How to Reduce Storage Costs During the SSD Shortage
Storage economics in the age of AI are forcing organisations to think more strategically about where their data lives. As SSD shortages, demand for AI infrastructure, and constrained supply chains drive enterprise storage costs higher, organisations can no longer afford to over-provision high-performance tiers for cold or infrequently accessed data. Instead, many are turning to intelligent tiering, metadata-driven data management, and hybrid storage architectures to reduce costs, improve resilience, and make better use of the infrastructure they already have.
Key takeaways (tl;dr):
Keeping cold or warm data on high performant flash drives is not affordable.
Organisations that haven’t invested in metadata management are discovering their tiering policies are too blunt to be truly effective.
Most enterprises have significantly over-provisioned expensive tiers simply because they lack the data insights to do otherwise.
It’s time to audit what’s actually on your current storage, understand the real access patterns of your data estate, and design the next architecture around a tiered, policy-driven model rather than a monolithic one.
“Flash storage is the new toilet paper…” Hearkening back to pandemic-lockdown-era supply chain interruptions, this strikes at the heart of the current industry squeeze: What we’ve come to take for granted as a cheap and simple solution to our daily mess is suddenly prohibitively expensive or impossible to get at all. Those who can are stockpiling and those who can’t need to get inventive.
The hardware shortage is a reckoning. It is forcing storage teams to pause and genuinely rethink rigid, single-tier, all-flash systems. The former storage paradigm has disintegrated, making retaining cold or warm data on expensive flash cost prohibitive.
Years of best-practice advice, casually kicked down the road while scrambling to stay atop never-ending tech refreshes and the lure of operationally simpler options, are now coming into focus as urgent implementations. Software-defined, policy-driven approaches to reduce dependencies on any specific hardware SKU suddenly look too good to keep putting off.
Intelligent tiering is now elevated from a blue-sky best practice, typically sidelined for the sake of streamlined operations, to a cost-management imperative.
Supply shortage drivers
We’re seeing the run-away effects of geopolitical instability disruptions cranking up the pressure on the combined demand shock and structurally constrained supply chains that were already squeezing storage hardware markets a year ago. SSD and HDD manufacturers sold out their supplies a year or more in advance to meet the exploding AI inference cluster demand, with the largest hyperscalers locking in multi-year supply agreements, leaving a mere trickle for everyone else. The long lead time to bring more fabrication capacity online and manufacturer hesitations to overcommit, coupled with raw material scarcity, led to the IDC warning that NAND and enterprise SSD pricing pressure and limited availability are likely to be the norm for at least the next 18–24 months.
With hyperscalers and large AI companies continuing to syphon off the bulk of GPUs, high-end memory, and flash storage, traditional enterprises are seeing huge price increases on the limited storage options available. Nearline HDD costs increased by a third over the past eight months, while enterprise-grade and high-capacity SSD models have jumped more than 200% — budget exploding hikes for anyone planning routine upgrades. Scarcity and prohibitive costs on premium hardware create a cascading effect, heaping more pressure onto secondary options, such as QLC SSDs, generating year-long delivery delays, and meaning infrastructure projects planned for 2026 may face indefinite postponement unless inventory was secured well in advance.
The big picture for enterprise IT
The purely transactional procurement model, reliant on short-notice commodity sourcing at predictable prices, is broken. This means down-scoped projects, extending refresh cycles past their sensible end-of-life, and IT teams spending disproportionate time on supply chain management rather than delivering against their strategic roadmaps. But it’s also an opportunity to look at other ways around the problem.
Organisations running on-prem AI/ML training or inference workloads are certainly getting the sharp end of the stick. However, industries with large unstructured data estates, such as media and entertainment, life sciences, research institutions, and financial services, are feeling the pinch from both ends as both nearline HDD and archive tiers get constrained simultaneously. The operations doing large-scale video production, genomics, seismic data analysis, or anything with rapidly growing cold data repositories need to rethink their approach.
