For years, all-flash became the default choice for AI storage. It simplified decisions and removed performance risk when NAND pricing was stable and supply was predictable.
That environment no longer exists.
As NAND prices rise and availability tightens, AI teams are being forced to rethink how storage is designed—not to lower standards, but to match performance to how AI workloads actually behave.
Why “All-Flash Everywhere” No Longer Scales for AI
All-flash solved real problems in traditional infrastructure. But AI workloads are not uniform—and not every stage of the AI pipeline benefits equally from flash.
Training, inference, preprocessing, checkpointing, and data retention place very different demands on storage. Treating every dataset as latency-critical increases NAND consumption without improving end-to-end outcomes across the AI workflow.
In today’s market, that approach creates unnecessary cost exposure—without guaranteeing better performance.
AI Pipelines Have Multiple Performance Profiles
Modern AI environments naturally break into tiers:
- Hot data – Active training sets, real-time inference inputs
- Warm data – Recent checkpoints, feature stores, intermediate outputs
- Cold data – Historical datasets, compliance archives, retained models
Not all of this data requires the same media—or the same performance characteristics.
The teams seeing success design storage around these differences instead of forcing a single media decision across the entire environment.
What “Hybrid” Really Means in Modern AI Architectures
Hybrid storage in AI is not about compromise.
It’s about intentional placement of performance.
- Flash where latency and throughput directly impact outcomes
- Disk where scale, durability, and sustained performance matter more
- Software defining how data moves as workloads evolve
When designed correctly, hybrid architectures preserve application-level performance while reducing unnecessary NAND exposure.
In production AI environments, DDN customers routinely achieve 90–98% GPU utilization while reducing storage CAPEX by 30–70% through workload-aligned hybrid configurations validated against real AI pipelines.
Why Flexibility Matters More Than Media Choice
In volatile NAND markets, the most valuable storage characteristic isn’t speed alone—it’s adaptability.
AI workloads change. Models evolve. Data grows. Performance requirements shift.
Architectures that allow teams to:
- Adjust tiers over time
- Rebalance workloads without re-architecting
- Avoid lock-in to a single media decision
The Takeaway
NAND volatility doesn’t require AI teams to lower performance expectations.
It requires them to design AI storage architectures that reflect how workloads actually run—today and over time.
Learn more and request an AI Storage Architecture & ROI Assessment to see how different configurations perform against your workloads—and where you can save without compromising performance.
