LLM inference costs can reach $4.7M annually due to underestimated traffic

The cost of running LLM features can be significantly underestimated, with teams often failing to multiply per-call inference costs by projected traffic volumes. A typical RAG query costing $0.015 per call can escalate to $4.7 million annually at a modest traffic level of 10 requests per second. Model selection is a critical financial decision, with a 30x cost spread observed across different models for the same workload. Optimizations should prioritize input tokens, as they constitute the majority of production LLM costs, rather than output tokens. AI

IMPACT Highlights the critical need for accurate cost modeling in LLM deployments, influencing model selection and optimization strategies.

RANK_REASON Article discusses the financial implications and cost-saving strategies for LLM features, rather than announcing a new release or product.

Read on Medium — MLOps tag →

• Claude Sonnet 4.6
• GPT-5 mini
• Haiku 4.5
• Opus 4.7
• retrieval-augmented generation

• infra
• product

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →