LLM Cost Audit
We audit every layer of your inference stack — model selection, routing, caching, prompt structure — and rank the optimizations by projected savings. Fixed fee. Written report. Fast.
What happens after you submit specs
1. Context
We inspect the system, constraints, and where delivery or architecture risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory track, scoped build, or a clear signal that the work is not ready yet.
3. Next Step
If there is a fit, we define the shortest path to a useful engagement and a production-ready outcome.
Your LLM bill is a cost problem. It’s also a fixable one.
Built on GPT-4 in 2024. Seeing $20K-$100K+ annual bills 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 — a $0.015/1K token model doing work a $0.0002/1K token model handles equally well
- No caching layer — 40-70% of production calls hit identical or near-identical inputs
- No routing logic — prompt complexity isn’t classified before hitting a model
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 count per request vs. information density? |
| Caching | Is semantic caching in place? What percentage of 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 monthly cost estimate by call type
- Ranked optimization opportunities with projected savings per item
- Complexity and implementation effort for each optimization
- Recommended implementation order
"60% LLM cost reduction through model routing and semantic caching."
Best Fit
- CTO, VP Engineering, or Head of AI with more than $5K/month LLM API spend
- LLM bills growing faster than revenue
- Budget review or board question surfaced the problem
- Internal engineers do not have a clear 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.
Not a Fit
- Current LLM API spend is under $2K/month and the audit ROI would be marginal
- 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 |
|---|---|
| Tier 1 — LLM Cost Audit: $3,000-$6,000 | 3-5 business days. Fixed fee. Written report delivered. If we find less than $20K in annual savings potential, we refund the difference. |
| Tier 2 — Cost Optimization Sprint: $12,000-$25,000 | Requires audit first. Implements top-ranked items: model router, semantic caching layer, prompt compression, short-circuit logic, before/after metrics. |
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
Submit system context, constraints, and delivery pressure. A Principal Engineer reviews every submission and recommends the right next step.
1. Context
We review the system, constraints, and where risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory, sprint, or pause.
3. Next Step
If there is a fit, we define the shortest useful engagement.
No SDRs. A Principal Engineer reviews every submission.