Though it is rational to note that AI inference costs are somewhat unpredictable at the moment, that also was true of cloud computing in general. But as the technology matures, it is likely--probably inevitable--that AI inference “as a service” will develop methods of providing customers more cost predictability.
Cloud computing, after all, also featured unpredictable, often expensive usage-based pricing, that made customer budgeting difficult.
But the market responded, creating pricing models (reserved instances, committed use discounts, and hybrid approaches) that provided cost predictability.
AI inference is likely following a similar trajectory. Token-based pricing means the cost per inference can fluctuate based on model complexity, input length, and provider capacity.
But providers already are experimenting with different approaches beyond pure pay-per-token models: subscription tiers, reserved capacity options or volume discounts that provide more predictable monthly costs.
Enterprise contracts increasingly include committed usage terms that offer better rate predictability as well.
And competition also will drive providers to offer more customer-friendly pricing structures. AWS, Google Cloud, and Azure all evolved toward more predictable pricing options as the market matured and customers demanded better cost management tools.
At the same time, models, hardware and inference acceleration will naturally drive down costs. So will analytics.
Some of us cannot see the cost unpredictability being a long-term issue for AI inference.
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