Policy-driven tiering unlocks organisations from single storage medium dependencies
Auto tiering has existed for years and always been technically sound, but financial pressures are making it a necessary architectural consideration. When SSD was scarce but reasonably priced relative to HDD, an organisation could rationalise over-provisioning flash for operational simplicity. But ballooning storage centre costs over the last year have re-quantified the argument.
Metadata management coupled with rich data intelligence transforms hardware dependencies into a software policy problem. Software policy problems are significantly more flexible and not tied to any particular piece of equipment. Automating and governing data movement between tiers based on access patterns and business rules, rather than manual intervention, allows an organisation to respond to supply constraints by adjusting policies: move more data to HDD or object storage when SSD is sparse and expensive; pull it back to flash when the data is needed. Platforms built for agility adapt more effectively to changing market conditions than their rigid, all-flash counterparts.
Not all workloads and data types are optimal for tiering, but those with predictable waterfall access patterns (Almberg, et. al, 2005) — backup and archive data, historical records, post-compute research datasets, post-production media assets, post-analysis log files, post-study genomics data — lend themselves well to policy-driven tiering. These data types, which are rarely accessed after their initial flurry of activity but require long-term archiving, have traditionally dominated nearline HDD storage.
While most storage architectures include both hot and warm tiers, expanding beyond that simple structure introduces technical challenges. Seamlessly integrating object and tape storage requires robust data management strategies. Good tiering systems keep data accessible at acceptable latency on HDD or dense object storage, avoiding bloating premium flash capacity. But to do so effectively, data managers need deep insights into access patterns and software to automate the workload. This is where a metadata management layer proves invaluable.
Three key tiering implementation / optimisation barriers facing organisations:
First, a lack of data intelligence. If you don’t know what you have, how old it is, who accesses it, and how frequently, you can’t tier rationally. Organisations that haven’t invested in metadata management often discover their tiering policies are too blunt to be effective.
Second, operational fear. There’s a genuine concern about SLA transgressions when data moves to a lower tier. Without good tooling to validate access patterns and tiering behaviour, that fear drives expensive flash over-provisioning.
Third, organisational inertia. Many storage architectures were designed years ago for a specific workload profile that has evolved into a different beast. Redesign efforts get perpetually deferred as they lose ground to other priorities. A storage shortage is a catalyst to break that inertia.
An insidious element of these barriers is the sticky misconception that storage utilisation equals storage efficiency. A storage array at 70-80% capacity may or may not be well managed — utilisation tells you nothing about whether data is sitting on the most appropriate tier. Most long-lived enterprise environments have a significant portion of flash clogged with cold or redundant data that hasn’t been accessed in months or years. Like a teenager’s room, the organisational incentive to overcome so much inertia and clean it up just isn’t there. This hardware shortage is the parent threatening to confiscate all devices and trash the collectables if it’s not spic-n-span by the time they get back.
A few elite universities around the world have deployed metadata-driven storage management in recent years. These pioneers are gaining insights into what they own, how it is being used and what it actually costs — and their findings are staggering. One institution determined they can archive 10s of petabytes of existing data, while another has overhauled their storage balance, reducing their high-end storage by 15 PB for a savings of ~$3M. Yet a third is completely reimagining their entire research data ecosystem around metadata-rich systems to optimise cutting-edge research data management for the next 100 years.
How object storage and tape fit into modern tiering architectures
Workload distributions are the principal consideration for teams trying to determine whether to maintain their cloud-first strategies as high performant storage costs rise or to build out their on-prem capabilities. The total-cost-of-ownership for these options has skewed considerably over the past few years. While cloud storage may certainly be the right choice for those primarily concerned with agility or juggling genuinely unpredictable demand pattern workloads, the costs at petabyte scale, combined with steep egress fees, are significant enough to recommend reviewing the numbers.
