The Token Pricing Illusion: AI Inference Costs | CoreWeave Blog

This post introduces a diagnostic metric for 'cost per useful token' to address the limitations of token pricing for AI inference. It defines a 'useful token' based on latency SLOs, context relevance, and single billing. The post analyzes three ways sticker price abstracts reality: SLO misses, idle capacity, and autoscaling overhead. It then categorizes four production inference workload patterns (Exploration & Iteration, Variable Production, Steady-State Production, Agentic at Scale) and recommends either token pricing or GPU-billed pricing based on utilization and performance requirements. This provides a framework for customers to evaluate their inference economics beyond simple per-token costs.