An Expert Analysis of the AI Music Generation Landscape in 2025

The New Frontier of AI Music Generation: A Market Overview

Introduction: From Novelty to Creative Engine

The field of artificial intelligence-driven music generation has undergone a dramatic and rapid transformation. What was once a niche technological curiosity, producing rudimentary and often cacophonous outputs, has evolved into a powerful and increasingly accessible creative engine. This technological leap is fundamentally democratizing the act of music creation, empowering a new generation of creators—from marketers and filmmakers to aspiring artists and hobbyists—to produce high-quality, customized audio with unprecedented ease.¹ The traditional barriers to entry, such as the need for formal musical training, expensive equipment, or professional studio time, are being systematically dismantled by these sophisticated new tools.²

This paradigm shift has been met with a mixture of excitement and apprehension across the creative industries. For many, AI music generators represent a new frontier of creative expression, offering a way to overcome writer’s block, rapidly prototype ideas, and bring musical concepts to life that were previously unrealizable due to a lack of technical skill or resources.⁴ Anecdotal evidence from forums and user testimonials speaks to the profound personal impact of these tools, enabling lifelong lyricists who cannot sing to finally hear their words performed, or amateur musicians to flesh out nascent ideas into fully produced tracks.⁵ However, this explosion in creative potential is shadowed by profound legal and ethical questions, particularly concerning copyright, the value of human artistry, and the very definition of creativity. The rise of platforms capable of generating entire songs, complete with human-like vocals, has ignited a fierce debate and high-stakes legal battles that threaten to reshape the music industry.⁶ This report will navigate this complex and dynamic landscape, providing a comprehensive analysis of the leading platforms, their capabilities, and the critical trade-offs between power and peril that every user must now consider.

Market Categorization: Understanding the Tiers of AI Music Generation

To effectively navigate the burgeoning market of AI music generation, it is essential to understand its distinct segments. The platforms available today are not monolithic; they cater to different user needs, technical abilities, and risk tolerances. The market can be broadly categorized into four primary tiers, each defined by its core functionality and target audience.

Tier 1: All-in-One Song Creators (Text-to-Song with Vocals)

This is the most advanced and disruptive category, defined by platforms that can generate complete, ready-to-share songs from a single text prompt. These tools integrate composition, lyric writing, vocal performance, and production into a seamless, automated process. The undisputed leaders in this space are Suno and Udio.⁶ Their ability to conjure original compositions with remarkably human-like vocals has captured the public’s imagination and positioned them at the vanguard of the industry.⁹ However, their technological prowess is matched by the controversy surrounding them, as they are at the center of major legal challenges from the music industry regarding their training data.⁶ Another platform,

SendFame, aims to push this concept even further by bundling full song generation with AI-created music videos and album art, offering a “complete artistic package” from a single interface.¹²

Tier 2: Instrumental & Background Music Generators

This tier consists of tools designed primarily for creators who require high-quality, customizable instrumental music. Their main use case is providing soundtracks for videos, podcasts, advertisements, games, and other multimedia projects. These platforms typically prioritize user control, customization, and, crucially, legal safety. Key players in this category include Soundraw, AIVA, Beatoven, and Ecrett Music.⁴ Unlike the Tier 1 platforms, these tools often build their value proposition on providing royalty-free licenses and using ethically sourced or proprietary training data, offering a more risk-averse solution for commercial users.¹³

Tier 3: Developer-Focused Models & APIs

This category serves a more technical audience, including developers, researchers, and enterprises who wish to integrate generative audio capabilities directly into their own applications, products, or workflows. The most prominent example is Stable Audio, developed by Stability AI.¹⁷ Stable Audio offers both a polished, user-facing product and a suite of tools for developers, including an API and open-source models that can be fine-tuned and deployed independently.¹⁸ This dual approach allows for both direct use and deep technical integration. Other platforms, such as

Soundraw, also provide API access for enterprise clients, recognizing the growing demand for programmatic music generation.¹⁶

Tier 4: Niche & Experimental Tools

This tier encompasses a range of platforms that serve more specific or experimental purposes. Boomy, for instance, focuses on extreme ease of use, allowing users with no experience to generate songs with a single click and then facilitating their distribution to streaming services for potential monetization.¹ Its interface is described as “stupidly simple,” prioritizing accessibility over deep creative control.⁹ On the other end of the spectrum is

Riffusion, a free and experimental tool that generates music from spectrograms, often used for creating interesting loops, weird sounds, and exploring unconventional sonic textures.⁹ These tools cater to hobbyists, students, and those looking to experiment at the edges of AI music without significant financial or time investment.

Initial Market Thesis: The Great Divide

The AI music generation market in 2025 is defined by a fundamental schism, a great divide that forces every potential user to make a strategic choice. This is not merely a difference in features or pricing, but a deep-seated conflict in business philosophy and legal strategy. On one side of this divide stand the high-flying, all-in-one song creators, Suno and Udio. They offer the most breathtaking capabilities, delivering on the promise of turning a simple thought into a fully realized song with vocals. Yet, this power comes at a price: they are embroiled in existential legal battles with the recording industry over allegations that their models were trained on vast archives of copyrighted music without permission.⁶ Their very existence hangs in the balance of a “fair use” legal argument.

On the other side of the divide are platforms like Soundraw and Stable Audio. They are consciously building their value proposition on a foundation of what they market as “ethical AI”.¹³ Soundraw explicitly states that it trains its models on music created by its own team of producers, while Stable Audio’s open model is trained on licensed public datasets like Freesound and the Free Music Archive.¹⁶ This approach offers users a significantly lower-risk proposition, providing legally safer, royalty-free music. The trade-off, however, is that these platforms have historically focused on instrumental music, lacking the full text-to-song vocal capabilities of their more controversial counterparts.