For large unstructured data workloads, cloud storage costs are now comparable to on-prem TCO, with the price gap widening significantly for large, stable, warm to cold datasets. Cloud providers price object storage competitively for the first few petabytes, but as datasets scale into the 10s-100s of petabytes with frequent retrieval, the cost comparison tilts increasingly towards hybrid or on-prem options. Calculating the exact TCO for any option is highly sensitive to specific workload, access patterns, data durability requirements, and whether fully loaded on-prem infrastructure management costs are included. Organisations that haven’t done a rigorous workload-by-workload TCO analysis in the last year, however, should do so.
Object storage has evolved significantly since its early days. With read/write speeds now matching traditional file storage performance and the S3 API natively supported by most data management platforms, object storage is a competitive cost-per-TB option. Whether on commodity hardware or cloud or tape, unstructured data at scale is genuinely competitive per terabyte, making it a natural target for cooler data, such as media files, research datasets, logs, and backups.
Tape, on the other hand, feels a bit more retro but is seeing a strong resurgence. Organisations such as national labs, archives, and media and entertainment organisations, managing multi-petabyte archives for decades or more, never abandoned its economies of scale. What has progressed is the ability of data management platforms to make the tape tier transparent and accessible without requiring manual operator intervention. Coupled with the quicksilver data management capabilities of a platform like Mediaflux® from Arcitecta®, tape isn’t old school, it’s trailblazing.
Hybrid storage models make more sense as supply pipelines dry up
Storage constrictions press the point for hybrid architectures, which were always technically sound but organisationally harder to justify. Approaches gaining traction include tiering data intelligently between flash, disk, and cloud; consolidating and extending existing NAS and object storage, and adopting software-defined and disaggregated storage architectures. All these options reduce specific hardware dependencies, giving organisations flexibility when supply or pricing changes. In an unpredictable market, hybrid solutions are resilient and sustainable architectures, not compromises. Organisations embracing a storage-medium-agnostic unified data management layer like Mediaflux, which facilitates fluid and transparent data movement across on-premises and cloud tiers based on policy, will be best positioned to weather the flash storm.
Strategic design and housekeeping
Waiting for prices to normalise in the near term is a risky gamble. It’s imperative to rethink architectures, refresh strategies and sourcing models both to stay afloat in the near term and mitigate the effects of future market strictures. Critical procurement timelines need to be bumped up where possible while developing and approving alternative configurations before shortages force last-minute substitutions. Establishing relationships with secondary and tertiary suppliers is another wise step.
Most importantly, investing in data intelligence and tiering automation to minimise premium storage media dependencies in proportion to actual workload requirements, not what historical provisioning habits assumed. Disaggregated software-defined storage architectures reduce hardware bottlenecks, providing flexibility and agility in a volatile supply environment. Employing data management tooling that provides visibility into the data you have, where it lives, when it was last accessed, and what it costs is now elevated from an idealised feature to a genuine business necessity. It’s impossible to optimise a storage estate when flying blind on utilisation and access patterns.
Final words
Organisations wading through a refresh cycle right now can’t afford to default to like-for-like hardware swaps. It’s critical to take a few strategic steps: Implement software-defined storage techniques to minimise write cycles, thereby extending existing flash media lifespans and sweating current assets a little longer; couple data consolidation and efficient tiering to alleviate the immediate need for net-new hardware; leverage the present supply constraints and elevated prices as an opportunity to audit storage access and usage; and design smarter, more resilient systems based on that insight.
Organisations that have invested in metadata-driven data management are already discovering opportunities to reduce costs, improve utilisation, and build more resilient storage architectures. As storage markets continue to tighten, visibility into data usage patterns will only become more important.
Reference
Almberg, L., Francis, R., Lohrey, J., & Soo, A. L. (2025). Developing a Data Report Process. eResearch Australasia 2025, Brisbane, Australia. Zenodo. https://doi.org/10.5281/zenodo.17772625
