Why Data Reduction Fails For AI Inference Pipelines
DDN, Friday, January 30th, 2026
For more than a decade, data reduction technologies such as deduplication and compression have been marketed as universal solutions to storage cost and scale challenges. In traditional enterprise workloads, those techniques deliver real value.
But AI has changed the rules.
As organizations build AI inference pipelines powered by massive volumes of unstructured data, many are discovering an uncomfortable truth:
Data reduction provides little to no economic benefit for AI inference. And in some cases, actively works against performance and cost efficiency.