The Token Pricing Illusion: AI Inference Costs | CoreWeave Blog

What this post added

This post introduces the concept of the 'token pricing illusion' in AI inference economics. It defines a 'useful token' based on latency SLOs, context relevance, and single payment. The post identifies three key areas where sticker token pricing abstracts reality: SLO misses, idle capacity overhead, and autoscaling overhead. It proposes 'cost per useful token' as a more accurate diagnostic metric and outlines four workload patterns (Exploration & Iteration, Variable Production, Steady-State Production, Agentic at Scale) with corresponding optimal pricing strategies (token pricing vs. GPU-billed). It also highlights CoreWeave's Serverless Inference and Dedicated Inference offerings as practical implementations of these pricing models.