Guides

Separate stable prefixes from dynamic tokens, estimate hit rate, and account for cache-write fees when planning spend.

How to set a latency SLO, compare planning p50 latency with list prices, and avoid overpaying for speed you do not need.

Connect token costs, adoption, and pricing so AI features stay within a healthy COGS band.

How UK, EU, Australian, and Canadian teams should budget OpenAI and Anthropic invoices that arrive in dollars.

Compare GPU-hour economics to hosted token prices, and know which assumptions flip the decision.

RPM, TPM, concurrency, and daily caps decide your real ceiling—often before list price does.

Use role weights and hard caps so a few power users do not consume the whole monthly AI pool.

Turn pages into tokens, account for chunk overlap, and decide when an LLM extract pass is worth the spend.

Estimate LLM spend when you split traffic or escalate hard cases from a cheap primary to a premium model.

Price an availability path: primary success rate, failed-attempt billing, and secondary spend during blips or outages.

Break eval spend into candidate generation and judge scoring—and see why rubric dimensions multiply the bill.

Price generate → critique → filter pipelines and understand why accept rate drives over-generation spend.

LLM rerank adds calls, but feeding top-N instead of top-K into the generator can cut context tokens enough to win overall.

A practical framework for turning traffic assumptions into monthly OpenAI, Anthropic, and Google API spend.