LLM Cost Audit
We audit every layer of your inference stack: model selection, routing, caching, prompt structure, and optimizations ranked by potential operating impact. Scoped assessment. Written report.
What happens next
- 1. Context We review the situation and constraints.
- 2. Fit We recommend an appropriate next step.
- 3. Scope If relevant, we discuss scope.
Your LLM Bill Is An Operating Signal
Built around a frontier-model default. Seeing recurring inference spend with no clear explanation by workflow, customer, feature, or call type. 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 is doing work a smaller validated model may handle after testing
- 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, or is frontier-model capacity handling work that could move to a smaller validated model? |
| 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
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 model routing, caching, prompt budget enforcement, and call-type measurement.
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 Readiness Review if inference costs are part of your production problem.
Engineering evidence
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Discuss your LLM Cost Audit path
Tell us about your system, the decision ahead, and the constraints. We will review the context and recommend the next step.
Direct contact with a principal engineer.