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Rating: ☕️☕️☕️ (3 Shots) - Great potential with room for improvement.
Background Context
My journey in music began as a vocalist, producer, and lyricist, culminating in performances before crowds of up to 10,000 people. Without formal training, I developed an unconventional approach to music creation, learning to play by ear and adapt across multiple instruments within digital audio workstations. This self-taught methodology, combined with extensive experience in digital audio production and professional video editing, provides me with a unique lens for analyzing AI music generation tools. The transition from spending weeks crafting a single song to watching AI generate complete tracks in under a minute offers both fascinating possibilities and concerning implications for the creative process.
Overview
Suno currently leads the AI music generation space, backed by substantial investment including a $125 million Series B funding round in May 2024, resulting in a $500 million valuation. Released in December 2023 and later integrated into Microsoft Copilot, the platform has rapidly evolved through multiple versions, with V4 (released November 19, 2024) marking significant improvements in audio clarity and dynamic song structure. However, this progress comes amid serious legal challenges from major record labels and the RIAA, who filed lawsuits in June 2024 challenging Suno's use of copyrighted material in their training data.
Technical Implementation
During my extensive testing of the platform, I discovered direct evidence of copyrighted material in their training data. After one generation session, I noticed the distinctive "STRANGE... Music..." tag associated with Tech N9ne's productions playing after a short pause following an instrumental. When reported to the Suno team, this observation received no response, becoming particularly notable when lawsuits regarding copyrighted training data were subsequently filed.
Version 4 marks a significant improvement in Suno's vocal generation capabilities. Earlier versions produced notably artificial vocals that became immediately recognizable across platforms like YouTube and Spotify. While v4's vocals show marked improvement, trained producers can still detect their artificial nature. This represents both progress and ongoing challenge in achieving truly natural-sounding vocals.
The platform's remaster feature stands out as a significant differentiator. While not performing true mastering in the traditional sense, it effectively enhances tracks by improving vocal naturality, instrument authenticity, and frequency space utilization. The feature operates more as a sophisticated enhancement tool, helping distribute musical elements more effectively within the frequency spectrum. However, it cannot fix fundamental issues in poorly generated tracks.
Core Functionality
Stem separation capabilities remain a notable limitation. Suno only offers vocal and instrumental separation, with noticeable artifacts and quality degradation. The separated files often sound processed through basic isolation software, making them largely unsuitable for professional work. This limitation is particularly notable given Suno's market position and investment backing.
File format restrictions present another challenge. While WAV format is available for premium users, most outputs utilize compressed formats like MP3 or M4A. This compression, while minimal, creates challenges for professional audio engineering and commercial applications. The issue compounds when working with stems, as compression artifacts become more pronounced during mixing and mastering.
Platform Limitations
Voice diversity represents a critical issue, with an overwhelming prevalence of Caucasian-sounding vocalists. This limitation particularly impacts genres with strong cultural roots, such as bourbon street jazz, where vocal authenticity plays a crucial role. While the platform offers some control through voice recording features, the overall lack of diversity remains a significant concern.
Real-time control during generation remains absent, a limitation shared across the industry. Users cannot preview or adjust outputs during creation, leading to a more iterative and potentially time-consuming workflow. This stands in stark contrast to traditional DAW-based production, where real-time adjustments and monitoring are standard.
All current AI music generation tools, including Suno, struggle with frequency balance. They typically favor high-end clarity at the expense of strong basslines and impactful kick drums. This tendency toward weaker lower frequencies often necessitates additional processing through traditional DAWs for professional use, particularly for genres requiring strong bass presence.
Pricing Structure and Value Proposition
Suno's pricing strategy presents an intriguing balance between accessibility and capability. The free plan offers surprisingly generous creative freedom with 50 daily credits, providing ample opportunity to explore the platform's capabilities without financial commitment. This approach effectively removes barriers to entry for curious creators and casual users.
The Pro plan, at $10 USD monthly ($8 annually), strikes an impressive balance of affordability and utility. With 2,500 monthly credits, priority generation, and commercial usage rights, it provides more than enough capacity for serious experimentation. From personal experience, I only hit the credit limit during my first month of use, when the sheer novelty of generating complete songs in under a minute led to extensive testing. The credit allowance proves more than adequate for ongoing creative work.
However, the question of value becomes more complex when considering the legal landscape. While the Premier plan ($30 monthly) offers 10,000 credits and appears attractively priced for high-volume users, current copyright controversies raise serious considerations. Given Suno's use of copyrighted material in training and ongoing legal challenges, users must weigh whether these seemingly affordable prices truly represent good value when potential copyright violations could leave them without clear ownership rights to their creations.
For users creating music solely for personal enjoyment – essentially treating Suno as a personalized Spotify radio – the pricing structure offers excellent value. However, those considering commercial use should carefully evaluate the risks. The rapidly evolving landscape of AI-generated content and increasingly complex copyright considerations might make even these modest prices questionable investments for professional applications.
Verdict
Suno maintains its market leadership through consistent quality and professional output, though several areas require attention. The v4 update demonstrates meaningful progress in vocal generation, but limitations in stem separation, frequency balance, and voice diversity highlight ongoing challenges. The platform's remaster feature shows promise, though it cannot fully replace professional audio engineering.
The pricing structure offers reasonable value, particularly for casual users and those focusing on non-commercial content. However, professional users may find the platform's technical limitations, particularly in stem separation and frequency balance, necessitate additional processing work.
While Suno earns three shots for its consistent quality and market-leading position, several factors prevent a higher rating. The platform's slow pace of innovation relative to its substantial funding, combined with persistent technical limitations, suggests room for improvement. As competitors continue to innovate, particularly in areas like stem separation and voice diversity, Suno will need to accelerate its development to maintain its market position.
For those seeking alternatives, Riffusion has emerged as a compelling option, currently offering impressive capabilities at no cost during its beta phase. You can find my detailed analysis of Riffusion's strengths and innovations in my recent review: https://www.queencaffeineai.com/post/expresso-review-riffusion
Stay tuned for our upcoming comprehensive comparison of AI music generation platforms, where we'll examine how Suno's capabilities stack up against emerging competitors like Riffusion and Udio.
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