92 Amid the buzz surrounding AI, big data, and cloud innovation, a crucial question remains unanswered: Is your storage scaling in all the dimensions your workloads demand? AI is just the latest disruption in a long history of technological evolutions. Over the past 15 years, cloud computing, IoT, e-commerce, and rich-media streaming have forced businesses to rethink their storage strategies — from handling massive media libraries to supporting ultra-fast analytics. Now, AI-driven workloads — massive data lakes, model training, and high-frequency transactions — are exposing the limitations of legacy storage architectures. Built for a pre-AI world, traditional storage systems struggle to keep pace with the multidimensional demands of modern workloads, including: AI data lakes aggregating petabytes from multiple sources ML applications managing hundreds of millions of S3 objects High-performance AI requiring ultra-fast or high-throughput data access Cloud apps handling millions of user-mapped S3 buckets Security monitoring generating and accessing petabytes of logs IoT apps storing hundreds of thousands of real-time events per second Cloud apps processing millions of authentication requests per hour Media apps enabling simultaneous access to terabyte-scale videos The ability to scale storage in all these dimensions isn’t just about adding capacity or boosting performance — it requires a fundamentally different approach. The era of multidimensional scaling is here Some storage vendors recognized this shift over 15 years ago, pioneering disaggregated architectures that have since proven their ability to scale seamlessly across diverse workloads. Disaggregated storage — where storage and compute resources are decoupled and can scale independently — has emerged as the only viable foundation for the varied and unpredictable demands of AI-driven environments. Newcomers are scrambling to enter the space, while legacy vendors that once clung to monolithic or scale-up architectures are now pivoting to disaggregated designs. But as the old adage in storage goes: ‘It takes seven years to get it right.’ And some are just getting started. The industry is full of hard-learned lessons, and history has shown that first-generation products often struggle to deliver on their early promises. Betting your AI strategy on unproven 1.0 solutions could mean running into scalability roadblocks just as your workloads hit full stride. We’ve entered a new era — one where “what got you here won’t get you there.” Storage must evolve beyond simple capacity and performance expansion. Multidimensional scaling, powered by disaggregated architecture, ensures that businesses can meet today’s AI demands without hitting storage bottlenecks tomorrow. The 10 most important dimensions of scale to consider for AI storage Most modern storage systems excel at scaling across a few key dimensions — mainly capacity and performance — but when pushed beyond the limits of their pre-AI era design, their rigid structure forces operational tradeoffs. Multidimensional scaling in enterprise storage makes the difference between staying ahead or falling behind. A storage system built to handle the demands of current and future workloads must excel at scaling across all of the following dimensions, providing seamless operations even under extreme demands. Applications – Supporting multiple workloads within the same infrastructure Capacity – Expanding total storage volume as data demands grow Storage compute – Allocating computing power to match workload intensity Metadata – Scaling metadata operations to maintain searchability and efficiency S3 objects – Managing ever-growing object counts without performance degradation S3 buckets – Allowing for an increasing number of storage containers S3 authentications per second – Scaling security processes to match global access needs Throughput – Ensuring fast, uninterrupted data movement Objects per second – Supporting high transaction rates for real-time applications Systems management – Allowing IT teams to manage complex environments with ease The ability to scale across these dimensions — independently — ensures that enterprises can meet evolving demands without compromising performance, security, or operational efficiency. How does each dimension empower AI, big data, and cloud workloads? #1 Scaling applicationsMeets the need for: Addressing a range of requirements from single workloads to many Future-ready storage must support a huge and ever-expanding number of applications, each with unique performance and capacity requirements. For example: Read More Most of today’s large enterprise, government, and IT service provider environments require data access from multiple apps. Private and public clouds are multi-tenant by definition, with multiple apps and use cases that share a common storage infrastructure. Business-critical AI and analytics data lakes require apps for everything from data ingestion and preprocessing to visualization and reporting. Many storage systems struggle to