AI music generators like Suno and Udio output audio that sounds "mastered" but often does not meet streaming platform standards. Many tracks are too loud, have harsh frequencies, or contain artifacts that become noticeable on professional playback systems. A quick mastering pass can fix these issues and make your AI tracks competitive with professionally produced music.

The good news: you do not need expensive software or professional training. AI-powered mastering tools like LANDR can optimize your tracks in minutes. This guide covers when mastering is necessary, streaming platform requirements, and step-by-step optimization.

Do AI tracks need mastering?

AI music tools apply their own processing during generation, but this is not true mastering. The output is optimized for sounding good during preview, not for streaming platform specifications.

Common issues with AI-generated audio

Issue	Description	How Common
Excessive loudness	Tracks pushed too hot, causing distortion on playback	Very common
Harsh frequencies	Sibilance in vocals, sharp high-end	Common in Suno
Ambient "sheen"	Unwanted reverb or haze that reduces clarity	Common in Suno
Inconsistent dynamics	Overly compressed or uneven sections	Moderate
Artifacts	Digital glitches, clicks, or unnatural transitions	Occasional

According to audio quality comparisons, Udio produces cleaner mixes that are closer to studio-ready, while Suno tracks often have a more "digital" tone and may benefit more from post-processing.

Tip Listen to your AI tracks on multiple systems before deciding. If they sound good on headphones, speakers, and car audio without issues, minimal mastering may be needed. If you hear harshness, muddiness, or distortion, mastering will help.

Streaming platform loudness standards

Streaming services use loudness normalization to ensure consistent playback volume across their catalog. Understanding these targets helps you master appropriately.

Platform targets

Platform	Target LUFS	True Peak	Behavior
Spotify	-14 LUFS	-1 dBTP	Turns loud tracks down; optionally boosts quiet tracks
Apple Music	-16 LUFS	-1 dBTP	Turns tracks up or down via Sound Check
YouTube	-14 LUFS	-1 dBTP	Only turns audio down, never up
Amazon Music	-14 LUFS	-1 dBTP	Normalizes all content
Tidal	-14 LUFS	-1 dBTP	Normalizes all content

According to Spotify's official documentation, tracks are adjusted to -14 dB LUFS during playback, and loudness normalization measures during upload without changing the actual audio file.

What this means for your masters

If your track is too loud (louder than -14 LUFS):

Spotify turns it down during playback
You lose no quality, but excessive compression cannot be undone
Over-compressed tracks sound flat compared to dynamic masters

If your track is too quiet (quieter than -14 LUFS):

Spotify may turn it up (in some listening modes)
YouTube does NOT boost quiet tracks, so they play softer than other content
Apple Music boosts to match -16 LUFS target

The universal target

Master to approximately -14 LUFS integrated with true peak at -1 dBTP or below. This single master works well across all major platforms. You do not need separate versions for each service.

Note Major label releases often land around -8 LUFS, much louder than the -14 target. After normalization, these tracks are turned down but sound less dynamic. Prioritizing dynamics over maximum loudness gives your music an edge.

Checking your track's loudness

Before mastering, measure your AI track's current loudness.

Free loudness meters

Youlean Loudness Meter (free plugin for DAWs)
Orban Loudness Meter (free standalone)
Loudness Penalty (online tool at loudnesspenalty.com)

What to measure

Integrated LUFS: The average loudness of the entire track
Short-term LUFS: Loudness of the loudest sections (choruses)
True Peak: The highest peak level, should be below -1 dBTP

Many AI tracks from Suno land between -8 and -12 LUFS, louder than streaming targets but not excessively so. If your track is already around -14 LUFS with clean peaks, minimal processing may be needed.

Mastering options for AI music

You have three main approaches: online AI mastering services, DAW plugins, or human mastering engineers.

AI mastering services

For most AI music creators, online AI mastering services offer the best balance of quality, speed, and cost.

LANDR

LANDR offers both online mastering and a DAW plugin. The service analyzes your track and applies processing automatically. According to reviews, LANDR produces a "modern, glossy tone" that works well for most genres.

Cost: $12.99/month for Studio Pro subscription (includes plugin)
Best for: Quick, consistent results without technical knowledge
Limitations: Less control over specific settings

CloudBounce

Similar to LANDR, CloudBounce offers automated mastering with multiple style options.

Cost: Pay-per-track or subscription
Best for: Simple mastering with style presets

DAW plugins

For more control, mastering plugins let you adjust every parameter.

iZotope Ozone

According to plugin comparisons, Ozone 12 is preferred for genres requiring precision. The Master Assistant suggests settings based on your track and genre, but you can tweak everything manually.

Cost: $599 for Ozone 12 Advanced (one-time purchase)
Best for: Producers who want full control and learning opportunity
Standout feature: Stem EQ can process vocals, drums, bass separately from a stereo file

FabFilter Pro-L 2

A limiter focused on transparent loudness with multiple limiting algorithms.

Cost: $199 (one-time)
Best for: Simple loudness optimization without extensive processing

Human mastering engineers

For important releases, professional mastering engineers offer expertise that AI cannot replicate.

Fiverr: Budget mastering from $20-100 per track
Professional studios: $50-200+ per track
Best for: Key singles, album releases, or when AI mastering does not fix issues

Step-by-step mastering workflow

Follow this process to master your AI-generated tracks:

Export your AI track in the best available format Most AI generators output MP3 or WAV. If WAV is available, use it. If only MP3 is available, work with that. Converting MP3 to WAV does not improve quality.
Analyze the current state Load the track into your DAW or loudness meter. Check integrated LUFS, true peak, and listen for any obvious issues like harshness, muddiness, or artifacts.
Address any frequency issues If the track sounds harsh, apply gentle high-frequency reduction (EQ). If vocals are unclear, consider a subtle presence boost around 2-4 kHz. For muddy tracks, reduce low-mids around 200-400 Hz.
Control dynamics if needed If the track sounds overly compressed (flat, lifeless), there is little you can do to restore dynamics. If it sounds too dynamic (volume jumping between sections), light compression can even things out.
Apply limiting to reach target loudness Use a limiter to bring the track to around -14 LUFS integrated. Set true peak ceiling at -1 dBTP to prevent clipping on playback.
Export in the correct format Export as WAV (44.1 kHz, 16-bit or 24-bit) for distribution. This is the format streaming platforms require.

Quick settings for AI music mastering

If you are using LANDR or a simple mastering tool:

Setting	Recommendation
Style	Warm or Balanced (avoid Aggressive)
Loudness	Medium (avoid Maximum)
Target	-14 LUFS if adjustable

If you are using iZotope Ozone or similar:

Module	Suggested Settings
EQ	High shelf -1 to -2 dB above 8 kHz to tame AI harshness
Multiband Compressor	Light compression, 2-3 dB gain reduction max
Maximizer/Limiter	Ceiling at -1 dBTP, target -14 LUFS output

Platform-specific considerations

Spotify

Spotify normalizes during playback based on user settings:

Normal mode: -14 LUFS target
Loud mode: -11 LUFS target (applies limiter to prevent distortion)
Quiet mode: -19 LUFS target

A -14 LUFS master works well for all modes. Spotify also normalizes albums together, so softer tracks remain proportionally softer. This is intentional and preserves album dynamics.

Apple Music

Apple's Sound Check feature targets -16 LUFS. A track mastered to -14 LUFS will be turned down by about 2 dB. This is not a problem and does not affect quality.

YouTube

YouTube only turns audio down, never up. If your track is quieter than -14 LUFS, it plays at that level and may sound soft compared to other content. For YouTube uploads, consider mastering slightly louder (around -12 to -14 LUFS) to maintain presence.

Fixing common AI music issues

Harsh high frequencies (Suno vocals)

Apply a high shelf EQ cut of 1-3 dB starting around 6-8 kHz. Alternatively, use a de-esser if the harshness is concentrated in sibilance.

Ambient "sheen" or unwanted reverb

This is difficult to remove completely. Some mastering plugins have "de-reverb" features, but they work best on subtle issues. For heavy ambient issues, the track may need to be regenerated.

Artifacts or glitches

Mastering cannot fix obvious digital artifacts. If you hear clicks, pops, or unnatural transitions, try regenerating the track in your AI tool or edit them out manually in a DAW.

Inconsistent sections

If volume jumps between sections, use multiband compression or automation to even out levels before limiting.

When to skip mastering

Not every AI track needs mastering. Skip it if:

The track measures close to -14 LUFS already
True peak is below -1 dBTP
It sounds clean on multiple playback systems
You are testing whether a track resonates before investing time

For experimental releases or rapid iteration, uploading unmastered tracks is acceptable. You can always replace the track later with a mastered version (though this resets streaming stats on some platforms).

Quality vs quantity consideration

AI music makes it easy to generate hundreds of tracks. Mastering each one individually becomes a bottleneck.

For high-volume creators:

Use batch processing in LANDR or similar services
Apply consistent settings across similar tracks
Focus mastering effort on tracks that perform well

For quality-focused releases:

Master each track individually
Consider professional mastering for key singles
A/B compare before and after to ensure improvement

The goal of mastering is to make your AI music competitive with professionally produced tracks. With modern AI mastering tools, achieving this takes minutes rather than hours. A well-mastered track sounds cleaner, translates better across playback systems, and gives listeners a more professional impression of your music.