When data scales fast, weak architecture breaks faster.
AI is often described as a compute problem. In reality it's a data system, where data is continuously created, stored, and reused over time.
Find out why it’s the ability to retain, manage, and learn from data that defines AI system performance.
Hard drives remain the bedrock of storage for the cloud and AI—and that’s no accident.
Hard drives remain the bedrock of storage for the cloud and AI—and that’s no accident.
AI infrastructure works a lot like a traditional library. The most-accessed information lives at the front in high-performance tiers built for real-time AI inference, while the majority of data is stored deeper within larger, cost-optimized capacity layers designed for scale and retention. From historical context to training data and outputs, most AI data doesn’t disappear after creation—it compounds over time. Modern cloud infrastructure is already built this way, with roughly 80% of cloud data residing on hard drives because scale, efficiency, and economics matter. AI isn’t simplifying infrastructure architecture—it’s reinforcing the need for intelligent data tiering at massive scale.
Generating intelligence gets more attention, but AI's bigger challenge is storing, managing, and scaling the data that makes intelligence possible.
As AI moves from experimentation to production, the data it generates doesn't disappear—it compounds. Organizations building for the long term are choosing infrastructure that delivers proven reliability, cost-efficient scale, and the ability to grow with their data. The numbers tell that story clearly.
AI infrastructure isn’t powered by a single storage technology—it’s built on a tiered architecture designed to balance speed, scale, and cost at every stage of the AI lifecycle. From data ingestion and preparation to model training, inference, and long-term retention, different workloads demand different performance profiles. High-speed DRAM and SSDs accelerate active compute and real-time inference, while HDDs provide the massive, cost-efficient capacity required to store the growing volume of training data, logs, metadata, and AI-generated outputs. As AI scales, intelligent data tiering becomes essential to keeping infrastructure performant, economical, and sustainable.
Advancing HDD technology to help unlock data’s potential.
Scalable, open infrastructure for any workload.
References
