin and standalone format rather than Matchering’s reference-matching forms.

Brainworx bx_masterdesk is a paid macOS and Windows option with a free trial and monthly plans. Consider it instead if you prefer a paid alternative to Matchering.

Voxengo SPAN is a free real-time FFT spectrum analyzer plugin for macOS and Windows. Choose it instead if spectrum analysis, rather than automated mastering, is what you need.

Browse Audio Mastering Software or AI Music Mastering Software for more options.

Verdict

Matchering is an easy recommendation for musicians with a suitable reference and developers comfortable with its open-source setup: it delivers a focused matching workflow at no cost and in several software forms. Its main drawbacks are the extra FFmpeg dependency for MP3 and the web application’s unsuitability for public hosting as-is. If you need reference-led mastering and can work within those limits, start with Matchering; if you need a broadly configurable mastering environment or a public web service without additional engineering, look elsewhere.

	AudioMuse AI Music Mastering	Matchering
Free plan		Yes
Paid from	$4/mo	Free
Platforms	Web	Linux, macOS, self-hosted, Web, Windows
Free plan	No	Yes
Stem mastering	No
Loudness targets	Yes
Supported platforms	web
Reference track matching		Yes
WAV export		Yes
Input formats		WAV, MP3

AudioMuse AI Music Mastering vs Matchering

AudioMuse AI Music Mastering

Matchering