After GPUs, storage capacity has emerged as the next major constraint for AI data centers. Hard-drive lead times are ballooning to more than a year, and enterprise flash storage is also expected to see shortages and price increases, experts say. This is being driven by an explosion in AI inferencing as trained models are put to use.
“AI inference isn’t just a GPU story, it’s a data story,” says Constellation analyst Chirag Mehta. “Expect tight supply into 2026, higher pricing, and a faster move to dense [flash storage] footprints, especially where power, space, and latency are constrained.”
Dell’Oro Group projects the according to TrendForce, and this might happen even more in the future as SSD prices come down and HDDs run into constraints.
“HDD bit output is difficult to increase,” says TrendForce analyst Bryan Ao. “AI will generate more data than the growth in HDD output. It is necessary to prepare for this with SSD storage.”
And 256-terabyte QLC SSDs are coming in 2028, he adds. QLC stands for “quad-level cells” and is an update to the triple-level cells, or TLCs, in the previous generation of SSD storage. “Such large-capacity QLC solutions will be more cost-effective to compete with HDDs from a total cost of ownership and performance perspective,” Ao says.
QLC is optimized for network-attached storage and can handle petabyte and exabyte-scale AI pipelines, according to Roger Corell, senior director for AI and leadership marketing at Solidigm, an SSD manufacturer. “QLC has tremendous savings in terms of space and power,” he says. “And when data center operators are focused on maximizing the power and space envelopes that they have for the AI data center build, they’re looking to get as efficient storage as they can.”
According to the TrendForce report, SSD manufacturers are increasing QLC SSD production, but AI workloads will also expand, “leading to tight supply conditions for enterprise SSDs by 2026.”
Corell says his company is seeing “very, very strong demand.”
But that doesn’t mean that SSDs are going to completely take over, he adds. “I think looking into 2026 and beyond it’s going to take a mix of SSDs and HDDs,” Corell says. “We do believe that there is a place for HDDs, but some of the demands for AI are clearly pointing to QLC as being the optimal storage for AI workloads.”
AI deployment uses multiple storage layers, and each one has different requirements, says Dell’Oro’s Fung. For storing massive amounts of unstructured, raw data, cold storage on HDDs makes more sense, he says. SSDs make sense for warm storage, such as for pre-processing data and for post-training and inference. “There’s a place for each type of storage,” he says.
Planning ahead
According to Constellation’s Mehta, data center managers and other storage buyers should prepare by treating SSD procurement like they do GPUs. “Multi-source, lock in lanes early, and engineer to standards so vendor swaps don’t break your data path.” He recommends qualifying at least two vendors for both QLC and TLC and starting early.
TrendForce’s Ao agrees. “It is better to build inventory now,” he says. “It is difficult to lock-in long term deals with suppliers now due to tight supply in 2026.”
Based on suppliers’ availability, Kioxia, SanDisk, and Micron are in the best position to support 128-terabyte QLC enterprise SSD solutions, Ao says. “But in the longer term, some module houses may be able to provide similar solutions at a lower cost,” Ao adds. “We are seeing more module houses, such as Phison and Pure Storage, supporting these solutions.”
And it’s not just SSD for fast storage and HDD for slow storage. Memory solutions are becoming more complex in the AI era, says Ao. “For enterprise players with smaller-scale business models, it is important to keep an eye on Z-NAND and XL-Flash for AI inference demand,” he says.
These are memory technologies that sit somewhere between the SSDs and the RAM working memory. “These solutions will be more cost-effective compared to HBM or even HBF [high bandwidth flash],” he says.
On the positive side, SSDs use standard protocols, says Constellation’s Mehta. “So, interface lock-in is limited,” he says. “The risk is roadmap and supply, not protocol.” He recommends that companies plan ahead for price and lead-time volatility — and power.
“US data center energy constraints are tightening,” he says. “Storage total-cost-of-ownership conversations now start with watts per terabyte. In 2026, your bottleneck may be lead time or power, not just price. Architect for either constraint, and you’ll make better storage decisions.”
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