Inference Cost

Inference cost refers to the financial expenditure incurred when operating an AI language model — the costs of processing every user request. Unlike training costs (one-time, very high), inference costs accrue continuously with every user request and represent the dominant AI cost factor in ongoing operations. Inference costs are typically billed in price per token. As of 2026: GPT-4o approximately $2–5/M input tokens and $8–15/M output tokens; Claude Sonnet at $3/M input, $15/M output; more affordable models like Claude Haiku or Gemini Flash range from $0.25–1/M tokens. Output tokens are more expensive than input tokens (due to sequential generation overhead), so cost-efficient systems actively optimize output length. Cost drivers include: model size (more parameters = higher cost), context length (longer contexts increase input token costs disproportionately), output length, provider hardware, peak vs. off-peak usage, and licensing model (API vs. self-hosted). Inference costs have fallen over 100× since 2023 — GPT-4-equivalent performance now costs ~1% of its 2023 price, driven by hardware advances and competition. This trend continues with Blackwell and Vera Rubin deployments. Key optimization strategies: model routing (cheap models for simple tasks), batch inference (50–75% discount), prompt optimization (request shorter outputs), caching frequent requests.