Blog
LLM pricing guide for engineering and finance teams
Modern products ship features on top of large language models, which means “unit economics” now includes prompt tokens, completion tokens, and model tier. This short guide frames how to talk about LLM pricing internally, what to measure first, and where a dedicated calculator helps.
It pairs with our free AI Token Cost Calculator so you can turn rough usage assumptions into per-request and monthly projections before you commit to architecture.
Start with tokens, not headlines
Vendors publish dollars per million or thousand tokens for input and output. That single fact drives most spreadsheets: multiply normalized token counts by the published rate, sum input and output for one call, then scale by traffic.
When you compare models, keep the workload identical. A cheap model with a sloppy prompt can cost more than a mid-tier model with a tight system message, because output length dominates many workloads.
Where calculators add clarity
Spreadsheets drift; calculators encode assumptions explicitly. Use presets for chat, summarization, or coding workflows, then stress-test with higher completion caps to see worst-case spend.
Cross-check any estimate against the vendor billing console before procurement decisions—rates and promotions change frequently.