Every time you type a short prompt into an enterprise AI tool, a series of invisible mechanisms and processes operate within your infrastructure behind the flashing indicators that appear on your screen. The tool attaches enterprise policies, session history, retrieved documents, and all other contextual data required to produce useful and reliable output, so a few dozen words entered systematically becomes a 40,000-token workload.
Now consider that thousands of users and agents access the system at once, and each prompt can reach an enterprise knowledge base that can reach 100 petabytes (PB) in the world’s largest enterprises. Serving from that archive generates a second active store, the key/value (KV) cache. This cache grows with the number of users querying it simultaneously, not with the amount of data you have.
Expensive computations on these tokens become a cache worth storing and reusing to prevent redundancies and inefficiencies that bottleneck the output. However, this tool can only do that if your technical infrastructure has a place to store its cache. And many companies don’t consider this storage need when building their AI infrastructure until they run out of space.
For enterprise AI tools to scale reliably, the foundation designed to generate AI output must have the functionality and capacity to store the complex operational computations required to create the AI output.
And at this scale, traditional options aren’t enough. Fast but limited capacity dynamic random access memory (DRAM) is too expensive to hold this data, and hard disk drives (HDD) are too slow to process it. Enterprise AI processing at the fleet level relies on high-capacity solid-state drives (SSDs). SSDs offer the capacity and processing speed to hold AI, along with the energy efficiency and footprint that allows you to achieve a return on your AI investment.
Impact of AI inference
As companies increasingly apply AI to their growth strategies, much of their focus remains on training larger, more powerful models and investing in powerful graphics processing units (GPUs). But the big challenge now is inference. That is, the process of delivering 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 entire bundle is fed into the AI system, where expensive GPUs do the calculations. These calculations (KV cache) become reusable assets so the system does not have to recalculate them over and over again. The KV cache represents “state” within an AI system, and as AI deployments mature, managing that state becomes critical to performance.
Storage challenges are further exacerbated as enterprises rely more on search augmented generation (RAG), agent workflows, and long-context inference than on internal knowledge bases. Each of these increases the amount of information that the system must store and access at one time. And because the AI needs to retrieve much of that information before it can respond, storage speed, not just capacity, determines how fast the system feels to the user using it. This is the time it takes for the user to see the first response, known as TTFT (Time to First Token).
Organizational leaders often assume that more GPU capacity will lead to faster AI, but in reality, GPUs and other accelerators often sit idle while AI systems retrieve documents, read context, restore cached computations, or wait for data movement or storage bottlenecks.
Mathematics scales up quickly. One long context request may require 312 GB of KV cache. Multiply this by 8 concurrent users and the requirement jumps to 2.5 terabytes (TB). Add agent workflows and that number grows to 10 TB, all of which must be stored, accessed, and managed with low latency.
Workloads that initially seemed manageable with per-session memory requirements become significant challenges when multiple users interact with the AI simultaneously. These stored calculations can be one of the biggest consumers of infrastructure resources.
That is the “hidden storage tax.” This problem only becomes apparent once AI systems are operational at scale. Tasks that seemed doable in the pilot actually become unsustainable when the number of users or AI sessions running simultaneously increases exponentially, known as concurrency. The result is slow response times, unexpected bottlenecks, underutilized infrastructure, and increased operational costs.
Why storage matters
Traditionally, organizations have treated storage as a passive repository of data, a place where stored data is held. This approach worked when we used storage primarily for backup systems, archives, and databases. However, in an AI environment, SSD storage is an active part of the application and is critical to responsiveness, scalability, user experience, and cost efficiency. Despite this change, companies that continue to use traditional benchmarking metrics are at risk of unscalable AI investments and failed AI pilots.
Although these changes are still emerging in reasoning, the underlying principles are already visible wherever AI is implemented at scale. That means the storage architecture, not just the compute, determines whether the system will work.
Software-defined storage provider PEAK:AIO is working with healthcare providers to use AI to analyze magnetic resonance imaging (MRI) scans to identify signs of cancer. These institutions generate vast amounts of image data, but many lack the necessary infrastructure to efficiently store, access, and analyze this data. PEAK:AIO offers high-capacity SSDs to help customers store and process large data sets within their systems and networks.
For its containerized modular data centers, DUG Technology, a provider of high performance computing and AI infrastructure solutions, uses SSDs to enable 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 locations.
Zero-day decision making with AI
The right storage architecture improves responsiveness, infrastructure efficiency, and scalability for long-context inference, RAG, and agent AI workflows.
As companies scale their AI efforts, it’s becoming increasingly important for AI architects, procurement and finance leaders, platform engineers, and other decision makers to build a technology foundation with enough high-capacity SSD storage to handle operations and prevent bottlenecks now and in the years to come.
This means storage needs to be part of the design consideration from the beginning, allowing businesses to avoid investing in retrofitting their infrastructure later.
Read Solidigm’s Anatomy of Prompts articles and technical documentation to learn how long-context AI, RAGs, and agent workflows turn prompt design into infrastructure decisions and how enterprises can evaluate storage before they run into latency, cost, and utilization issues in production.
