How prompt caching reduces LLM API costs
Separate stable prefixes from dynamic tokens, estimate hit rate, and account for cache-write fees when planning spend.
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Separate stable prefixes from dynamic tokens, estimate hit rate, and account for cache-write fees when planning spend.
Read articleHow to set a latency SLO, compare planning p50 latency with list prices, and avoid overpaying for speed you do not need.
Read articleConnect token costs, adoption, and pricing so AI features stay within a healthy COGS band.
Read articleHow UK, EU, Australian, and Canadian teams should budget OpenAI and Anthropic invoices that arrive in dollars.
Read articleCompare GPU-hour economics to hosted token prices, and know which assumptions flip the decision.
Read articleRPM, TPM, concurrency, and daily caps decide your real ceiling—often before list price does.
Read articleUse role weights and hard caps so a few power users do not consume the whole monthly AI pool.
Read articleTurn pages into tokens, account for chunk overlap, and decide when an LLM extract pass is worth the spend.
Read articleEstimate LLM spend when you split traffic or escalate hard cases from a cheap primary to a premium model.
Read articlePrice an availability path: primary success rate, failed-attempt billing, and secondary spend during blips or outages.
Read articleBreak eval spend into candidate generation and judge scoring—and see why rubric dimensions multiply the bill.
Read articlePrice generate → critique → filter pipelines and understand why accept rate drives over-generation spend.
Read articleLLM rerank adds calls, but feeding top-N instead of top-K into the generator can cut context tokens enough to win overall.
Read articleA practical framework for turning traffic assumptions into monthly OpenAI, Anthropic, and Google API spend.
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