This schism means the question “What is the best AI for music generation?” cannot be answered with a single name. The answer is contingent on the user’s own position on a spectrum of risk versus reward. A hobbyist creating a song for fun may be unconcerned with the RIAA’s lawsuit against Suno. A major corporation developing a global advertising campaign, however, will view that same lawsuit as an unacceptable liability. The market, therefore, is not just segmenting by function but by the user’s legal and commercial risk tolerance.

Furthermore, the very definition of “music generation” is expanding beyond the simple act of composition. The earliest AI music tools focused on creating symbolic data like MIDI files, leaving the production to the user.⁴ The current vanguard, led by Suno and Udio, has integrated composition, performance, and production into a single, seamless step.⁹ Now, platforms like SendFame are pushing the boundaries even further, bundling music generation with AI-powered creation of music videos and album art, effectively offering a complete, multimedia artistic package from a single prompt.¹² This trend suggests that the future of this technology lies not just in creating an audio file, but in generating an entire creative ecosystem around a musical idea. The “best” tool of tomorrow may be the one that offers the most integrated and comprehensive content creation suite.

The following table provides a high-level overview of the key platforms discussed in this report, allowing for a quick comparison of their core functions, target users, and, most importantly, their stance on the critical issue of training data.

Table 1.1: High-Level AI Music Generation Platform Comparison

Platform	Primary Function	Ideal User Persona	Key Differentiator	Pricing Model	Stated Training Data Policy
Suno	Text-to-Song (with Vocals)	Musician/Songwriter, Hobbyist	Generates full songs with impressive vocals from a text prompt.⁶	Freemium	Undisclosed (Subject of RIAA lawsuit).⁶
Udio	Text-to-Song (with Vocals)	Musician/Songwriter, Content Creator	High-fidelity output and advanced editing features like inpainting.¹¹	Freemium	Undisclosed (Subject of RIAA lawsuit).⁷
Soundraw	Customizable Instrumentals	Content Creator, Marketer	Ethically-sourced training data from in-house producers; royalty-free license.¹³	Freemium, Subscription	In-house producers and licensed data.¹³
AIVA	Customizable Instrumentals, MIDI Generation	Composer, Musician, Enterprise	Specializes in classical/symphonic styles; Pro plan sells full copyright ownership to the user.⁴	Freemium, Subscription	Trained on a large collection of classical music (e.g., Bach, Mozart).²⁴
Stable Audio	High-Fidelity Audio & SFX, Audio-to-Audio	Developer, Creator, Researcher	Offers both a commercial product and open-source models for integration.¹⁸	Freemium, Subscription, API Credits	Commercial: Undisclosed. Open Model: Licensed from Freesound & Free Music Archive.¹⁸
Boomy	Instant Song Generation	Hobbyist, Casual User	Extremely simple interface; streamlines distribution to streaming platforms for monetization.¹	Freemium, Subscription	Claims not to use third-party IP subject to copyright.²⁵

The Vanguard of Vocal Generation: A Head-to-Head Analysis of Suno and Udio

Introduction to the Contenders

In the rapidly advancing field of AI music, two names have risen above the rest to define the state of the art in full song generation: Suno and Udio. These platforms have captured widespread attention by achieving what was, until recently, the holy grail of generative audio: the creation of coherent, high-quality songs complete with instrumentation, lyrics, and remarkably realistic vocals, all from text-based prompts.⁹ They represent the premier competitors in the market’s most ambitious and technologically demanding segment.

Their rivalry is made all the more compelling by their shared heritage in the world of elite AI research. Suno was founded by a team with experience at pioneering tech companies like Meta, TikTok, and the AI startup Kensho.⁶ Udio, meanwhile, was launched by a team of former researchers from Google DeepMind, one of the world’s leading AI labs.¹¹ This background in cutting-edge AI development has positioned them as the two dominant forces pushing the boundaries of what is possible in music generation, setting the standard against which all other platforms are measured.

Core Capabilities: Sound, Structure, and Prompting

While both Suno and Udio operate on the same fundamental premise of text-to-song generation, they exhibit distinct characteristics in their output, creating a nuanced choice for users based on their creative goals.

Audio Quality and Fidelity

A critical point of comparison is the sonic character of the music each platform produces. Both generate audio at a quality that is often indistinguishable from human-produced tracks, especially to the casual listener.⁵ However, detailed comparative analyses and user reviews reveal subtle but important differences. Udio is frequently lauded for producing tracks that sound “crisper,” more “harmonically complex,” and sonically polished.¹¹ Its output is often described as having a higher fidelity and a more “human-like” feel, with tasteful effects that enhance the realism.²⁶ In contrast, Suno is praised for its high-energy output and its adeptness at blending genres to create novel sounds.²⁶ However, some analyses note that Suno’s tracks, while well-structured, can sometimes feel more “prosaic” or generic in their sonic texture compared to Udio’s more layered and atmospheric results.³⁰

Prompt Adherence and Creative Interpretation

The way each platform interprets a user’s prompt reveals a fundamental difference in their creative philosophies. Suno is noted for its strong adherence to prompts, reliably generating songs that are well-structured and closely aligned with the specified genre and mood.²⁶ This makes it an excellent tool for users who have a clear vision and need the AI to execute it faithfully. A user looking for a 30-second pop-punk jingle for an ad, for example, would likely find Suno’s predictability to be a significant asset.

