The Total Cost of AI Ownership (AI TCO) | Cohere

This post details the unit economics of AI ownership by breaking down the costs beyond token pricing. It highlights that the 'system that produces the token' is the true driver of TCO. Key cost drivers identified include the number of tokens consumed per task, model calls per user action, model routing, context window size, retry frequency, latency requirements, and infrastructure utilization. It provides concrete cost comparisons between owned hardware (e.g., NVIDIA H100, GB300) and cloud/API rentals, showing owned inference costs around $0.11-$0.12 USD per million tokens versus $0.89 for cloud instances and $2.00 for frontier APIs. The post emphasizes that model efficiency (e.g., MoE, low-bit quantization) and hardware utilization are critical for reducing AI costs, and that for sustained high volumes, owning infrastructure becomes significantly more cost-effective than renting.