Every time you enter a short prompt into your enterprise AI tool, behind that pulsing indicator you see on the screen, a flurry of invisible mechanisms and processes goes to work in the infrastructure. As the tool attaches the enterprise policies, session history, retrieved documents, and all the other contextual data it needs to generate a useful and reliable output, the dozen words you’ve typed systematically become a 40,000-token workload.
Now consider that thousands of users and agents may be hitting the system at once, each prompt reaching into a corporate knowledge base that, at the world’s largest companies, can run to 100 petabytes (PB). Serving from that archive generates a second active store, the key-value (KV) cache, that scales not with how much data you have but with how many users are querying it at the same time.
The expensive computation over those tokens becomes a cache worth saving and reusing to prevent redundancies and inefficiencies that bottleneck output. But the tool can only do that if the technical infrastructure has somewhere to keep that cache. And many enterprises don’t consider the need for this storage when they’re building AI infrastructure—until they run out of room.
To ensure enterprise AI tools can scale, the foundations designed to generate AI outputs must have the capability and the capacity to store the calculations of the complex operations that go into creating them.
And traditional options fall short at this scale: Fast but capacity-limited dynamic random access memory (DRAM) is too expensive to hold this data, and hard disk drives (HDDs) are too slow to serve it. Processing enterprise AI at the fleet level depends on high-capacity solid state drives (SSDs), which deliver the capacity to hold it and the speed to serve it—with the energy efficiency and footprint that make returns on AI investment achievable.
The Impact of AI Inference
As enterprises increasingly apply AI to their growth strategy, much of their focus remains on training larger and more capable models and investing in powerful graphics processing units (GPUs). But the bigger challenge now is inference: the process of serving AI responses accurately, reliably, and quickly at scale.
In modern AI systems, every prompt creates a bundle consisting of policy instructions, session history, retrieved documents, tool outputs, and other contextual components. This whole bundle is fed into an AI system where the expensive GPUs make computations. These computations—the KV cache—become reusable assets so the system doesn’t have to recalculate them over and over again. The KV cache represents a “state” within the AI system, and as AI deployments mature, managing that state becomes critical to performance.
The storage challenge only compounds as enterprises lean on retrieval-augmented generation (RAG), agentic workflows, and long-context reasoning over internal knowledge bases. Each of these increases the volume of information the system must store and access at once. And because much of that information has to be retrieved before the AI can respond, storage speed, not just capacity, shapes how fast the system feels to the people using it: the lag before a user sees a first response, known as time to first token (TTFT).
Although organization leaders often assume more GPU capacity powers faster AI, in reality, GPUs and other accelerators frequently sit idly while AI systems retrieve documents, load context, restore cached computations, or wait on data movement and storage bottlenecks.
The math scales quickly. A single long-context request can require 312 gigabytes of KV cache. Multiply that across eight concurrent users and the requirement jumps to 2.5 terabytes (TB). Add agentic workflows and the figure balloons to 10 TB—all of it needing to be stored, accessed, and managed with low latency.
A workload that may have initially appeared to be a manageable per-session memory requirement becomes a massive challenge when multiple users interact with AI simultaneously. Those saved calculations become one of the largest consumers of infrastructure resources.
That’s the “hidden storage tax”: issues that only become obvious when AI systems are put into production at scale. Even a task that appeared workable in pilots becomes unsustainable in practice as the number of users or AI sessions running simultaneously increases exponentially, known as concurrency. The result: slower responses, unforeseen bottlenecks, underused infrastructure, and higher operating costs.
Why Storage Is Critical
Historically, organizations have treated storage as a passive repository for their data—a holding place for data at rest. That approach worked when they were using storage primarily for backup systems, archives, and databases. But in AI environments, SSD storage is an active part of applications, critical to responsiveness, scalability, user experience, and cost efficiency. Enterprises that continue to use traditional benchmark metrics despite this shift are risking AI investments that can’t scale and failure of AI pilots.
These shifts are still emerging in inference, but the underlying principle is already visible wherever AI runs at scale: Storage architecture, not just compute, decides whether the system delivers.
PEAK:AIO, a software-defined storage provider, works with medical institutions using AI to analyze magnetic resonance imaging (MRI) scans to identify signs of cancer. These institutions generate enormous volumes of imaging data, but many lack the infrastructure they need to store, access, and analyze this data efficiently. PEAK:AIO offers its customers high-capacity SSDs so they can store and process large data sets within their own systems and networks.
For its containerized modular data centers, DUG Technology, a provider of high-performance computing and AI infrastructure solutions, uses SSDs to allow its customers to run AI systems in locations where the ability to deploy storage infrastructure is limited, such as industrial sites, energy facilities, and other remote areas.
A Day-Zero AI Decision
The right storage architecture can improve responsiveness, infrastructure efficiency, and scalability for long-context inference, RAG, and agentic AI workflows.
As enterprises expand AI initiatives, it’s becoming increasingly critical for AI architects—as well as leaders in procurement and finance, platform engineers, and other decision makers—to build technical foundations with sufficient high-capacity SSD storage to handle their operations and prevent bottlenecks today and in the years ahead.
And that means storage needs to be a part of the design conversation from the start—so their enterprises can avoid needing to invest in retrofitting their infrastructure later.
