SSD Pricing Volatility and the Case for Mixed Fleet Storage

Executive Summary

The enterprise storage market is experiencing unprecedented SSD price volatility driven by explosive AI demand and multi-year capacity commitments from hyperscalers. Between Q2 2025 and Q1 2026, 30TB TLC SSD pricing increased 257%—from $3,062 to $10,950—while HDD pricing remained relatively stable with a 35% increase. This divergence creates challenges for all-flash storage architectures while highlighting the value of mixed fleet storage systems that can adapt to market conditions.

This technical bulletin examines the impact of SSD pricing volatility on storage system costs, showing how VDURA's mixed fleet architecture provides flexibility to optimize storage configurations based on current market conditions without sacrificing performance.

1. SSD Market Volatility Analysis

1.1 Historical Price Trends

The NAND flash market has historically exhibited cyclical pricing patterns driven by supply-demand imbalances, manufacturing capacity constraints, and technology transitions. However, the current price spike represents an unusually steep and sustained increase driven by two unprecedented factors:

AI infrastructure buildout: The rapid expansion of AI training and inference infrastructure has created extraordinary demand for high-capacity, high-performance storage. Major AI labs and cloud providers are deploying exabyte-scale storage systems to support large language models, computer vision, and other AI workloads.

Hyperscaler capacity commitments: Large cloud providers have entered into multi-year purchase agreements for flash capacity, effectively pre-booking significant portions of global SSD production. These commitments—often spanning 2-3 years—reduce available supply for enterprise customers and create sustained upward pressure on spot market pricing.

The combination of these factors has disrupted traditional pricing cycles, resulting in price increases that persist longer than historical patterns would suggest.

1.2 Cost Impact on All-Flash Architectures

All-flash storage systems from competitors experience linear cost scaling with SSD price increases. A storage system requiring 3 PB of raw capacity faces dramatically different costs depending on deployment timing:

• Q2 2025 deployment: 100 × 30TB SSDs = $306,200 in SSD costs alone
• Q1 2026 deployment: 100 × 30TB SSDs = $1,095,000 in SSD costs alone
• Cost increase: $788,800 (+257%) for identical capacity

The impact extends beyond SSDs. AI-driven demand has also affected other critical components: DRAM pricing increased 205% over the same period as hyperscalers procure memory-intensive GPU servers, and high-speed networking components face similar supply constraints. This creates compounding cost pressure across the entire storage infrastructure stack.

This volatility makes budgeting and long-term planning challenging for enterprises committed to all-flash architectures. Capital expenditure forecasts can become obsolete within a single quarter, particularly when competing for supply with hyperscalers making multi-year commitments.

1.3 Long-Term Market Outlook: A Structural Shift, Not a Temporary Spike

Unlike previous NAND flash pricing cycles that corrected within 12-18 months, industry analysis indicates this shortage represents a fundamental, long-term reallocation of silicon manufacturing capacity. Multiple factors point to sustained tight supply conditions extending well beyond 2027:

Structural Supply-Demand Imbalance: Market forecasts project NAND demand growing 20-22% year-over-year in 2026, while supply is expected to increase only 15-17%. This widening gap creates inevitable upward pricing pressure. By 2026, AI applications alone are projected to consume one in five NAND bits produced globally, representing 34% of total market value despite being just 20% of volume—reflecting the premium pricing power AI infrastructure commands.

Strategic Capacity Reallocation: NAND manufacturers are deliberately shifting production away from consumer products toward higher-margin AI and enterprise memory. This isn't a temporary shortage that will self-correct through market forces—it's a strategic business decision by manufacturers to prioritize customers paying premium prices for guaranteed multi-year supply commitments. Enterprise customers purchasing on the spot market face both higher prices and reduced allocation priority.

Manufacturing Timeline Reality: Even if manufacturers announced new fab construction today, the 24-36 month timeline for building and qualifying new NAND production lines means meaningful capacity additions won't arrive until 2027-2028. Industry analysts warn that NAND shortages could persist through the end of the decade, with some projections suggesting supply constraints lasting up to ten years as AI infrastructure deployment accelerates.