Udio, on the other hand, is often described as more of a creative collaborator, exhibiting a tendency to be more “unpredictable,” “organic,” and surprising in its interpretations.²² It might deviate from a prompt in unexpected ways, introducing melodic or rhythmic twists that the user did not explicitly request. This can be a powerful feature for musicians seeking inspiration or a way to break through creative blocks, as the AI can feel like it has a “soul” of its own, offering novel ideas.³⁰ However, for a user who needs precise control, this serendipity can sometimes be a source of frustration. This dynamic creates a clear trade-off: Suno offers reliability and predictability, while Udio provides a more collaborative and potentially more surprising creative experience.

Genre Versatility

Both platforms demonstrate impressive versatility, capable of generating music across a vast spectrum of genres, from pop, rock, and EDM to country, jazz, and even niche styles like German pop or barbershop quartets.⁹ Direct comparisons have shown that both can excel in popular genres like rock and electronic music, producing tracks with solid buildups, punchy basslines, and coherent structures.³⁰ However, they can also struggle with more complex or historically nuanced genres. For instance, one analysis found that both platforms had difficulty generating joyful classical music, with Suno’s orchestral pieces tending toward cinematic tension and Udio repeatedly adding vocals to instrumental prompts.³⁰ This indicates that while their genre range is broad, the depth of their “understanding” of each genre can vary.

Vocal and Lyric Generation

The ability to generate high-quality vocals is the key feature that sets this tier of AI apart. Suno was a pioneer in this area, impressing users with its ability to generate not just music, but also lyrics and a sung performance from a single prompt.⁹ Udio is similarly praised for its “incredibly realistic and even emotional” vocal output, with some reviewers stating it has an “uncanny ability to capture emotion in synthetic vocals”.¹¹ Both platforms allow users to input their own lyrics for the AI to sing, or they can have the AI generate lyrics based on the prompt.⁹ While generally impressive, the AI-generated lyrics can sometimes be a weak point. Some users have noted that Suno’s lyrics can be “a bit generic or weird,” and Udio’s can sometimes devolve into “utter gibberish” as a song progresses.⁹

Advanced Features and Creative Control

The initial “magic” of generating a song from a prompt is quickly becoming table stakes in the high-end AI music market. Consequently, Suno and Udio are engaged in a strategic arms race to provide users with more powerful and intuitive tools for editing and refining the AI’s output. This focus on post-generation control is a direct response to one of the biggest limitations of early AI music tools: the lack of fine-grained creative control. The platforms that can best bridge the gap between raw AI generation and the detailed editing capabilities of a traditional Digital Audio Workstation (DAW) are likely to win the loyalty of more serious musicians and producers.

Track Extension and Structure

The core workflow on both platforms involves generating short musical clips (typically 30-33 seconds) and then extending them to build a full-length song.²² This iterative process allows users to guide the song’s structure, deciding which sections to keep and where to go next. Suno’s V3 model, for example, enabled the creation of 4-minute songs, a significant leap at the time.⁶ Udio also supports the creation of extended tracks, with some reports suggesting lengths of up to 15 minutes are possible.²⁶

Editing and Inpainting

This is a key battleground where Udio has established a notable lead. Udio offers a suite of advanced editing functions, including a “Crop & Extend” feature for trimming segments and, most significantly, a feature called “Inpainting”.²² Inpainting is a game-changer, allowing for segment editing on a granular level. Users can select specific regions within a track and have the AI regenerate material only in those areas, enabling fine-tuned adjustments to vocals, instrumentation, or solos without affecting the rest of the song.²² This level of control moves the platform closer to a professional production tool. Suno also offers editing capabilities on its paid plans, including a powerful stem separation feature that can split a track into up to 12 individual vocal and instrument stems, giving users full control over the mix.³³

Audio Uploads

Recognizing the needs of musicians who have their own pre-existing ideas, both platforms have introduced the ability for users to upload their own audio clips.³³ This powerful feature allows a user to upload a melody, a drum loop, or a vocal snippet and have the AI build a full track around it, transforming the tool from a pure generator into a sophisticated collaborative partner.

User Interface and Experience

Both Suno and Udio are generally praised for their intuitive, user-friendly interfaces that make the complex process of music generation accessible to non-musicians.⁹ Suno offers a dedicated mobile app and a notable integration with Microsoft Copilot, bringing its technology to a massive user base.⁶ Udio has also launched its own iOS app, recognizing the importance of mobile creation.³⁵ A particularly clever feature of Udio’s web interface is its community feed, which not only allows users to discover music made by others but also to copy the exact prompts and tags used to create those tracks, providing an invaluable learning tool for mastering the art of prompting.²²

Pricing and Commercial Use

The pricing structures and commercial rights offered by Suno and Udio are broadly similar, with both tying commercial usage rights to paid subscriptions. This is a critical consideration for any user who intends to monetize their AI-generated creations.

Suno Pricing

Suno operates on a freemium model with three main tiers ³:

Free Plan: Offers 50 credits per day (enough for about 10 songs). This plan uses the older v3.5 model and is strictly for non-commercial use.
Pro Plan: Costs $8 per month when billed annually. This plan provides 2,500 credits per month (approx. 500 songs), access to the latest and most advanced models (v4 and v4.5), and grants the user commercial use rights for any songs created while subscribed. It also unlocks pro features like stem separation and priority processing.
Premier Plan: Costs $24 per month when billed annually. This plan increases the credit allowance to 10,000 per month (approx. 2,000 songs) and includes all the features of the Pro plan.

Udio Pricing

Udio also uses a freemium model with a generous free offering and two paid tiers ³²:

Free Plan: Provides users with 10 credits per day, with a monthly cap of 100 credits.
Standard Plan: Costs $10 per month. This plan provides 1,200 credits per month, priority processing, and unlocks advanced features like audio uploads, inpainting, and custom cover art.
Pro Plan: Costs $30 per month. This plan increases the credit allowance to 4,800 per month and provides early access to new features, including the 2-minute generation model.

