AI Cost & Reliability Audit
In one week, find out what your LLM feature really costs, where it's failing customers, and which fixes save the most per month, each checked so quality holds. Read-only, fixed price.
1 week
From read-only access to a fix list priced in $/month
You see what each successful answer costs you, where the feature fails, and how much each fix saves per month. Every fix is quality-checked, so savings never come from worse answers.
- fixed price
- $5,000
- pays back at
- $420/mo
- deliverables
- 4
Every fix priced in dollars
An estimated monthly saving for each fix, ranked, so you know where to start.
Savings without worse answers
Every fix is checked against your quality bar before it's recommended.
No risk to production
Read-only access. Nothing in your systems changes during the audit.
Who it's for
- Seed to Series B SaaS teams based in the US, EU, UAE, Singapore, Australia, or New Zealand.
- LLM features already live in production
- On AWS (Bedrock) or direct API providers such as OpenAI and Anthropic
- Teams that need a month-long vendor onboarding. The audit runs in one week, so procurement has to clear first.
- Teams where read-only access takes weeks of approvals. The audit needs access on day 1.
- Teams without an LLM feature in production yet. The audit measures live traffic, not plans.
What it's worth to your business
Lower AI spend
Fixes ranked by estimated monthly saving (context trimming, model routing, caching, retries), so you start with the ones that pay most.
Healthier gross margin
A cost model that shows AI cost per customer as usage grows, so pricing and plans keep their margin.
Fewer failures customers see
Where answers fail and what that costs in retries and support time, with every fix checked against your quality bar.
Spend you can forecast
A clear number for what the feature costs today and at 5× or 10× the usage, before the bill arrives.
The payback math: the audit is a fixed $5,000. If the fixes are worth about $420 a month, it pays for itself within a year, and you see every fix's dollar value before deciding anything else.
Deliverables
Audit report
Where the feature's AI spend goes today, and what each successful outcome really costs you.
Fix list ranked by $/month
Each fix with its estimated monthly saving, effort, and proof that quality held, so you start with the ones that pay most.
Golden eval set (you keep it)
Test cases built from your real traffic, so future model and prompt changes can't quietly lower quality.
Cost model
Projects AI spend and cost per customer as usage grows, so you can price plans and protect margin before the bill arrives.
Pilot program and price
Pilot
2 of 3 places leftFree
The full audit, in exchange for an anonymized case study, a short testimonial and a 30-minute feedback call. 3 places.
Apply for a pilot placeRegular
$5,000 fixed
The audit only; implementation is quoted separately. 50% at signing, 50% on delivery of the report. Pays for itself within a year if the fixes save about $420 a month.
Get a free cost checkHow the week runs
- day 1
Measure
Read-only access set up; pull billing exports, provider usage and traces, including failure, retry, latency and fallback rates.
→ Baseline cost per successful outcome and reliability rates
- day 2
Attribute
Map the feature's spend to the 5 cost layers.
→ Spend breakdown by cost layer
- day 3
Find
Identify fixes: context trimming, routing, caching, retries.
→ Candidate fix list with estimated $/month
- day 4
Verify with evals
Build the golden eval set from production traces; run each fix against it and check its latency and fallback rates.
→ Golden eval set + before/after pass rates and latency
- day 5
Rank by $
Rank fixes, build the cost model, write the report; readout call.
→ Audit report, ranked fix list, cost model
Where the money goes: 5 cost layers
Model tokens
Input and output tokens per call, by model. Usually the largest line, and the one most changed by model choice and prompt size.
Retrieval / context
Embeddings, vector store and RAG chunks. More context makes every call cost more, often with no gain in quality.
Infrastructure around the call
Compute, logging, data transfer and queues. Easy to miss because it isn't on the model invoice.
Eval runs
Every eval and regression run costs tokens. Needed, but it should be sized, not left unbounded.
Human review & retries
Failed outputs that get retried or checked by a person. This is where cost per request and cost per successful outcome drift apart.