For storage procurement teams, this represents a fundamental shift in planning assumptions. The historical approach of "waiting out" NAND price spikes is no longer viable. Organizations building all-flash infrastructure today must plan for sustained high pricing and limited supply availability—making architectural choices that reduce SSD dependency increasingly strategic.

2. The Mixed Fleet Architecture Advantage

2.1 Tunable Cost/Performance Profile

VDURA's mixed fleet architecture decouples performance from capacity by using SSDs for the hot working set and HDDs for the capacity tier. This design provides flexibility: the SSD percentage can be tuned based on workload requirements and current market conditions.

Consider a large-scale deployment: 25 PB storage delivering 1,000 GB/s read performance with 20% SSD. The cost advantage of mixed fleet architecture was already meaningful in Q2 2025, but it has grown substantially as SSD prices increased:

Q2 2025: Low SSD Pricing

Cost Advantage: 2.9x (VDURA $5.55M less expensive)

Q1 2026: High SSD Pricing

Cost Advantage: 3.7x (VDURA $17.98M less expensive)

The key observation: VDURA's cost advantage grows as SSD prices increase. In Q2 2025, VDURA was 2.9x more cost-effective than AFA competitor. By Q1 2026, that advantage expanded to 3.7x—a 28% improvement in relative value. AFA competitor's all-flash cost increased 189% while VDURA's mixed fleet cost increased 123%.

2.2 Dynamic Response to Market Conditions

The true strategic value of mixed fleet architecture emerges when analyzing cost sensitivity to SSD pricing across different market scenarios. Consider the same 25 PB storage system delivering 1,000 GB/s read performance examined in Section 2.1 (16 VPODs + 7 JBODs configuration at 20% SSD). The table below shows how this system's annual cost changes with different SSD percentages when comparing low (Q2'25) vs high (Q1'26) SSD pricing:

For this specific 25 PB system, mixed fleet configurations with lower SSD percentages show substantially reduced sensitivity to SSD price volatility. VDURA's 20% SSD configuration experiences a 123% cost increase compared to AFA competitor's 189% increase—a difference of 66 percentage points. At Q1'26 pricing, VDURA saves $17.98M annually versus AFA competitor Nitro ($6.56M vs $24.54M).

2.3 Efficiency Advantage: Performance Per Node

Beyond the mixed fleet architecture advantage, VDURA delivers superior performance density—more throughput per server. This architectural efficiency provides additional insulation from component price volatility, particularly for expensive resources like DRAM which increased 205% from Q2 2025 ($5.67/GB) to Q2 2026 ($17.31/GB).

To deliver 650 GB/s performance, VDURA requires 10 VPOD servers while competitors require 17 servers—a 41% reduction in node count. This efficiency translates directly to lower component costs:

• Q2 2026 DRAM cost: VDURA $86,550 vs AFA competitor $226,040 (62% savings)
• Server infrastructure: Fewer CPUs, NICs, power supplies, and rack space
• Operational costs: Lower power consumption, cooling requirements, and management overhead
• Volatility insulation: When component prices spike, fewer nodes means proportionally lower impact

This architectural efficiency compounds with the mixed fleet advantage. VDURA uses less expensive HDD capacity and achieves performance targets with fewer servers, reducing exposure to volatile pricing across all components—SSDs, DRAM, CPUs, and networking.

3. Architectural Flexibility in Practice

3.1 Workload-Optimized Configurations

Different workloads benefit from different SSD/HDD ratios. VDURA's architecture allows enterprises to optimize each deployment:

• AI/ML Training (20-30% SSD): Hot checkpoints on SSD, archived checkpoints on HDD
• Media and Entertainment (30-40% SSD): Active projects on SSD, archive content on HDD
• High-Performance Computing (40-60% SSD): Simulation results on SSD, long-term datasets on HDD
• All-Flash Mode (100% SSD): Available when workload demands or budget permits

3.2 Future-Proof Investment Strategy

SSD pricing is inherently unpredictable, particularly in an environment dominated by AI infrastructure buildouts and hyperscaler commitments. The 257% increase from Q2 2025 to Q1 2026 was not forecasted by industry analysts in early 2025. As AI workloads continue to evolve and hyperscalers adjust their capacity strategies, enterprises making multi-year storage investments face significant risk when locked into all-flash architectures.

