AI INTEGRATION SPECIALISTS
Token Expenditure Audits

Know Exactly Where Your AI Spend Goes

A forensic breakdown — then a roadmap to cut the bill

Most teams see one number on their AI invoice and no idea what drives it. We trace spend to the feature, the call and the model — then hand you a prioritized, quantified plan to cut it without touching quality.

Per-FeatureCost Attribution
RankedBy Savings Impact
QuantifiedEvery Fix
FixedScope & Price
The Problem

The Invoice Is One Number. The Truth Isn't

A single monthly total tells you nothing about which features pay for themselves and which quietly bleed budget. Without attribution, you can't optimize — you can only guess.

⚠️ Flying blind

  • No idea which feature or endpoint drives the bulk of the bill
  • Input vs output vs cached tokens all blurred into one figure
  • Expensive models used where a cheaper one would do identically
  • Spend growing faster than usage and nobody can say why

✦ What the audit gives you

  • Spend attributed per feature, per endpoint, per model
  • Input / output / cache split so you see the real cost drivers
  • A ranked list of fixes, each with an estimated dollar saving
  • A clear picture of cost-per-outcome, not just cost-per-call
Deliverables

What's in the Audit

A complete, evidence-backed picture of your AI spend — and exactly what to do about it.

🧾

Spend Breakdown

Every dollar mapped to a feature, endpoint and model — with the input/output/cache split that actually explains the bill. The map you've never had.

Per-featureI/O splitCacheModelsEndpoints
🔬

Hotspot Analysis

The handful of calls and prompts responsible for most of the cost, ranked so you fix the biggest leaks first.

🎯

Model Fit Review

Where you pay frontier prices for work a cheaper or cached model handles identically — flagged and quantified.

💡

Savings Roadmap

A prioritized list of changes, each with an estimated dollar impact and an effort rating — quick wins first.

📊

Cost-per-Outcome

What each successful result actually costs — optimization tied to value rather than raw token count.

📁

Readout & Report

A written report plus a live walkthrough with your team — yours to keep, act on, and re-run against later.

The Engagement

How the Audit Runs

A focused engagement with a fixed scope — light on your team's time, heavy on evidence.

1

Connect

Instrument usage data and logs — read-only, scoped to what we need.

2

Attribute

Map every token and dollar to a feature, endpoint and model.

3

Analyze

Surface hotspots, model mismatches and cache opportunities.

4

Quantify

Estimate the dollar saving and effort for each recommended fix.

5

Deliver

Report plus a live readout — and an optional hand to implement.

Scope

What We Examine

Provider-agnostic — we audit whatever you're running, however it's wired together.

API logsInputOutputCachedClaudeRoutingRAGAgentsCachingCompressionDashboardsRoadmap API logsInputOutputCachedClaudeRoutingRAGAgentsCachingCompressionDashboardsRoadmap

Usage Data

API logsBilling exportsTraces

Token Mix

InputOutputCachedReasoning

Models

ClaudeOpen modelsRouting

Pipelines

RAGAgentsBatch jobs

Levers

CachingCompressionRight-sizing

Output

ReportDashboardsRoadmap
🔒

Read-Only, Confidential, Yours

The audit runs on read-only access to usage data and logs — we don't touch production. Everything we find is confidential and the full report is yours to keep, share internally, and re-run against after the fixes land. If we can't find savings worth more than the audit, we'll tell you that too.

Find Out What You're Really Spending

One fixed-scope engagement turns a mystery invoice into a map — and a prioritized plan to shrink it without losing a thing.