support such large numbers of concurrent workloads, but multidimensional scaling gives you the power and flexibility needed to support the full spectrum of modern applications — all on a single system. #2 Scaling capacityMeets the need for: Storing and protecting increasing data volumes to grow with your business Scaling capacity is a given. It’s table stakes and is the foundational dimension of any storage system. Today’s modern storage must: Read More Effortlessly scale into the exabyte range Allow incremental expansion across disks, servers, racks, or data centers while maintaining 100% online availability (zero service disruption) Provide strong protection against cascading failures or multiple simultaneous hardware outages by enabling synchronous, geo-stretched deployment #3 Scaling storage computeMeets the need for: Increasing resources to deal with the demands of higher data volumes Storage demands aren’t always about adding more space. In many scenarios, workloads require increased compute performance without the need for additional storage capacity.For example: Read More E-commerce platforms, facing seasonal spikes in user activity, require more compute power to handle traffic without needing more disk space. Many traditional storage systems can’t scale performance resources without the addition of new storage servers — an inefficient and costly limitation. In contrast, systems with a disaggregated architecture allow for independent scaling of compute and capacity. This allows your business to scale exactly what it needs — whether it’s increasing S3 services to handle more API requests or boosting metadata services for higher ops/second — without the added expense of unnecessary capacity. As further explained in this Tech Target article: The future of storage is disaggregated, however you cut it, “Running AI workloads such as large language model training and inference at scale will require the ability to process a vast amount of unstructured data at unprecedented speeds. Doing this using historic storage approaches is challenging; compute and storage resources within a storage architecture are typically tightly coupled, which can make scaling both incredibly difficult and incredibly inefficient if resource requirements grow at different rates.” #4 Scaling metadataMeets the need for: Efficiently managing/accessing growing metadata volumes and accommodating data augmentation for AI applications Metadata is the master catalog that powers every storage system, enabling the efficient organization, access, and management of data. As storage requirements reach hundreds of petabytes and beyond, the ability to quickly process and scale metadata is critical for keeping systems performant. Read More Many traditional storage systems struggle in this area, relying on fixed-size metadata databases or embedding metadata with the data itself. These approaches create bottlenecks, limit scalability, and often lead to costly, time-consuming migrations as data demands grow. To efficiently scale in metadata handling, a storage solution should: Store metadata in a distributed, scalable architecture to eliminate rigid size limitations Enable seamless growth by allowing additional resources (disks, servers, or clusters) to be added as needed Deliver consistent performance, ensuring metadata services always keep pace with growing workloads without disruption. To keep performance high, the architecture should keep the metadata in a separate database. #5 Scaling objects per secondMeets the need for: Simplifying applications that manage large numbers of objects Modern workloads — think AI model training, e-commerce platforms, and IoT — demand not just immense storage capacity but also the ability to process billions of small transactions at lightning speed. This capability, often measured in objects per second, determines whether your storage can keep pace with workloads where latency isn’t an option. Read More Traditional storage systems can become overwhelmed by transaction-heavy workloads, leading to latency spikes and performance degradation that ripple across applications. Scaling in transactions means ensuring storage systems can: Handle millions to billions of requests per second with minimal latency Optimize for high-frequency read and write operations without bottlenecks Adapt dynamically to workload changes, scaling transaction capacity as demand grows #6 Scaling S3 bucketsMeets the need for: Ensuring data privacy through strong security protocols In large multi-tenant environments, storage often means provisioning new S3 buckets for each user, workload, or application. This provides essential controls — securing data, setting lifecycle policies, and managing access — but it also introduces a major scaling challenge. Read More For use cases like backup-as-a-service or storage-as-a-service, bucket counts can rapidly climb into the millions. Many storage systems struggle under this load, hitting hard limits or suffering from severe performance degradation. Storage that’s built for