Introduction
Have you wondered why storage media have become more expensive than ever before? For example, the cost of a 2 TB Portable Solid–State Drive (SSD) in 2026 is 150% or more than its price in 2024 and 2025. What drove this steep price increase, and what does it mean to users, both businesses and individuals? Read on…
Between 2024 and 2026, the global storage market for Dynamic Random‑Access Memory (DRAM), Solid‑State Drives (SSD), HDD – Hard Disk Drive (HDD), Network‑Attached Storage (NAS) and removable media entered a sustained price upcycle. Unlike previous boom–bust sales cycles, the current increase is structural and is driven primarily by the explosive growth of AI workloads and the deliberate re–allocation of manufacturing capacity toward higher–margin Artificial Intelligence (AI) infrastructure. The consequence of this shift is a multi–layered impact.
- Storage manufacturers now optimize for AI profitability and are seen to shy away from storage solutions for traditional computing.
- Cloud service providers absorb costs but pass on higher infrastructure costs to their customers.
- Enterprises that consume storage solutions face rising Total Cost of Ownership (TCO).
- Small & Medium Enterprises (SME) delay storage refresh cycles, upgrades and are choked between rising technology costs and using older technology infrastructure.
- Individual consumers are forced to purchase under–configured devices at higher prices.
And don’t forget that device aging accelerates due to motherboard–imposed limits on RAM and storage expansion making later upgrades impossible even if prices stabilize.
The Shift
Up until 2024 and through the first half of 2025, storage media and memory demand–supply fluctuated. These components were priced lower and they were commonly available. However, between 2025–2026, AI workloads have increased significantly. Large Language Models (LLMs), inference engines and vector databases are memory and storage–intensive and consume significantly more bandwidth and capacity than traditional enterprise workloads. As a result, storage and memory are no longer treated as commodity inputs to computing equipment, but as strategic infrastructure. To meet this increasing demand, manufacturers of storage media and memory components, including industry giants such as Samsung and Micron are seen to prioritize long–term, high–margin contracts with hyper–scalers and AI platform providers and reduce their exposure to consumer electronics and traditional system requirements. This has created inelastic demands that outbid Personal Computers (PC), Laptops and consumer devices. Typical components affected include:
- High Bandwidth Memory (HBM) that delivers extremely high memory bandwidth with lower power per bit. HBM is critical for AI / Machine Language (ML), High‑Performance Computing (HPC) and advanced graphics workloads in data centers and cloud environments where memory throughput is a primary requirement.
- Double Data Rate 5 (DDR5), the fifth‑generation system DRAM standard that doubles per‑pin data rates over DDR4 with increasing capacity and efficiency. DDR5 underpins modern servers and cloud instances by supporting higher core counts and denser workloads without impacting a computer’s Central Processing Unit (CPU) and main‑memory bandwidth requirements.
- Enterprise Non‑Volatile Memory Express (NVMe) (Peripheral Component Interconnect Express (PCIe)–Gen4 / Gen5). NVMe is a high‑performance storage protocol that uses PCIe Gen4 / Gen5 to connect SSDs directly to the CPU with massive parallel queues and low latency. It powers fast transactional databases, analytics and AI pipelines in on‑premise and cloud computing environments, dramatically reducing Input / Output wait times compared to SATA/SAS SSDs
- High–capacity HDDs (20TB+). 20TB+ HDDs are large‑capacity spinning disks that offer the lowest cost per terabyte of data for both cold and warm data storage, backups and bulk object storage. They are essential in hyperscale and enterprise clouds for massive datasets (e.g., logs, archives, media) where capacity and Total Cost of Ownership (TCO) matter more than latency.
Manufacturer–Specific Developments
- Samsung
Over 2024 and 2025, Samsung is seen to focus on memory components to cater to a strong AI demand. In their Q3 2025 results, Samsung stated that they achieved a “Record–High Quarterly Revenue”. For 4Q 2025 Outlook, Samsung stated that they would actively respond to AI / Conventional Server Demand and for 2026 Outlook, they would strategically focus on HBM, through prioritizing the expansion of HBM4 business with differentiated performance to address increasing AI demand. Samsung’s intent is clear.
- Micron Technology
Micron is on a similar strategy. Micron’s Financial Results, FQ1 2026 quotes, “Memory is now essential to AI’s cognitive functions, fundamentally altering its role from a system component to a strategic asset that dictates product performance from data center to the edge”. The Company also states that their customers’ AI data center build–out plans have driven a sharp increase in demand forecasts for memory and storage and so the aggregate industry supply will remain substantially short of the demand for the foreseeable future.
- Western Digital
In its recent news release, Western Digital announce that the Company would focus on delivering cutting–edge innovation that will drive the next generation of AI–driven data workloads.
Across the industry, storage and memory manufacturers are converging on a similar strategy. Optimize for AI data centers and high–capacity storage tiers. Consequently, consumer products such as USB drives, SD cards and entry–level SSDs receive less innovation and fewer discounts, reinforcing upward price pressure.
Impacts on Various Stakeholder Groups
- Storage Technology Vendors
Storage supplying vendors are likely to benefit from higher margins and more predictable revenue streams, but they are also likely to face increased concentration risk tied to AI investment cycles and hyper–scaler spending patterns.
