AI/ML

Storage 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.

26% of AI builders lack confidence in dataset preparation and trustworthiness of their data. This upstream bottleneck cascades into time-to-delivery delays, poor model performance, and suboptimal user experience.

Performance optimization has emerged as the #1 operational challenge (46%), displacing earlier basic adoption concerns. Organizations struggle to optimize performance across databases, AI/ML, and traditional containerized workloads simultaneously.