For both platforms, the message is clear: casual experimentation is free, but any form of commercialization, from posting on a monetized YouTube channel to releasing on Spotify, requires a paid subscription.

The Creator’s & Prosumer’s Toolkit: A Comparative Analysis of Leading Platforms

Beyond the high-profile rivalry of Suno and Udio, a diverse ecosystem of AI music generators has emerged, each catering to specific user needs and, in many cases, offering a more conservative and legally sound approach to creation. These platforms are the workhorses for content creators, the specialized assistants for professional musicians, and the accessible entry points for hobbyists.

Soundraw: The Ethically-Sourced Workhorse

Soundraw has carved out a distinct and powerful position in the market by building its entire platform on a foundation of legal safety and ethical data sourcing. Its core proposition is the generation of unique, high-quality, royalty-free instrumental music that commercial users can employ with confidence.¹³ This is achieved through a deliberate and transparent approach to its AI training: Soundraw explicitly states that its models are trained on original sounds and musical patterns created by its own in-house team of producers, not by scraping the internet for copyrighted material.¹³ This strategy is a direct counter-position to the legal ambiguity surrounding its Tier 1 competitors and is its primary selling point for risk-averse businesses and creators.

The platform’s functionality is tailored for efficiency and customization. Instead of relying on open-ended text prompts, users generate music by selecting from a structured menu of parameters, including genre, mood, theme, track length, and tempo.³⁹ Once the AI generates a selection of 15 tracks, users can engage in real-time customization, modifying the instrumental structure by adjusting the intensity of different sections (e.g., intro, verse, chorus) or changing the instrumentation.⁴ This parameter-driven approach is ideal for quickly finding suitable background music that fits the specific needs of a video or podcast.

Soundraw’s licensing model is straightforward and designed for commercial use. It offers a “Creator” plan and several “Artist” plans, with annual prices ranging from approximately $11 to $33 per month.¹⁶ A subscription grants the user a perpetual, royalty-free license to use the generated music in commercial projects, including monetization on YouTube and distribution to streaming services like Spotify.¹⁴ This clear and comprehensive license makes it an ideal choice for its target audience: content creators, YouTubers, podcasters, marketers, and small businesses that require a reliable and legally unencumbered source of high-quality background music.⁴ The platform has also demonstrated its quality through collaborations with major artists like French Montana and Trippie Redd, and it offers an API for enterprise-level integration.¹⁶

AIVA: The Classical Virtuoso Turned Multi-Genre Composer

AIVA, which stands for Artificial Intelligence Virtual Artist, began its journey with a specialized focus on classical and symphonic music. Its initial models were trained on a vast collection of works from public domain composers like Bach, Beethoven, and Mozart, allowing it to learn the deep structures of harmony and composition.²⁴ This foundation in musical theory has enabled AIVA to evolve into a versatile composer, now capable of generating music in over 250 different styles, including modern genres like rock, pop, and jazz.²³

The platform’s strength lies in its ability to generate highly structured and melodic compositions. However, its most significant feature for professional musicians is the ability to export generated tracks as MIDI files.⁴ This functionality is a crucial bridge between AI generation and professional music production. A composer can use AIVA to generate a complex orchestral idea, export the MIDI data, and then import it into their own professional Digital Audio Workstation (DAW). There, they can edit every single note, re-assign instruments to high-end sample libraries, and integrate the AI-generated composition seamlessly into their human-driven workflow. AIVA also includes a DAW-like editor within its own platform for more immediate adjustments.⁴⁰

This professional focus is reflected in AIVA’s unique licensing model, which introduces the concept of “copyright-as-a-feature.” While its Free and Standard (€11/month) plans follow the typical model where AIVA retains ownership of the copyright, its Pro plan (€33/month) offers a groundbreaking proposition: it grants the user full copyright ownership of their compositions.²³ This is a major differentiator that moves the platform from being a service that rents out music to a tool that creates a tangible, ownable asset. For serious artists, film composers, and game developers who need to own the intellectual property they create, this feature is invaluable. This makes AIVA the premier choice for professionals who require deep editing capabilities and prioritize the legal ownership of their work.⁴

Boomy: The Gateway to Instant Music Creation and Monetization

Boomy is designed with a singular focus on accessibility, aiming to democratize music creation for users with absolutely no prior musical experience.¹ Its core philosophy is to make the process as simple as possible, encapsulated by a workflow that is often described as “click a button, get a song”.⁹ The user experience is stripped down to its essentials: a user selects a style, such as lo-fi, EDM, or rap, and the AI instantly generates a complete track.¹ This “stupidly simple interface” removes nearly all technical and creative barriers, making it an appealing entry point for the curious.⁹

While it offers some tools for customization, such as adding AI vocals or adjusting arrangements, Boomy is not intended to be a replacement for a full-featured DAW.¹ Its standout feature is not its generative power but its streamlined distribution pipeline. Boomy makes it exceptionally easy for users to take their AI-generated songs and submit them to over 40 major streaming platforms, including Spotify and Apple Music, with the potential to earn royalties from the streams.¹

Boomy operates on a freemium model. The free plan allows for song generation with limited saves, while paid plans—Creator at $9.99 per month and Pro at $29.99 per month—offer more saves, MP3 downloads, and commercial use rights.¹ By default, Boomy retains the copyright to the music generated on its platform, but the paid plans grant subscribers a license for commercial use of the songs they download.⁴⁵ This positions Boomy as the ideal tool for hobbyists, casual users, and aspiring artists who want to experiment with song creation in a fun, low-stakes environment and are attracted by the simple, integrated path to potential monetization.¹

Stable Audio: The Developer’s Choice and High-Fidelity Challenger

Emerging from Stability AI, the company renowned for the open-source image model Stable Diffusion, Stable Audio brings a similar dual-pronged strategy to the audio domain. It exists as both a polished commercial product for creators and a powerful set of open-source models and tools for developers, researchers, and enterprises.¹⁷ This makes it a unique and formidable player in the market.