- Cloud Service Providers
Cloud Service Providers (CSPs) are likely to face rising infrastructure costs, particularly for NVMe–backed storage tiers. Major CSPs like AWS, Microsoft Azure and Google Cloud need massive amounts of SSD storage, NVMe drives, DRAM and Data center hardware. AI Systems consume huge amounts of storage and memory, and these components will be more expensive for the CSPs. It is predictable that these costs will flow down to the CSPs customers. In essence, the era of “storage always gets cheaper over time” is coming to an end.
- Large Enterprises (Financial Institutions)
Large enterprises such as Financial Institutions (Fis) are likely to experience higher Total Cost of Ownership (TCO) for their endpoints (e.g., Laptops, Mobile Phones) and Technology Infrastructure. Fis also need to meet their regulatory retention requirements and ensure encryption of data. These overheads further amplify their storage consumption. Understandably, Fis will be forced to examine their data lifecycle management practices and explore tiered storage architectures.
- Small and Medium Enterprises (SMEs)
SMEs are disproportionately affected by this situation, due to their limited bargaining power and capital limitations. SMEs may possibly resort to delaying their refresh cycles on technology hardware components and may also place reliance on refurbished hardware. However, this may result in an increase in their operational risk and productivity loss.
- Individual Consumers and Families
Expect individual consumers to be faced with laptops that have downgraded base configurations (e.g., 8GB RAM, 256GB SSD) at higher prices. The configurations of these devices (e.g., motherboard limits) further prevent meaningful upgrades, leading to an accelerated device obsolescence.
Outlook and Trajectory (2026–2029)
The golden era of reducing prices in storage is over, for the foreseeable future. The storage market is unlikely to revert to pre–2024 pricing dynamics. The industry understands that AI demand remains structurally strong and supply discipline for storage components is now an explicit strategy for not only the manufacturers but suppliers of storage solutions. Arguably, over the next three to five years, storage will remain a premium input in technology ownership, with clear segmentation between AI–grade memory and consumer–grade capacity. To sum up,
- Enterprises may want to procure devices with maximum upgradability upfront and invest in data lifecycle governance, especially to meet regulatory obligations.
- SMEs may want to favor business–class, modular hardware that can enable them to sail through for sufficiently larger time frames.
- Individual consumers should expect higher upfront costs on their laptop purchases and prioritize longevity over fancy features.
Closing Thoughts
AI has permanently altered the economics of memory and storage. Storage and memory are undergoing a structural reset driven by AI. Leadership teams of enterprises must now treat data infrastructure not as a commodity or a routine IT expense, but as a strategic capital asset that shapes competitiveness, risk, and regulatory posture through 2026 and beyond. Remember, the most significant impact is not the price rise itself, but the acceleration of device aging caused by configuration limits in an environment where upgrades are increasingly expensive.
The new reality: Storage is no longer cheap, and memory is no longer a commodity. It is strategic infrastructure in the AI–first world.
For you to think through…
- Have you reviewed your plans for your storage needs in 2026 and beyond?
- How do you position your AI and data strategy against increasing storage costs?
- What are the regulatory, operational, and data–retention risks that can emerge for you, if your storage economics force changes to encryption, retention and data archival needs?
- How are you planning to rebalance your on–premises, cloud, and hybrid architectures if cloud storage prices start to spiral upwards?
- What is the impact to your productivity, security, and TCO for extending the use of downgraded endpoint configurations against faster device aging and how do you plan to manage your TCO?
About This Article:
This Article explores the influence of AI on the strategic shift in storage and memory components of technology ownership and what they mean to owning, subscribing and operating technology devices in 2026 and beyond. Analyzing recent industry trends, this Article highlights the financial and operational risks that are likely to hit various types of consumers, both corporate and individual.
This Article is authored by, Narasimham Nittala who leads the Strategy and Research vertical of Financial Technology Frontiers (FTF) and is published as part of FTF’s Hi2AI Series. Like our previously published Articles, this Article is written in an accessible, practitioner–focused format and it aims to raise awareness about responsible AI adoption across Financial Institutions.
FTF believes that financial service providers, Fintech entities, consulting firms and technology companies can all benefit from reflecting on the perspectives shared here and consider how their own approaches to AI Risk Management can evolve. Practitioners in other industries are equally encouraged to adapt these insights to their unique contexts.
About Hi2AI
Hi2AI is FTF’s AI ecosystem For Financial Services. Hi2AI is a trusted community shaping the future of Artificial Intelligence in Financial Services by driving responsible innovation, influencing policy with regulators, and crafting future standards that ensure growth, resilience, and trust across the global financial ecosystem. Hi2AI exists to accelerate the responsible adoption of AI across the global financial ecosystem through:
- AI–Driven Industry Collaboration.
- Ecosystem Connection & Innovation.
About Financial Technology Frontiers
Financial Technology Frontiers (FTF) is a global media–led fintech platform dedicated to building and nurturing innovation ecosystems. We bring together thought leaders, financial institutions, fintech disruptors, and technology pioneers to drive meaningful change in the financial services industry.
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