110 As enterprises scale up their AI initiatives, a new kind of bottleneck is emerging — not in compute, but in data. Generative AI has transformed enterprise IT priorities virtually overnight, as organizations are now racing to build robust AI pipelines and data lakes capable of handling unprecedented volumes and varieties of data. But many are discovering that traditional storage architectures simply weren’t designed for these new demands. The AI data pipeline spans from data preparation stages (aggregation, curation and processing in AI data lakes), to AI model stages (training, fine-tuning, inference). A simplified view is provided here. AI applications — from training large language models to powering business analytics — require storage solutions that can simultaneously handle massive capacity, high transaction rates, and rapid metadata operations while maintaining performance across the entire data pipeline. More challenging still, the rapidly evolving nature of AI means organizations face significant uncertainty about their future storage needs. This unpredictability is precisely the challenge that Scality has been solving for over a decade. Future-proofing storage: The power of true disaggregated architecture While competitors are just beginning to recognize the need for disaggregated storage to address AI workloads, Scality has been ahead of the curve. Over a decade ago, we pioneered this approach with RING’s patented MultiScale architecture — originally developed to solve the same scale, flexibility, and performance challenges in cloud environments that AI now demands. So, what is disaggregated storage? Traditional storage architectures bundle metadata, compute, security, and management into a tightly coupled stack — forcing all components to scale together, regardless of actual need. The result? Costly, inefficient growth. Disaggregated storage breaks that rigid, traditional model by decoupling key services, allowing each to scale independently based on demand. For example: Metadata services can scale independently to handle rising object counts and operations per second Storage-compute services can scale to serve increasing numbers of applications and users Security services can scale to meet evolving authentication and encryption demands Systems management services can scale to handle increased monitoring, logging, and orchestration across large environments This level of flexibility is essential in unpredictable, multi-tenant AI and cloud workloads — where overprovisioning wastes resources, and underprovisioning introduces risk. While some vendors have only recently announced solutions that disaggregate across two dimensions (typically metadata and data), Scality RING has operated as a fully disaggregated platform for over a decade, with the ability to scale independently across ten distinct dimensions. This isn’t legacy technology being retrofitted for AI — it’s a proven, forward-looking architecture built to adapt. RING’s MultiScale design delivers the ultimate in distributed storage flexibility, with modular services that work in sync yet grow independently to support workloads that evolve in diverse and often unexpected ways. From cloud to AI: Why RING’s architecture is ideally suited for both The parallels between cloud storage and AI data pipeline requirements are striking. Both environments involve multiple applications accessing shared storage, which introduces a common set of challenges—particularly around unpredictability: Fluctuating numbers of concurrent users and applications Nonlinear data growth and unpredictable volume patterns Complex, aggregated workloads across tenants or services Sudden spikes in security and compliance processing These shared characteristics demand storage solutions that can adapt dynamically, scale efficiently, and maintain performance under pressure. Scality didn’t specifically design RING for AI workloads — they weren’t even on the horizon when the architecture was first conceived. Yet the solution we built to handle the extreme flexibility demands of cloud-scale storage has proven remarkably well-suited for today’s AI data pipelines. This isn’t a coincidence. It’s the direct result of RING’s core design philosophy: build storage that can adapt to any future requirement, even those we can’t yet imagine. When we talk about “multidimensional scale,” we’re describing the ability to independently scale any aspect of the storage system to meet new demands without overhauling your entire infrastructure. For most storage systems, the combination of unpredictable capacity needs, performance requirements, and access patterns represents an “I/O blender” nightmare. But for RING, with more than a decade of experience handling similar challenges in cloud environments, it’s exactly the scenario the system was built to address. How RING removes AI data pipeline roadblocks The advantage of experience in an emerging field: As organizations build out their AI infrastructure, they face a critical choice: adopt storage technologies hastily modified to accommodate AI workloads, or implement solutions whose fundamental architecture has already proven capable of handling similar challenges at scale. Scality RING stands alone in offering both cutting-edge capabilities and battle-tested reliability. While competitors scramble to develop disaggregated architectures for AI, Scality customers benefit from technology that has been refined through more than a decade of real-world deployment in some of the most demanding storage environments. An unpredictable AI future demands multidimensional scale The AI revolution has fundamentally changed how organizations think about data storage. Yet the core challenges — unpredictable scaling needs, diverse performance requirements, and the need for future-ready flexibility — are precisely what Scality RING was designed to solve from its inception. RING’s pioneering MultiScale architecture delivers what modern AI pipelines demand: the ability to independently scale any dimension of storage infrastructure as requirements evolve. This multidimensional scaling approach, proven over a decade of enterprise deployments, offers unique advantages for organizations building AI infrastructure: Freedom from arbitrary technical limitations that force costly migrations Confidence that storage can adapt to emerging AI techniques and requirements The ability to optimize resources by scaling exactly what’s needed, when it’s needed Enterprise-grade reliability backed by years of production experience in demanding environments In a landscape where AI requirements continue to shift rapidly, Scality RING transforms uncertainty into opportunity. Rather than limiting what’s possible with your data, RING’s architecture ensures your storage infrastructure can evolve in lockstep with your AI ambitions — without limitations, bottlenecks, or costly rip-and-replace cycles. The storage solution that pioneered cloud-scale flexibility a decade ago is now proving to be the ideal foundation for the AI workloads of today and tomorrow. With Scality RING, you’re not just solving today’s AI storage challenges — you’re future-proofing your infrastructure for whatever comes next.
