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TokenAtlas

AI Cost Optimization Guide

Most AI optimization advice is a tactic list. This guide sequences tactics into a four-stage playbook so teams know what to do first, second, and last.

Stage 1: audit

Pull 30 days of provider invoices. Break down by model and, if possible, by feature. The single largest line item is your starting point — everything else is noise.

Stage 2: quick wins (week 1–2)

Cap max_tokens, enable prompt caching, route obvious mini-tier traffic away from flagship. Typical: 30–50% savings, no product impact.

Stage 3: structural changes (month 1–3)

Build a routing layer, move async jobs to Batch API, instrument per-feature spend, set up alerting. Typical: another 20–30% savings.

Stage 4: continuous improvement

Quarterly model re-evaluation, prompt regression tests, per-customer unit-cost reviews. Locks in savings and prevents drift.

KPIs that prove the work

$/active user, $/successful task, gross margin on AI features, cache hit rate, model mix. Token volume is a vanity metric.

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