Its core technology is built on a latent diffusion model architecture, which differs from the transformer-based models used by many competitors and is known for producing high-fidelity audio.⁴⁶ The commercial product, Stable Audio 2.0, introduced two key features: the ability to generate high-quality, coherent audio tracks up to three minutes long, and a powerful

audio-to-audio generation capability.¹⁸ This allows a user to upload an audio sample—be it a hummed melody or a drum beat—and use a text prompt to transform it into a fully fleshed-out piece of music, offering a powerful new workflow for creators with existing ideas.¹⁸

Simultaneously, Stability AI has released Stable Audio Open, an open-source model designed for generating short audio samples, sound effects, and production elements.¹⁸ Crucially, this model was trained on an ethically sourced dataset licensed from the audio libraries Freesound and the Free Music Archive, making it a legally sound foundation for developers to build upon.¹⁸ The platform’s licensing reflects its dual audience. It offers a free tier for non-commercial use (10 tracks/month) and several paid plans (starting with Pro at $11.99/month) that grant commercial use licenses for the generated audio.⁴⁹ For developers, the open-source models are available under their own permissive licenses, and an API allows for deep integration into third-party applications.¹⁸ This positions Stable Audio to serve two distinct but important markets: creators who demand the highest sonic fidelity and developers who require a robust, transparent, and legally vetted foundation for building the next wave of audio applications.

The market’s evolution reveals a fascinating three-way philosophical split regarding the data used to train these powerful models. This divergence goes beyond mere technical specifications and shapes the legal risk, transparency, and ethical posture of each platform. The first approach, exemplified by Suno and Udio, can be termed the “Undisclosed/Scraped Data” model. These platforms have not disclosed the datasets used for training, but the quality and versatility of their output have led to widespread belief and legal allegations that they were trained on massive amounts of copyrighted material scraped from the internet without license.⁶ This approach yields the highest capability but also carries the highest legal risk for both the platform and potentially its users.

The second approach is the “Proprietary/In-house Data” model, championed by Soundraw.¹³ Here, the company invests in creating its own dataset from scratch, hiring musicians and producers to create original music and sounds specifically for the purpose of training its AI. This method offers a high degree of legal safety and quality control but operates as a “black box,” with the training data remaining a private, proprietary asset.

The third philosophy is the “Public/Permissive Data” model, used by platforms like AIVA and Stable Audio for some of their offerings. AIVA’s models were initially trained on the vast corpus of public domain classical music, a dataset that is inherently free of copyright restrictions.²⁴ Similarly, Stable Audio’s open-source model was explicitly trained on permissively licensed content from public repositories like Freesound and the Free Music Archive.¹⁸ This approach offers the greatest transparency and the lowest legal risk but may be limited by the scope and quality of the available public data. When choosing a platform, a user is therefore implicitly selecting which of these data philosophies they trust and wish to support, a decision with significant ethical and legal ramifications.

The Copyright Conundrum: Navigating Legal Risks and Licensing

The meteoric rise of generative AI music has created a legal and philosophical crisis for copyright law. The core question—who, if anyone, owns AI-generated music?—is the single most important consideration for any professional creator or business looking to use these tools. The answer is complex, evolving, and varies dramatically between platforms, creating a minefield of potential risks and opportunities.

The “Human Authorship” Doctrine: The U.S. Copyright Office’s Stance

The foundational principle of United States copyright law is the requirement of human authorship. According to the U.S. Copyright Office (USCO), for a work to be eligible for copyright protection, it must be the product of human creativity.⁵² This single doctrine has profound implications for AI-generated music.

The USCO has clarified its position through recent guidance and court rulings. A work created solely by an AI system, without sufficient human creative input, cannot be copyrighted.⁵⁴ In the context of music generation, simply writing a text prompt (e.g., “a sad country song about a lost dog”) is not considered sufficient creative input to claim authorship of the resulting song. The USCO views the prompt as an unprotectable “idea,” not a fixed creative expression, and notes that the user lacks the necessary control over the final output, as the AI makes the expressive choices that constitute the musical work.⁵⁴ Even the process of “prompt engineering”—iteratively refining a prompt to achieve a desired result—is not enough to warrant copyright protection for the final output.⁵⁴

However, the situation changes when AI is used as a tool in a collaborative process with a human creator. In such cases, the resulting work can be copyrighted, but the protection extends only to the elements created by the human. For example:

If a human writes original lyrics and uses an AI to generate the accompanying music, the lyrics are copyrightable, but the AI-generated music itself is not.⁵⁴
If a human takes multiple AI-generated elements (e.g., a drum beat, a bassline, a synth melody) and arranges them into a new composition, the arrangement can be copyrighted if the human’s contribution of selection and coordination is “sufficiently creative.” However, the individual AI-generated elements remain uncopyrightable.⁵⁴

This creates a “copyright void” where individual AI-generated musical phrases, no matter how brilliant or unique, effectively enter a new form of public domain. If one user generates a catchy melody, another user could theoretically generate the same or a very similar melody and use it without infringing on any copyright, as the original element was never protectable in the first place. This lack of protection for the raw AI output incentivizes creators to add their own significant creative input—be it through performance, arrangement, or deep editing—to secure any form of legal ownership over their final product.

