LLM Cost Audit
We audit every layer of your inference stack — model selection, routing, caching, prompt structure — and rank optimizations by potential operating impact. Scoped assessment. Written report.
What you get back
- 1. Diagnosis What works, what is blocked, and why.
- 2. Recommendation Audit, advisory, sprint, or pause.
- 3. Scope Next action, boundaries, and timing.
Your LLM bill is a cost problem. It’s also a measurable one.
Built around a frontier model default. Seeing meaningful annual inference spend with no clear path to reduction. Internal engineers have tuned the obvious things. Finance is asking questions.
Typical engagement starts when
- You’re using the same model for every task — frontier-model capacity doing work a smaller model may handle equally well after validation
- No caching layer — repeated or near-repeated production calls are being paid for every time
- Routing logic missing: prompt complexity reaches the model before classification
What We Audit
| Area | What We Assess |
|---|---|
| Model selection | Are you using the right model for each task? Is GPT-4 doing work that GPT-4o-mini or Claude Haiku could handle? |
| Routing logic | Do you have a model router? Are tasks classified by complexity before hitting a model? |
| Prompt efficiency | Are prompts bloated? Token use per request vs. information density? |
| Caching | Is semantic caching in place? Which calls are cache-eligible? |
| Batching | Are API calls batched where possible? |
| Output validation | Are failed outputs re-tried at full cost? Is there short-circuit logic? |
| Contract/commitment | Are you on pay-per-token vs. committed throughput? Is the tier optimal for your volume? |
What you leave with
Written cost analysis report with:
- Current cost pattern by call type
- Ranked optimization opportunities with potential operating impact
- Complexity and implementation effort for each optimization
- Recommended implementation order
"Material LLM cost reduction through model routing and semantic caching, validated against the workload's own quality bar."
Best Fit
- CTO, VP Engineering, or Head of AI with meaningful recurring LLM API spend
- LLM bills growing faster than revenue
- Budget review or board question surfaced the problem
- Internal engineers need a clearer answer on model selection, routing, caching, or prompt structure
The audit focuses on LLM cost optimization through LLM API cost reduction, model routing optimization, caching, and prompt budget enforcement.
Better Routed Elsewhere
- Current LLM API spend is too small for a dedicated audit to justify the effort
- The system is still a prototype with no meaningful usage logs
- The team wants a vendor migration opinion before first measuring call types, routing, caching, and prompt cost
How We Engage
| Engagement | What You Get |
|---|---|
| LLM Cost Audit | Scoped assessment. Written report with call-type analysis, optimization ranking, implementation effort, and potential operating impact. |
| Cost Optimization Sprint | Requires audit first. Implements top-ranked items: model router, semantic caching layer, prompt compression, short-circuit logic, and before/after measurement. |
Related
Also see: Production AI Audit — if inference costs are part of your production problem.
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Discuss your LLM Cost Audit path
Send the system context, constraints, and pressure. A Principal Engineer reviews it and recommends the next step.
No SDRs. A Principal Engineer reviews every submission.