Back to listCategory performance Workaround partial Freshness emerging Scope framework Recurring Yes Buyer Type enterprise
Storage I/O performance bottlenecks in AI/ML workloads
7/10 HighStorage I/O performance is the primary performance concern (24%), followed by model/data loading times (23%). For AI/ML workloads, storage costs have become the dominant concern (50% cite as primary), reflecting enormous data requirements of training datasets and model checkpoints.
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Query: “What are the most common pain points with Kubernetes in 2025?”3/27/2026
Storage I/O performance is cited as the primary concern, followed closely by model/data loading times. For organizations running AI/ML workloads, storage costs (50%) have become the primary concern — reflecting the enormous data requirements of training datasets, model checkpoints, and inference results.
Created: 3/27/2026Updated: 3/27/2026