multidimensional scale eliminates these constraints by: Supporting millions of buckets without sacrificing performance Maintaining low-latency operations even as bucket counts grow Enabling seamless scaling for multi-tenant, high-demand environments #7 Scaling authentication transactionsMeets the need for: Separating data by workload in large multi-tenant environments Every time a user accesses cloud storage, an authentication request is triggered. In large-scale environments, these requests pile up fast: Read More A public cloud serving millions of users demands millions of requests per second. A private cloud with just 1,000 active users uploading documents can generate hundreds of thousands of S3 authentication requests per second. Every request also requires checking user and bucket-level security policies — adding even more strain on storage systems. Many traditional storage architectures simply aren’t built to handle this flood of requests without bottlenecks. Systems designed for multidimensional scale solve this by: Independently scaling authentication services to keep up with demand Distributing the authentication workload across dedicated resources Ensuring seamless access control enforcement without slowing down operations #8 Scaling throughputMeets the need for: Quickly processing large object data (e.g. media applications) Throughput is the lifeblood of demanding workloads like video streaming, analytics, and AI pipelines. It determines how much data your system can move, process, and serve at any given moment. And when data volumes soar, systems without scalable throughput will buckle under the pressure, leading to disruptions and missed opportunities. Read More The challenge? Traditional storage systems often hit hard limits as throughput demand grows, forcing costly upgrades or re-architecting. To thrive in a world of exponential data, your storage must ensure: Consistent, high-speed performance, even when multiple workloads compete for resources. The ability to accommodate exponential increases in data flow without service interruptions. Flexibility to expand throughput as workloads evolve. #9 Scaling object transactionsMeets the need for: Quickly processing small object data (e.g. AI training and fine-tuning) Some workloads aren’t about massive files — they’re about handling millions (or billions) of small objects at lightning speed. Read More AI training & fine-tuning require rapid access to huge volumes of small datasets. IoT sensor data streams in constantly, generating vast amounts of tiny files. Log files & microservices transactions flood storage with high-frequency write-and-read requests. Many storage systems slow to a crawl under these conditions. To keep up, systems designed for multidimensional scale: Optimize metadata services with flash storage and in-memory caching for near-instantaneous access. Ensure storage policies (erasure coding, replication) adapt dynamically based on object size. Deliver extremely high transaction rates without sacrificing performance. #10 Scaling systems managementMeets the need for: Efficiently managing large-scale systems or deployments with fewer people As infrastructures scale, so do the challenges of keeping them running smoothly: Read More System health & performance must be monitored with real-time KPIs. Activity logs & metrics generate terabytes — sometimes petabytes — of data. Cloud-scale operations require automation to stay efficient. Traditional systems struggle to process and store this flood of operational data. Storage that can efficiently scale systems management ensures: Seamless monitoring of system health across vast deployments. Granular logging of user/admin activity without overwhelming storage. Efficient, automated operations — so teams can manage more with less. Download the whitepaper to discover how the pioneer of disaggregated, software-defined storage delivers on the promise of multidimensional scaling. Get Whitepaper The future of storage is multidimensional: Is your system equipped to scale? Storage is no longer just an operational necessity — it’s a catalyst for growth. As AI, big data, cloud computing, and data-intensive workloads accelerate, your infrastructure must keep pace. The bottom line: Organizations that embrace multidimensional scaling today will be the ones best positioned to thrive in the future. The ability to scale in all critical dimensions transforms storage from a reactive component into a strategic advantage, eliminating bottlenecks, reducing downtime, and enabling innovation. How many dimensions can your storage system scale? If you don’t know the answer, now is the time to find out. Explore how Scality’s patented multiscale architecture enables 10 dimensions of scaling —and what that means for your data. Curious how your current storage stacks up? Let’s assess your environment—or better yet, see Scality in action with a live demo. Additional resources Explore: Scality RING object storage with Multidimensional ScaleTechTarget: Why object storage for AI makes sense