VDURA mixed fleet systems provide three critical advantages:

1. Initial deployment flexibility: Choose SSD percentage based on current pricing
2. Expansion flexibility: Add capacity using SSDs or HDDs based on future pricing
3. Migration flexibility: Dynamically adjust tiering policies as workloads evolve

4. Competitive Positioning Analysis

4.1 AFA competitor: Separate S3 Tiering with Additional Costs

AFA competitor's architecture does support tiering to capacity storage, but through a fundamentally different approach than VDURA. While their Nitro and Prime platforms maintain a single namespace, the capacity tier relies on third-party S3 object storage that is priced and supported separately from the base system.

Key architectural constraints:

• Separate procurement: S3 capacity tier purchased independently from storage cluster
• Proprietary format: Data stored in S3 using vendor-specific format, creating lock-in
• Additional licensing fees: Vendor charges customers for data stored in S3 tier
• Third-party dependencies: Capacity tier supported by different vendor/team
• Fixed bandwidth bottleneck: S3 connections typically limited to ~5 GB/s

This approach creates split TCO where the all-flash tier experiences 189% cost increase (Q2 2025 to Q1 2026) with capacity tier costs tracked separately. The proprietary S3 format and additional licensing fees add ongoing operational expenses not present in VDURA's integrated architecture.

4.2 AFA competitor: All-Flash Focus

AFA competitor is a strong proponent of all-flash storage architectures. While they offer S3 connectivity for archival use cases, their primary positioning emphasizes SSD-only deployments using their specialized C+D Box architecture:

• C-Box nodes: Performance-focused servers with 40 GB/s throughput each
• D-Box nodes: Capacity-focused servers using SCM and QLC flash (no HDDs)
• All-flash design: Even "capacity tier" D-Boxes use expensive SCM + QLC SSDs
• Fixed ratios: C and D boxes deployed in specific ratios, limiting flexibility

The all-flash C+D architecture experiences 186% cost increase between Q2 2025 and Q1 2026—63 percentage points worse than VDURA's 20% SSD mixed fleet configuration. The use of SCM drives in D-Boxes, which increased from $200 to $700 each, particularly amplifies cost sensitivity to component price volatility.

4.3 VDURA: Integrated Mixed Fleet Architecture

VDURA's architecture achieves mixed fleet storage through intelligent tiering between homogeneous VPOD servers and parallel JBOD enclosures—all integrated into a single system with unified pricing and support. This design provides:

• Included in base system: JBOD capacity tier is part of system purchase, not separately procured
• No additional licensing: No per-TB fees for data in HDD tier
• Single vendor support: One support contract covers entire system (SSD + HDD tiers)
• Continuous tuning: Any SSD percentage from 5% to 100% in 5% increments
• Granular expansion: Add VPODs (performance + capacity) or JBODs (capacity only)
• Single namespace: No complex data movement between tiers or systems
• Scalable bandwidth: 21.5 GB/s per JBOD vs fixed 5 GB/s S3 connections
• Standard formats: No proprietary data formats creating vendor lock-in

The key differentiator is integration. While AFA competitor requires separate S3 procurement and charges additional fees, VDURA's HDD tier is included in the base system price with no ongoing per-TB charges. This transparent, all-inclusive pricing model simplifies TCO calculations and eliminates hidden costs.

