The adoption of AI by enterprises is limited by the complexities of predicting ongoing inference costs linked to cloud services. While businesses increased spending on cloud infrastructure by 21% year-on-year, the unpredictability of usage-based pricing models for AI services creates hesitancy. As companies transition from piloting to deploying AI, they grapple with the need for cost efficiency and effective budgeting to avoid exorbitant bills, leading to restricted usage and underutilization of AI capabilities.
"Unlike training, which is a one-time investment, inference represents a recurring operational cost, making it a critical constraint on the path to AI commercialization."
"As AI transitions from research to large-scale deployment, enterprises are increasingly focused on the cost-efficiency of inference, comparing models, cloud platforms, and hardware architectures..."
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