The Elephant in the Room: The Suno and Udio Lawsuits

The abstract principles of copyright law have collided with the realities of the market in a series of landmark lawsuits filed against Suno and Udio by a coalition of major record labels, publishers, and artist groups, including the Recording Industry Association of America (RIAA) and Universal Music Group.⁶ The core allegation is copyright infringement on a massive scale. The lawsuits claim that to achieve their impressive results, the platforms must have trained their AI models on vast quantities of commercially released, copyrighted music without obtaining licenses or providing compensation to the rights holders.⁶ The plaintiffs are seeking statutory damages that could reach up to $150,000 per infringed work, a figure that could amount to an existential threat to the AI companies if the suit is successful.⁷

The AI platforms are expected to argue that their training process constitutes “fair use,” a complex legal doctrine that permits the limited use of copyrighted material without permission for purposes such as criticism, commentary, and research. However, legal experts and the USCO itself have signaled that this will be a difficult defense to mount. The commercial nature of the platforms, the sheer volume of data allegedly used, and the fact that their output directly competes with and potentially harms the market for human-created music (a concept known as “market dilution theory”) all weigh against a finding of fair use.²¹

The outcome of these lawsuits will have monumental consequences for the entire generative AI industry. In the meantime, the platforms are not standing still. In a move widely seen as an attempt to demonstrate responsible behavior to the industry, Udio has partnered with the content identification company Audible Magic. This partnership will create a “content control pipeline,” automatically fingerprinting and registering every track generated on Udio’s platform, allowing streaming services and rights holders to easily identify Udio-generated content and apply appropriate licensing rules.²¹ While this does not resolve the issue of their training data, it is a strategic step toward building a more transparent and manageable ecosystem. For users, this legal battle creates a cloud of uncertainty. Choosing to use a platform like Suno or Udio is no longer a simple consumer decision; it is an implicit alignment with a high-stakes legal argument. While the lawsuits target the companies, not the end-users, a business that builds a major campaign around a song generated by a platform later found guilty of mass infringement could face significant reputational damage and potential legal entanglements.

A Practical Guide to Platform Licensing Models

Navigating the rights granted by each platform is crucial for any creator. The terms vary significantly based on the platform and the user’s subscription tier. Understanding these differences is essential for avoiding legal trouble and maximizing commercial opportunities.

Full Copyright Ownership: This is the gold standard for creators who want complete control over their work. Currently, AIVA’s Pro plan is the most prominent example of a platform that explicitly transfers full copyright ownership of the generated composition to the user.²³ This makes the user the legal author and owner of the intellectual property.
Broad Commercial Use License: This is the most common model for paid subscription tiers. Platforms like Suno (Pro/Premier plans), Udio (Standard/Pro plans), Soundraw, and Stable Audio (paid plans) grant the user a broad, often perpetual, license to use the generated music for commercial purposes.¹⁶ This includes monetizing content on YouTube, using the music in ads, and distributing it on streaming services. However, under this model, the platform typically retains the underlying copyright to the composition itself, or the copyright status remains ambiguous under USCO rules. The user owns the
right to use the music, but not the music itself.
Non-Commercial License: This is the standard for all free tiers. The free plans offered by Suno, Udio, AIVA, Boomy, and Stable Audio all restrict the use of generated music to personal, non-commercial projects.²³ Any form of monetization is strictly prohibited.
Credit Requirements: Some free plans come with an additional stipulation: the user must provide attribution to the platform when using the music. AIVA’s free plan, for example, requires that credit be given to AIVA in any project where its music is used.²³

The following table provides a detailed breakdown of the licensing and copyright terms for the leading platforms, offering a critical tool for assessing legal risk and commercial viability.

Table 4.1: Detailed Copyright and Licensing Comparison

Platform	Free Tier Copyright Owner	Free Tier Usage Rights	Paid Tier Copyright Owner	Paid Tier Commercial License Scope	Stated Training Data Policy	Current Legal Status
Suno	Suno, Inc. ³¹	Non-commercial only.³³	User owns the generated audio, subject to terms.³¹	Full commercial rights for songs made while subscribed.³³	Undisclosed.⁶	Subject of RIAA Lawsuit.⁶
Udio	Udio ⁵⁶	Non-commercial, credit to Udio may be required.⁵⁶	User owns their human inputs; AI output is licensed.⁵⁶	Full commercial rights with subscription.²²	Undisclosed.¹¹	Subject of RIAA Lawsuit.⁷
Soundraw	SOUNDRAW inc. ³⁹	N/A (Free trial, not a persistent free plan)	SOUNDRAW inc. ³⁹	Perpetual, royalty-free commercial license for BGM and song distribution.¹⁶	In-house producers, proprietary data.¹³	No known litigation.
AIVA	AIVA ²³	Non-commercial, credit to AIVA required.²³	User (on Pro Plan); AIVA (on Standard Plan).²³	Full monetization with Pro plan; limited monetization with Standard plan.²³	Large collection of classical music and other styles.²⁴	No known litigation.
Stable Audio	Stability AI ¹⁷	Non-commercial only.¹⁷	User owns the output, subject to terms.¹⁷	Commercial use rights with paid subscription.¹⁷	Open Model: Freesound/FMA. Commercial: Undisclosed.¹⁸	No known litigation.
Boomy	Boomy ⁴⁵	Non-commercial, limited saves/releases.¹	Boomy (retains copyright).⁴⁵	Full commercial rights for downloaded songs while subscribed.⁴⁵	Claims not to use third-party IP subject to copyright.²⁵	No known litigation.

Under the Hood: A Practical Guide to the Technology

While the outputs of AI music generators can seem magical, they are grounded in sophisticated but comprehensible technological principles. Understanding the basic concepts behind how these models “think” about music can empower users to craft better prompts and make more informed choices about which platform best suits their creative process.

