AI demand and NAND supply cycles are making multi-year storage planning harder than most teams expect.
What matters
Economics, not just performance
Performance is solvable. The risk is budget drift and forced architecture compromises over time.
What to do
Architect for volatility
Separate performance data from retention data without losing a single namespace or parallel speed.
About the researcher
Erik Salo, SVP at VDURA
Erik Salo leads products, markets, and operations at VDURA for AI data infrastructure, while publishing analysis on how storage architecture and economics affect performance, scalability, and long-term risk.
Erik’s knowledge base includes decades of experience spanning semiconductors, storage systems, and global enterprise deployments.
The hidden risk in AI infrastructure economics
AI infrastructure planning often assumes flash pricing is stable. It is not. Volatility shows up as budget drift, refresh constraints, and forced tradeoffs at scale.
THE ASSUMPTION
“We can forecast SSD cost over 3 years.”
Procurement cycles assume predictable unit economics
AI programs assume storage cost curves look like last year
NAND cycles and AI demand are making SSD pricing less predictable.
Pricing variability becomes a planning risk
Cost spikes force retention data onto premium tiers
Teams delay capacity adds or compromise performance
Model the real cost of flash over time
Use the SSD Pricing Volatility Calculator to understand how flash pricing variability impacts your environment across multiple years. Updates reflect the latest research as it evolves.
Want full methodology and assumptions? View the whitepaper →
SSD pricing volatility research
Analysis tracked SSD pricing behavior across multiple NAND cycles to quantify the economic risk introduced by flash volatility. The conclusion is clear: architectures that assume flat flash pricing expose organizations to unpredictable cost expansion over time.
AI workloads are not uniform. Training and inference demand NVMe flash. Retention, checkpoints, and long-term datasets do not. If everything is forced onto flash, pricing volatility becomes systemic risk.
Architect for volatility
Separate performance data from retention data without sacrificing a single namespace or parallel performance.
Flash-first NVMe performance where it matters most
Cost-efficient capacity tiers for retention and growth
Single global namespace across data tiers
True parallel file system architecture
Built for performance and economic resilience
VDURA was designed to decouple performance from long-term storage economics. That design choice matters more today than ever.