Four calculations that reveal your LLM feature's true monthly cost upfront

Developers often select AI models based on benchmark scores without estimating real costs, only discovering the financial impact weeks later when invoices arrive. A practical method breaks any LLM workload into four numbers — daily calls, input tokens per call, output tokens per call, and active days per month — to produce an accurate monthly cost estimate before writing code. Input tokens are the most commonly underestimated factor, since system prompts and retrieved context can dwarf the user's actual message on every single call. Comparing a frontier-tier model against a mid-tier alternative using illustrative figures reveals a difference of roughly one cent per conversation, which can scale to hundreds or thousands of dollars monthly at volume. Crucially, input and output token prices scale at different rates across tiers, so the model that appears cheaper overall may not be, depending on a workload's specific input-to-output ratio.