What you'll learn: How to calculate total cost of ownership (TCO) for LLMs beyond token pricing A framework for comparing inference speed, latency, and operational efficiency How to benchmark real-world performance across different model providers W...
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dualdust
I like the focus on traffic shape. I would add one more comparison layer: cost per accepted outcome, not only cost per model call.
For RAG or agent workflows, a cheaper model can still need more context, retries, reranking, or eval passes. That can make raw dollars-per-million-tokens misleading.
The budget I would want to see is workflow-level: input, output, retrieval/context assembly, fallback/retry, and eval/judge calls.