Creative AI · Posted by Diana Okafor ·

Getting Started with AI Music Generation: Suno vs Udio vs Custom Models

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AI music generation has gotten incredibly good in 2026. Here’s my overview for anyone interested in getting started.

Suno: The easiest entry point. Type a description of what you want (“upbeat indie folk song about morning coffee”) and it generates a full song with vocals in under a minute. Quality is impressive for the ease of use. Free tier gives you about 5 songs per day.

Udio: Better for people who want more control. You can specify instruments, song structure, and style in more detail. The audio quality is slightly higher than Suno in most cases. Also has a free tier.

Custom models (for the technical): If you want full control, Stable Audio and Meta’s MusicGen are open-source options you can run locally. Requires technical setup but gives you unlimited generation and the ability to fine-tune on specific styles.

What you can realistically do with AI music:
– Create background music for videos and podcasts
– Generate demo tracks for songwriting ideas
– Create lo-fi/ambient playlists
– Produce custom jingles for business use

What you can’t really do yet:
– Generate music that sounds 100% professional and human (close but not quite)
– Replicate specific artist’s voices (and this is legally questionable)
– Create complex arrangements that match skilled human production

Anyone making music with AI? I’d love to hear what you’re creating.

6 replies

6 Replies

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suno's style tags are where the real control is. if you put in something like [genre: synthwave] [mood: melancholic] [tempo: 95bpm] in the lyrics prompt you get way more consistent results than just describing it in plain text

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the "not quite professional" thing depends heavily on genre. i've gotten udio outputs in ambient and lo-fi that genuinely fooled people in listening tests. but yeah anything with complex live instrumentation or jazz still falls apart pretty fast

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the copyright question around training data for these music models is going to be messier than the image stuff. at least with images the output looks different. with music the model can reproduce melodic structures pretty closely. lawsuits incoming

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genuine question - has anyone actually tried fine-tuning MusicGen on a specific artist or band's catalog for personal use? curious how much data you need before it actually captures a distinct style

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from what ive read you need like 2-4 hours of clean audio minimum to get anything coherent, and even then it captures vibe more than actual style. someone on huggingface posted a writeup on fine-tuning it on jazz trios, worth finding

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been using suno for podcast intros and honestly the turnaround is absurd. used to pay a guy on fiverr $40 per track and wait 3 days. now i iterate 10 versions in 20 minutes. the economics just don't make sense for simple stuff anymore