Universal Music Group (UMG) and ElevenLabs have signed a multi-year licensing and product development agreement to build an AI-powered music creation platform that lets fans co-create tracks using licensed artist content. The deal - ElevenLabs' first with a major label - ties revenue directly to authorized material rather than the unrestricted training that has defined much of the AI music debate so far.
What the partnership covers
The agreement spans two areas: licensing UMG's catalog for use in ElevenLabs products, and jointly developing new tools for artists, songwriters, and fans. The first output will be a standalone platform separate from ElevenLabs' existing music products, including its Music API and ElevenMusic application. It will offer remixing, mashups, reinterpretations of existing tracks, and personalized vocal experiences built around participation from artists and songwriters.
Both companies said additional products and fan experiences will follow over the coming months and years. ElevenLabs brings its AI voice and audio technology, which operates across more than 70 languages and is used by roughly two-thirds of Fortune 500 companies. UMG contributes its rights management expertise and global artist community.
The licensing-first approach
The structure matters because it anchors the platform in authorized content from the start. Rather than training on publicly available music and negotiating later, ElevenLabs and UMG are building around licensed material where artists and songwriters can opt in and share in the economics. This contrasts with approaches that have drawn legal challenges from rights holders over the past two years.
"The most exciting possibilities for AI and music are those that put artists, songwriters, and fans at the center," said Sir Lucian Grainge, Chairman and CEO of Universal Music Group. "UMG and ElevenLabs are aligned that responsible AI can inspire discovery, deepen engagement between artists and fans, and unlock new revenue opportunities for the creative community."
Product development signals
For product teams, the deal offers a concrete model of how AI licensing agreements can shape product roadmaps. The platform is being built as a contained environment - not an open-ended generation tool - which constrains the product scope to specific, licensable interactions. That design choice reduces legal risk and creates clear attribution paths for compensation.
Mati Staniszewski, Co-Founder and CEO of ElevenLabs, framed the collaboration around compensation and experience: "By combining UMG's global community and rights management expertise, with our AI models and products, we'll enable artists and songwriters to create powerful new experiences for their fans, and ensure they are fairly compensated."
ElevenLabs' existing music tools already give developers API access to studio-grade music generation and let users edit original songs with control over vocals, instrumentation, style, and arrangement. The new platform will sit alongside those products but target a different use case: fan co-creation tied directly to specific artists' catalogs. Product managers working on AI for Product Development in media or entertainment will recognize the licensing-plus-tooling pattern - it's a repeatable framework for markets where IP ownership is the primary constraint on feature scope.
Why this matters for product development professionals
The UMG-ElevenLabs deal is a working example of how to structure an AI product when the core training data is copyrighted and high-value. The takeaway for product leaders is not about music specifically - it's about the licensing architecture. The agreement separates authorized content from unrestricted generation, builds compensation into the product layer, and gates features behind artist participation. If your product roadmap depends on third-party IP, this is the template that legal and business affairs teams will point to. Understanding it now, before your own licensing negotiation starts, puts you ahead of the curve. For teams navigating similar build-or-license decisions, the AI Learning Path for Product Managers covers frameworks for evaluating AI partnerships and integrating licensed models into product strategy.
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