Why tape storage matters for AI data

AI infrastructure is usually discussed in terms of data-center costs, power and compute. But the data used to train and run models is another critical resource. Stanford University’s 2025 AI Index Report says training dataset sizes for LLMs are doubling every eight months.

Not every dataset needs high performance at every stage. Some data moves quickly into curation and model-development systems, while other material can remain securely stored until it is needed. The challenge is keeping large datasets available to AI workflows without paying for always-on, high-performance infrastructure across the entire data estate.

Tape remains one option for that lower-cost tier. Successive LTO generations have increased capacity, throughput and security, and modern tape systems can stream data into faster storage when curation or training begins. At multi-petabyte scale, tape can cost less per terabyte than flash or HDD infrastructure, while performance and capacity can scale independently.

Tape also provides hardware encryption, Write-Once-Read-Many (WORM) media and the ability to move cartridges offline or offsite. Those features can help organizations keep more proprietary data available, protected and recoverable for AI pipelines, while limiting the amount of high-performance storage they need to keep online.