The Two Schools of Thought: Symbolic vs. Direct Audio Generation

At a fundamental level, AI music models approach creation in one of two ways: they either generate a symbolic representation of music or they generate the audio signal directly. This technical distinction has a significant impact on the output’s characteristics and editability.

Symbolic Generation

This is the older of the two approaches and is still utilized by some platforms, most notably as a core feature of AIVA.⁴ In symbolic generation, the AI creates music as a set of instructions or data points, much like a digital score. The output is typically a MIDI file or a piano roll, which contains information about which notes to play, when to play them, and for how long, but not the actual sound itself.⁵⁷ The timbre, or the specific sound of the instruments (e.g., a Steinway piano vs. a Fender Rhodes), is applied in a separate step, either by the platform’s built-in synthesizers or by the user in their own DAW.

Advantages: Symbolic output is incredibly editable. A user can change every single note, alter chord progressions, and adjust rhythms with precision. The file sizes are also very small, and it is an excellent method for generating compositional ideas and structures.⁵⁷
Disadvantages: This approach inherently lacks control over the nuances of performance and timbre. It cannot easily capture the subtle variations in articulation, dynamics, and texture that give music its “human feel”.⁵⁷

Direct Audio Generation

This is the modern approach that powers the leading platforms, including Suno, Udio, and Stable Audio.¹¹ These models bypass the symbolic step and generate the final audio product—the digital waveform or its visual representation, the spectrogram—directly.⁵⁷ The AI learns not just the notes and rhythms, but the complete sonic characteristics of the music, including the timbre of the instruments, the texture of the recording, and the expressive nuances of a vocal performance.

Advantages: This method produces highly realistic and sonically rich audio that can include complex vocal performances and detailed production effects. It captures the holistic “sound” of a recording, not just its compositional blueprint.⁵⁷
Disadvantages: Direct audio generation is computationally intensive, requiring significant processing power.⁵⁷ Historically, the resulting audio files have been difficult to edit at a granular, note-by-note level, though the advent of features like inpainting and stem separation is rapidly changing this.

A Look at the AI Architectures

The specific type of neural network architecture a platform uses directly influences its capabilities and the character of its output. While the exact details are often proprietary, the research points to a few dominant model types.

Transformer Models: This is the architecture that revolutionized natural language processing and powers models like ChatGPT. It has proven to be exceptionally effective for music generation as well. Suno is confirmed to use a transformer-based autoregressive model.⁵⁸ These models excel at understanding sequences and long-range dependencies, which is crucial for creating music that has a coherent structure and evolves logically over time.⁵⁷ The CEO of Suno, Mikey Shulman, has offered a compelling, if cartoonish, summary of the output style: autoregressive models like transformers tend to make “really beautiful music that sounds like it was poorly recorded,” suggesting they are strong on musicality and structure but perhaps less so on pristine audio fidelity.⁵⁸
Diffusion Models: Made famous by image generators like DALL-E and Stable Diffusion, this architecture is the foundation for Stable Audio.⁴⁶ Diffusion models work by starting with random noise and progressively refining it, step-by-step, into a coherent output that matches the user’s prompt.⁶¹ They are renowned for their ability to generate extremely high-fidelity and texturally rich outputs. Continuing Mikey Shulman’s analogy, diffusion models tend to make “great sounding elevator music that’s a little boring,” implying they excel at sonic quality but may sometimes lack the compositional interest of their transformer-based counterparts.⁵⁸
Variational Autoencoders (VAEs): VAEs are often used as a critical component in conjunction with other models, particularly diffusion models, as seen in Stable Audio’s architecture.⁴⁶ A VAE is a type of neural network that learns to compress complex data, like an audio file, into a much smaller, more efficient representation called a “latent space.” The main generative model then works within this compressed space, which dramatically speeds up the training and generation process. Afterward, the VAE’s decoder reconstructs the full-quality audio from the generated latent representation.⁴⁷

The Art of the Prompt: How to “Talk” to a Music AI

The quality of the output from a generative AI is directly proportional to the quality of the input. Crafting an effective prompt is an art form that requires specificity and detail.

The single most important principle is to be descriptive. A vague prompt like “rock song” will yield a generic result. A far more effective prompt would be: “gritty rock song with heavy guitar riffs, moody vocals, and dark lyrics about heartbreak”.⁹ This provides the AI with much more information to guide its creation.

Effective prompts often include several key elements:

Genre and Style: Be specific (e.g., “90s alternative rock,” “Chicago house,” “cinematic orchestral”).
Mood and Vibe: Use evocative adjectives (e.g., “uplifting and hopeful,” “dark and mysterious,” “chill lo-fi with a rainy night vibe”).
Instrumentation: Name the specific instruments you want to hear (e.g., “featuring a soulful saxophone solo,” “driven by a punchy 808 drum machine”).
Tempo and Rhythm: Include terms like “slow tempo,” “fast-paced,” or “a four-on-the-floor kick pattern.”
Structural Elements: Some platforms allow for tags like [Verse], [Chorus], or `` within the prompt to guide the song’s structure.¹⁰

Platforms like Soundraw and Beatoven use a more structured, tag-based approach where users select these elements from menus rather than writing them in free text.¹³ Udio also provides suggested tags below its prompt box to help users get started.¹⁰ While mastering the art of prompting can lead to vastly superior results, it is important to remember the legal context: under current USCO guidelines, even highly skilled “prompt engineering” does not grant the user copyright over the AI’s output, as the creative expression is still deemed to originate from the machine.⁵⁴

Strategic Decision Framework: Selecting the Optimal AI Music Generator

Defining Your Primary Goal: Creation, Collaboration, or Content?

