AI Cost Optimization Guide

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.

Worked example: applying the four stages

Starting point: 100K calls/month, 4K in/600 out, all GPT-4o, no caching — roughly $1,600/month (see the GPT-4o worked example). Stage 2 quick wins (cap output, cache a 2K-token static system prompt): input drops toward $850, output capped at 500 tokens drops output to about $500, total ≈ $1,350. Stage 3 routing 60% of simple calls to mini: mini share ≈ $58, flagship share (40K calls) ≈ $540, total ≈ $600 — roughly a 62% reduction from the stage-1 baseline across the two stages combined.

Mechanics: why savings compound across stages rather than stacking linearly

Each stage acts on a different part of the cost equation — caching reduces the effective input rate, output caps reduce output volume, and routing reduces which rate applies at all — so their percentage savings multiply against a shrinking base rather than adding together. This is why a naive sum of "30% + 30% + 20%" overstates total savings; the worked example above shows the actual compounded effect is smaller than the arithmetic sum but still substantial.

Decision checklist for sequencing your own optimization

— Have you identified the single largest cost line item before optimizing anything? — Are quick wins (caching, output caps, obvious routing) implemented before structural work? — Is there a re-evaluation cadence (quarterly) built in, since rates and models change? — Are you tracking $/1M blended rate over time, not just total spend, to isolate real savings from volume changes?

What this playbook does not include

This guide sequences cost-reduction tactics; it does not cover how to instrument per-feature usage tracking or pull invoice data from a provider account, which are prerequisites for the audit stage but require separate engineering work outside a token-volume modelling tool.

Frequently asked questions

Where do I start with AI cost optimization?

Start by pricing your top three workloads. Most teams find that one workload drives the majority of spend, so a single model swap or prompt trim there beats a dozen small changes elsewhere.

How much can optimization realistically save?

It depends entirely on your current setup. Teams running a frontier model on a task a cheaper model handles well typically model the largest deltas; teams already on small models see less. Model the change before you promise a number.

Does TokenAtlas apply these optimizations automatically?

No. TokenAtlas is a modelling layer: it prices each option so you can decide. Implementing caching, routing or prompt changes happens in your own application.