Commercial guide - Last reviewed 2026-08-17
The AI Audit Checklist: Where AI Bills Actually Leak
A practical AI audit checklist from real audits: the questions that find spend leaks in agent loops, retries, routing, serving, and infrastructure — before you buy more capacity.
Direct answer for AI audit
The short answer
An AI audit is a structured review of what an AI system costs per completed task and where that cost leaks: model routing, retries, agent loops, retrieval overhead, serving utilization, and infrastructure placement. A useful audit produces a written leak map ranked by savings against effort — one of ours cut a production bill from $47K to $28K per month. The checklist below is the same set of questions we ask, published so you can run the first pass yourself.
Cost per completed task: can you state what one unit of user value costs? If not, that is finding #1 — instrument before optimizing.
Model routing: is every workload step on the frontier model, including steps a mid-tier model passes evals on?
Retries and loops: what is the retry rate, and do agent loops have budgets and stop conditions — or does failure multiply spend silently?
Retrieval overhead: how many tokens does RAG context add per request, and is any of it cached?
Serving utilization: if you run GPUs, what fraction of paid capacity does real batching actually use?
Idle and oversized capacity: what runs at 3am, and which instances were sized for a launch-day peak that never returned?
Egress and data paths: is traffic crossing AZs, regions, or NAT gateways that a same-region design would avoid?
Placement math: has anyone computed the API vs self-hosted break-even for your real volume — in either direction?
Comparison table
| Factor | Self-audit with this checklist | Written audit by NavyaAI |
|---|---|---|
| What you get | A first-pass leak map from your own billing and logs. | A written leak map with findings ranked by savings vs effort, read personally by the founder. |
| Cost | Your time. | Free for teams spending $20K+/month; no call required. |
| Best for | Teams under $20K/month, or validating that an audit is worth requesting. | Production spend where a 42%-class finding pays for years of attention. |
| Proof standard | Depends on your instrumentation. | Same methodology as the published $47K→$28K audit, methods and data public. |
Frequently asked questions
What does an AI audit include?
A cost baseline (what the system spends and on what), a leak map across routing, retries, loops, retrieval, serving, and infrastructure, and a ranked fix list — savings against implementation effort. Ours is delivered in writing, so it is useful whether or not you ever engage us.
Is a free AI audit actually free?
Ours is, for teams spending $20K+ per month on inference: you share spend range, provider mix, and workload shape through a short intake, and receive the written leak map with no call required. The business logic is simple — some audited teams become optimization clients, and the audit proves the method either way.
How is an AI cost audit different from a cloud cost review?
A cloud review reads the infrastructure bill. An AI audit reads the workflow: token flows, model choices, retries, agent behavior, retrieval overhead, and serving efficiency — then connects them to the bill. Most AI spend leaks are invisible at the infrastructure line-item level.
How often should AI systems be audited?
At every step-change: traffic growth, new models, new agent features, or a bill that outgrew its explanation. Continuously operated systems fold the audit into monthly cost reporting instead of running it as an event.
References & related
Apply this to your stack
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Share your monthly spend, token volume, model stack, RAG or agent pattern, and latency target. NavyaAI will identify the first cost levers to inspect.
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