AI infrastructure needs more than accelerators and fast storage: it also needs a plan for where datasets, checkpoints, logs, embeddings and outputs live after compute work ends. Match storage to each workload stage, keeping active data on responsive systems while moving less-active data to capacity or archive tiers only when their retrieval time and protection fit the job.
Why AI storage is a lifecycle problem
Compute is reused; data persists. Training datasets may be reused for later runs, checkpoints preserve work in progress, and inference can generate logs, embeddings, synthetic data and outputs worth retaining. That means storage demand can accumulate even when a particular training job has finished.
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Western Digital Chief Product Officer Ahmed Shihab described the issue in the company’s May 2026 announcement: “AI is fundamentally a data systems challenge, not just a compute challenge. Our customers are on the front lines of solving it, and their needs directly shape our innovation roadmap and the technologies we build for the AI era and beyond,” he said. “While compute is reused, data persists — and grows.” Western Digital’s release reported survey findings, not a census of all organizations.
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How storage needs change across AI work
There is no single storage profile for an AI pipeline. SNIA’s March 2025 presentation distinguishes workload stages by capacity, access pattern and performance requirement:
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| Stage | Storage need described by SNIA | Planning implication |
|---|---|---|
| Ingest | High capacity and sequential reads | Plan for moving large datasets into the pipeline efficiently. |
| Training and tuning | Burst throughput and low latency | Keep data feeding active jobs on systems that can meet their performance needs. |
| Inference and tuning | Mixed random reads and writes | Account for access patterns that differ from large sequential ingest. |
| Archive | Very high capacity | Optimize for retained data, while making retrieval time and return-to-pipeline workflow explicit. |
The requirements come from SNIA’s “Storage Trends in AI” presentation. They explain why archive and active training storage should not automatically be treated as the same tier.
What belongs on fast storage—and what can move
Keep data on a high-performance tier when an active job needs its latency or throughput, when repeated access is likely, or when retrieval delay would disrupt an operational pipeline. Data that is retained for compliance, reproducibility, or possible future use but accessed infrequently may fit a capacity or archive tier instead.
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Do not treat “cold” as synonymous with “never needed again.” In the WD-sponsored IDC research, 75.9% of surveyed organizations reported bringing increasing volumes of archived cold-tier data back online to support AI workloads. That finding makes retrieval planning part of the architecture: identify which data may return, how quickly it must be available, and how it will be staged back into compute systems.
SNIA’s presentation poses questions such as “Where Does AI Generated Data Land in 2027?” and “What is the storage for AI Agents?” Those are useful planning prompts, not predictions that every generated item should be retained indefinitely. Define retention and reuse requirements for each dataset, log, checkpoint or output before assigning a tier.
How to choose tiers without assuming a universal winner
A tiered design can align performance to active work and capacity to less-active data, but it does not guarantee savings. Economics depend on data scale, retrieval frequency, infrastructure, and the operational work required to move and manage data. Compare the full workflow rather than media price alone.
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- Access and retrieval: What latency is acceptable, and how long does retrieval and staging actually take?
- Performance: Does the workload need sequential throughput, random I/O, burst capacity, or low latency?
- Capacity and growth: Can the tier scale as datasets, checkpoints and outputs accumulate?
- Total cost of ownership: Include infrastructure, power, management, migration and retrieval operations, not just cost per terabyte. In the WD-sponsored IDC summary, 98.2% of respondents considered TCO per terabyte important or very important in storage decisions.
- Protection: Assess resilience, backup, immutability and whether offline media adds a useful protection layer for the particular threat model.
- Energy and compatibility: Consider energy requirements and whether existing systems, formats and software can work with the tier.
- Pipeline return: Map how archived data is located, restored, validated and made available to AI jobs.
Flash or SSD can address performance-sensitive workloads; HDD can provide a capacity tier; tape is one possible enterprise archive approach. None is automatically best for every stage. McKinsey’s December 2024 baseline forecast estimated enterprise SSD demand at 181 exabytes in 2024 and 1,078 exabytes in 2030; this was a forecast whose assumptions included AI compute demand and data-center deployment constraints, not evidence that archive tiers are unnecessary. McKinsey’s analysis addresses the separate performance-storage opportunity.
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Where tape can fit—and its practical limits
Tape can serve as a high-capacity enterprise archive tier, and offline media can provide a protection feature by keeping a copy outside continuously online systems. But an archive is only useful if its access time and retrieval workflow suit the data’s actual reuse pattern. Tape is not a substitute for low-latency storage feeding an active training or inference job.
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The argument for tape in the September 2026 TechRadar Pro article “AI’s overlooked storage opportunity” comes from Skip Levens, identified as Quantum’s Product Marketer and AI Strategist for the LTO Program. He wrote: “The question for infrastructure planners, then, is not whether AI needs fast storage, but where organizations should keep the very large datasets that will be required in future, before they are ready to be processed.” That is a vendor-associated perspective; treat tape as an option to evaluate against workload, retrieval and protection needs, not a universal recommendation. Read the article.
For an enterprise tape archive, compatibility is essential: confirm that the intended media generation works with the drive or library, and account for the infrastructure and process needed to retrieve and restore data. The relevant general product category is an LTO data cartridge; it is useful only where a compatible tape drive or library and an appropriate archive workflow are in place.
What the market forecasts do—and do not—say
Market projections indicate growing infrastructure demand, but they do not decide how an individual organization should tier its data. JLL Research estimated that AI represented about one-quarter of data-center workloads in 2025 and projected it could reach half by 2030. JLL also projected inference could overtake training as the dominant AI requirement in 2027; that remains a projection, not a settled outcome. JLL’s 2026 Global Data Center Market Outlook provides that context.
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