7 guides
AI API usage forecasting mistakes that make LLM budgets too low. Learn how average request cost, output token growth, cache assumptions, retries, fallback, evals, batch jobs, and agent steps can make next-month AI spend exceed the forecast.
AI API cost forecasting guide for teams planning next-month LLM spend. Build baseline, growth, and stress scenarios from users, requests, tokens, model mix, retries, cache hit rate, evals, agents, and batch jobs without inventing model prices.
Monthly AI API cost review guide for teams using Claude, GPT, Gemini, DeepSeek, and other LLM APIs. Learn how to break down spend by feature, model, tokens, retries, cache hit rate, agents, and batch jobs, then turn the review into next-month cost governance actions.
Build an AI API cost budget spreadsheet from one request to monthly forecast, covering tokens, request volume, caching, retries, evals, and peak usage.
AI API cost alert guide for teams using Claude, GPT, Gemini, DeepSeek, and agent workflows. Learn how to monitor daily budgets, per-request cost, retry rate, output token growth, cache misses, agent tool loops, and batch job duplication before bills become incidents.
AI API cost incident postmortem guide for teams facing sudden bill spikes. Learn how to stop spend growth, reconstruct the timeline, attribute cost by feature, inspect token growth, retry loops, agent tool calls, batch jobs, and create prevention checklists for future launches.