The preceding analysis demonstrates that there is no single “best” AI for music generation. The optimal choice is entirely dependent on the user’s specific goals, technical needs, creative ambitions, and tolerance for legal risk. Therefore, the selection process must begin with a clear definition of the primary objective. Is the goal to quickly create background content for a video? Is it to engage in creative collaboration with an AI to write a new song? Or is it to facilitate the creation of a professional musical asset? By identifying the primary use case, a user can narrow the field of potential platforms to those best suited for the task at hand.

Recommendations by User Persona

This section synthesizes the report’s findings into specific, justified recommendations for the key user personas that have emerged from the market analysis.

Primary Needs: The foremost requirements for this persona are speed, ease of use, affordability, and, most critically, a legally safe, royalty-free license that eliminates the risk of copyright strikes or future legal complications. The music serves as a background element to enhance their primary content.
Top Recommendations: Soundraw, Ecrett Music.
Justification: These platforms are purpose-built for this use case. Soundraw’s explicit commitment to using ethically sourced, in-house training data provides a strong defense against copyright claims, making it a very safe choice for commercial use.¹³ Ecrett Music is similarly focused on providing simple, royalty-free licensing at an affordable price point.¹⁵ Their parameter-driven generation (selecting mood, genre, etc.) is often faster and more efficient for finding suitable background tracks than trying to craft the perfect free-text prompt.

For the Independent Musician, Songwriter, or Producer

Primary Needs: This user demands high creative potential, excellent audio quality (especially for vocals), fine-grained editing control, and tools that can serve as a source of inspiration. Ownership and the ability to integrate AI output into a professional workflow are also key considerations.
Top Recommendations: Udio, Suno, AIVA.
Justification: Udio and Suno are the undisputed leaders in generating full songs with vocals, making them indispensable for songwriters looking to quickly prototype ideas or hear their lyrics performed.⁹ Udio’s advanced editing features like inpainting and its tendency toward “serendipitous” output make it a powerful tool for creative exploration.²² Suno’s predictability and strong structural coherence can be an asset for those with a clear vision.³⁰ For this persona, the significant legal risks associated with Suno and Udio may be a calculated and acceptable trade-off for their unparalleled creative capabilities. For the musician who prioritizes intellectual property ownership above all else,
AIVA is the premier choice. Its Pro plan, which grants the user full copyright ownership, and its ability to export to MIDI for use in a professional DAW, make it a unique and powerful tool for creating ownable musical assets.⁴

For the Developer, Enterprise User, or Technical Researcher

Primary Needs: This persona requires robust and reliable models, well-documented API access for integration into their own products, clear licensing terms for commercial development, and, for researchers, open-source options that allow for experimentation and fine-tuning.
Top Recommendations: Stable Audio, Soundraw API.
Justification: Stability AI is a leader in providing foundational models for the developer community, and Stable Audio continues this tradition with its powerful API and its open-source offerings.¹⁸ The fact that its open model is trained on permissively licensed data is a significant advantage for developers building commercial applications who need a legally sound base.¹⁸ Soundraw also offers a well-supported API for businesses looking to programmatically integrate high-quality, legally safe background music into their platforms.¹⁶

For the Hobbyist, Student, or Casual Experimenter

Primary Needs: The primary drivers for this user are fun, exploration, and learning, with minimal financial commitment. A generous free tier, an engaging user experience, and a vibrant community are highly valued.
Top Recommendations: Suno, Udio, Boomy.
Justification: Suno and Udio offer exceptionally capable and generous free tiers that allow for a significant amount of creation without any payment, providing the perfect sandbox for experimentation.³² The non-commercial use restrictions of these free tiers are not a barrier for this user group. For those seeking the absolute simplest entry point,
Boomy is the ideal choice. Its one-click generation process provides instant gratification and a fun way to engage with AI music creation.¹

Final Verdict: The State of AI Music in 2025 and Beyond

The AI music generation landscape in 2025 is a vibrant, chaotic, and rapidly evolving space. It is a market defined by a central tension: the trade-off between unprecedented creative power and profound legal uncertainty. As such, a definitive “best” platform does not exist in a vacuum; the optimal choice is a strategic decision that must align with a user’s specific goals and risk profile.

The analysis leads to a clear, multi-faceted conclusion:

For raw capability and the generation of complete songs with vocals, Udio and Suno are in a class of their own. They represent the current peak of generative music technology. The choice between them hinges on a preference for Udio’s creative serendipity and advanced editing versus Suno’s structural predictability and speed. However, any user, particularly a commercial one, must approach these platforms with a full understanding of the significant and unresolved legal risks associated with their training data.
For legally safe, commercially viable instrumental music, Soundraw is the standout choice. Its transparent and ethical approach to data sourcing, combined with a user-friendly interface and robust customization features, makes it the most prudent and reliable option for content creators and businesses.
For composers and artists who demand full copyright ownership of their work, AIVA offers a unique and invaluable proposition. Its Pro plan’s transfer of copyright is a powerful feature that sets it apart, making it the essential tool for professionals building a catalog of ownable intellectual property.
For developers and the technical community, Stable Audio provides the most powerful, flexible, and forward-looking toolkit, offering both a high-fidelity commercial product and a legally vetted open-source foundation for building the next generation of audio applications.

Ultimately, the user’s journey into AI music generation must be guided by a clear-eyed assessment of their own needs. The choice is no longer just about the quality of the sound, but about the soundness of the legal foundation upon which it is built. This tension between creative ambition and legal pragmatism will continue to define the AI music landscape for the foreseeable future, shaping the tools, the industry, and the very nature of music creation itself.

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June 26, 2025

What is the best ai for music generation?