If you’re building an AI feature, the first thing to model is tokens — not model choice. Pricing pages show per-1K-token rates, but they don’t multiply the math for your actual prompt and completion patterns.
The mistake I see most often is estimating from a single “hello world” call. That number is almost always wrong because real usage has three cost layers: input tokens from system prompts, output tokens from model responses, and hidden costs from retries, logging, or repeated calls in loops.
Input cost is usually predictable if you count your system prompt and the average user message length. Output cost is where variance hides — it depends on max response length, temperature settings, and whether your app chains multiple model calls per interaction.
A practical approach before writing any production code: estimate prompt, completion, and training tokens separately. Run the pricing math at your expected daily call volume, then add a 2x buffer for traffic spikes and experimental prompts.
The trap nobody budgets for is long-running costs from prompt caching, context window growth, or feature expansion that quietly increases token counts. Modeling the ranges — minimum, expected, and worst case — before launch prevents the “why is my bill so high” moment later.
If you’re comparing providers or models, build the estimate table once and swap rates. The math is the same regardless of whether you’re using GPT-4, Claude, or another provider — only the per-1K-token numbers change.
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