5. Strategic Recommendations

5.1 For New Deployments

1. Analyze workload access patterns to determine true hot data requirements
2. Model TCO across multiple pricing scenarios using current and projected SSD costs
3. Select initial SSD percentage that balances performance requirements with current market pricing
4. Plan expansion strategy with flexibility to add SSDs or HDDs based on future conditions

5.2 For Procurement Teams

• Demand architectural flexibility in vendor proposals
• Request multi-scenario pricing that models cost impact of SSD price changes
• Evaluate total 3-year TCO rather than initial purchase price
• Consider vendor price protection or flexible expansion terms

5.3 For Storage Architects

• Implement tiering policies that maximize HDD utilization for cold data
• Monitor access patterns to validate SSD sizing assumptions
• Plan for expansion headroom in both performance and capacity dimensions
• Design for workload evolution with ability to adjust tier ratios over time

6. Conclusion

SSD pricing volatility has moved from theoretical concern to practical challenge. The 257% price increase from Q2 2025 to Q1 2026—driven by unprecedented AI demand and hyperscaler capacity commitments—represents millions of dollars in additional costs for large-scale storage deployments. With AI infrastructure continuing to expand and hyperscalers maintaining long-term purchase agreements, this supply-constrained environment is likely to persist. In this context, architectural flexibility becomes important for managing storage costs.

VDURA's mixed fleet architecture provides enterprises with tools to respond to market volatility:

• Tune configurations based on current SSD pricing
• Maintain performance requirements regardless of SSD/HDD ratio
• Reduce cost sensitivity to market fluctuations
• Preserve expansion flexibility for future deployments

All-flash architectures from AFA competitor and separately-priced S3 tiering from AFA competitor present challenges when pricing spikes occur. AFA competitor's approach requires separate procurement and additional licensing fees for the capacity tier, while AFA competitor's all-flash focus offers limited cost mitigation options. VDURA's integrated mixed fleet system with tunable SSD percentages and included HDD tier delivers better cost efficiency across different market scenarios while providing insulation against future volatility.

⚠️ Important Disclaimer

This technical bulletin is designed as a comparative analysis instrument to demonstrate the impact of rising SSD prices on different storage system architectures. It is not intended to provide accurate pricing forecasts or factual statements about any specific vendor's actual products, pricing, or commercial terms.

Methodology & Assumptions

• Uniform Commodity Pricing: All cost calculations use identical commodity component prices (SSDs, DRAM, CPUs, NICs, HDDs) across all vendors to ensure fair architectural comparison. This allows isolation of the impact of architectural differences rather than vendor-specific pricing negotiations or discounts.
• Simplified System Models: Each storage architecture is modeled using simplified parameters derived from publicly available sources. These models represent generalized configurations and may not reflect specific deployment scenarios, custom options, or vendor-specific optimizations.
• Publicly-Sourced Architecture Information: Competitor architectural specifications are derived from publicly available sources including datasheets, whitepapers, and technical documentation available as of January 2025. These may not reflect the latest configurations, product updates, or vendor roadmap changes.
• Estimated Margins: The 8% contract manufacturer margin and 15% channel partner cost shown are industry-typical estimates for comparative purposes. Actual margins vary significantly by vendor, deal size, customer relationship, market conditions, and negotiated terms.
• No Statement of Competitor Pricing: This document does not claim to represent, and should not be interpreted as representing, the actual prices, discounts, quotes, margins, commercial terms, or total cost of ownership offered by any third-party vendor. All competitor cost estimates are modeling exercises for architectural comparison purposes only.

What This Document IS

• A comparative framework showing how different storage architectures respond to commodity component price changes
• An educational analysis demonstrating the cost impact of SSD price volatility on all-flash vs. mixed fleet systems
• A technical examination of architectural trade-offs during periods of component price volatility

What This Document IS NOT

• A factual statement about any competitor's actual pricing, costs, or commercial terms
• A precise price quote or forecast for any vendor's products or services
• A guarantee that competitor systems cost exactly as modeled in this analysis
• A replacement for formal vendor quotations, RFP processes, or direct vendor engagement

For Accurate Pricing and Product Information: Please contact vendors directly for formal quotations, current product specifications, and commercial terms based on your specific requirements. Validate all assumptions and engage in direct negotiations before making purchasing decisions.

The comparative cost models presented in this document are based on publicly available architectural information and uniform commodity pricing applied consistently across all vendors for analytical purposes. They do not reflect actual vendor pricing, which may differ significantly based on numerous commercial factors including volume discounts, promotional programs, customer relationships, competitive situations, and negotiated terms.