AI is changing what it means to build efficient data systems.
Two-thirds of enterprises expect GenAI to increase stored data volumes. But only about one-third say their infrastructure is “fully optimized for GenAI.” It’s critical to design data center architectures capable of supporting AI workloads and the ever more data they require.
Every model trained, every video analyzed and every simulation refined generates a wave of data that must be stored and reused. Can architectures and business models keep up with the pace of data creation? Thanks to tiering, yes.
How data tiering optimizes AI performance and scale
Data tiering keeps information in the right place at the right time. It’s how infrastructure balances performance, scale and cost without constant manual tuning.
- Real-time access tiers handle active, read-heavy bursts, with memory and flash often supporting these.
- Frequent access tiers serve mixed activity and hard-drive storage clusters sustain them efficiently at scale.
- Mass-capacity tiers retain large datasets, so they remain available for reuse and analysis, while high-capacity hard drives anchor these for the lowest cost per terabyte and optimal performance.
Tiering isn’t about hierarchy; it’s about harmony. Each layer serves a distinct role and together they make the system faster, more efficient, scalable and sustainable.
Data created by AI-driven applications doesn’t follow a straight path — it flows through multiple tiers as it’s captured, processed, trained, refined and retrained to unlock its true value. Tiering turns that infinite flow of data into an advantage: it keeps more of your data productive instead of dormant. It ensures valuable results don’t get stranded. Teams codify data with policy placement, lifecycle tags and telemetry. The aim: keep the working set close to compute and keep the deep set affordable, durable and ready when called.
How tiered storage keeps AI training fast and efficient
GPUs pay off only when they’re fed mass data fast enough to stay busy. Modern hard-drive systems deliver millisecond-level response — quick enough to keep AI training and inference moving. Memory and flash handles the cache while hard drives deliver the depth. That’s why about 85% of cloud data still resides on hard drives1: scale depends on capacity that’s both high and affordable.
Tiering makes it possible for petabytes of outputs to avoid overwhelming high-cost infrastructure or disappearing before they can train the next model. It makes reuse practical: the same database can feed training today, fine-tuning tomorrow and governance audits in the future. Capacity planning becomes a lever, not a constraint.
Efficiency is the new performance
Tiering helps enterprises use existing infrastructure optimally. It prevents over-provisioning, trims idle power draw and extends asset life. At the system level, hard-drive-based tiers deliver strong total cost of ownership advantage.
Higher areal densities — drives built for data-hungry applications — mean more terabytes per spindle, fewer racks to power and cool, and lower energy per terabyte. The result is greater efficiency without sacrificing speed or resilience.
How tiered storage reduces energy use and supports sustainable AI
At scale, efficiency often equals sustainability. Moving infrequently accessed data to power-efficient, high-capacity tiers reduces operational emissions.
Recent system-level modeling backs this up: hard-drive racks use roughly four times less power and emit about 10 times less embodied carbon than SSD racks of equal capacity.2
Circular practices — such as refurbishing and redeploying drives or redeploying rare earth elements into the supply chain — cut embodied carbon and extend useful life.
Fewer drives, longer lifecycles and less waste: these are tangible gains that tiered storage3 make possible.
How enterprises succeed
AI’s data footprint will only keep expanding. Tiering is how enterprises stay ahead, with speed delivered where it’s needed, scale where it’s demanded and sustainability throughout.
At massive scale, hard drives remain the backbone of that balance: enabling performance, affordability and efficiency, all at once.
That’s how tiering delivers advantage at scale. It keeps the right data in the right place at the right time.
The full potential of AI requires data — and the storage that upholds it.
Footnotes
- Seagate analysis of multiple industry analyst reports.
- HotCarbon 2024, “A Call for Research on Storage Emissions.”
- FY2024: over 1.8 million drives returned to service (Seagate Circularity); FY2023: 1.19 million drives extended back to service (Seagate Planet page, “Circularity”).