Sources I work from on AWS: the Cost & Usage Report with IAM principal attribution, and Bedrock invocation logs.
Access & data
Read-only, under NDA. The agreement includes an anonymized case-study clause (industry, scale, relative metrics).
- AWS Cost & Usage Report or Cost Explorer, via a read-only IAM role
- Bedrock invocation logs / CloudWatch
- Read-only usage view on direct providers (OpenAI, Anthropic)
- A redacted sample of production prompts and traces
- A short call with the engineer who owns the feature
What I won't do
- Claim an outcome before measuring your baseline.
- Recommend a cost cut without an eval check.
- Ask for write access during the audit. The audit is read-only.
After the audit: hands-on implementation
Your team can ship the ranked fixes with the golden eval set as the safety net. Or I ship them with you, highest saving first, so the savings reach your bill: 4–8 weeks inside your repo, through your PR review, with every change checked for quality.
Lower spend
Routing, caching and prompt trimming from the ranked list, highest saving first.
Fewer failures
Retrieval, prompts and fallbacks fixed, from the reliability findings.
Quality gate and dashboards
Changes ship only when quality holds, and cost per feature stays visible.
Not included in the $5,000 audit: each package is quoted separately, at a fixed price, from your fix list. Success is measured in dollars against the audit baseline: cost per successful outcome, with quality pass rate and latency held. See the implementation
Questions
How much will we save?
I won't promise a number before measuring your baseline. What you get is a dollar estimate for every fix, checked against your quality bar, so whether to act is a business decision you make with real numbers. The audit pays for itself within a year if the fixes are worth about $420 a month.
How long does it take?
One week.
What access do you need?
Read-only access, under NDA: billing exports or Cost Explorer through a read-only IAM role, Bedrock invocation logs, read-only usage on direct providers, a redacted sample of prompts and traces, and a short call with the feature owner.
Do we keep anything?
Yes: the golden eval set, the cost model and the ranked fix list.
Will quality drop?
Every fix is checked against an eval before it's recommended.
How is it priced and paid?
Pilot: free, 3 places. Regular: $5,000 fixed, 50% upfront and 50% on delivery.
What does the price cover?
A full week of senior, hands-on work on your feature: the baseline, the spend split across the 5 cost layers, and an eval check on every fix before it's recommended. You keep the golden eval set, the cost model and the ranked fix list, so the work keeps paying after the readout. The price is fixed: no hourly billing and no overruns. Implementing the fixes isn't included; that's quoted separately if you want it. Fixes that save about $420 a month cover it within a year.
Do you implement the fixes?
Yes, as a separate engagement: the hands-on implementation, quoted at a fixed price per package from your fix list, on top of the audit. 4–8 weeks inside your repo, working with your team, with every change gated by evals. It's never required; the report, cost model and golden eval set are yours either way.
You're in India. How do calls work?
Calls Mon–Fri, 9 AM–7 PM IST: early morning US East, working hours EU, UAE and Singapore, afternoon ANZ. Between calls you get written async updates.
Can an LLM judge another LLM?
Yes, for some checks, if the judge is itself tested. I calibrate it against human-labelled cases from your golden eval set and use plain code checks wherever the answer can be checked exactly.
Why not a cost dashboard or our cloud console?
Dashboards show spend by service. The audit shows cost per successful outcome for the feature, and checks every fix against your quality bar.
Why not do this ourselves?
You can, and after the audit you'll have the golden eval set and cost model to keep doing it. The audit compresses the first pass into one week.
Which providers do you cover?
AWS (Bedrock) and direct API providers such as OpenAI and Anthropic.
Get a free 30-minute cost check
You leave with the 3 most likely cost or reliability leaks in your LLM feature, a rough idea of what they cost you each month, and a clear yes or no on whether the audit is worth it. No pitch if it's not a fit.
Choose a timeCalendly, with three short intake questions. Or email [email protected]
$5,000 fixed
Pilot free · 3 places