Fixed price / Audit your token spend
Audit your token spend
Find out where your AI spend actually goes — and get it under control — without ripping out what you've built.
What you get
I install observability into your AI agents and hand you a report on exactly where your tokens go — plus specific, costed changes to cut spend and put a ceiling on it.
- Token and cost tracing wired into your existing agents
- Dashboard of spend by feature, model, and call path
- Ranked list of changes to cut cost, each with an estimated saving
- Spend limits and alerts configured so cost can't run away
- Tooling stays in your accounts and repositories
Why this works for a small business
Your provider’s invoice is not an explanation
A monthly total tells you that AI is getting expensive, not why. I wire tracing into your agents so spend is broken down by feature, by model, and by call path — the difference between “we spent $6k on tokens” and “one retry loop in the intake flow spent $2k of it.”
Control matters as much as savings
Uncapped AI spend is a budgeting risk even when the bill is small today. Part of this package is configuring hard spend limits and alerts, so a bad deploy or a traffic spike can’t quietly turn into a five-figure surprise.
You own the observability, not just a report
The tracing, dashboards, and limits live in your accounts and your repositories. If you never hire me again, you keep a permanent view into what your agents cost — and the report’s ranked list of fixes stays just as actionable.
Questions, answered
How is this different from checking my provider's billing dashboard?
Provider billing tells you the total, not which feature, prompt, or retry loop is driving it. This traces spend down to the individual call path, so you can see the three or four things actually costing you money and fix them.
Do you have to change our agent code?
Mostly it's instrumentation wrapped around what you already have — minimal code changes, all in your own repositories, and you keep every line. Nothing gets rebuilt.
What kind of savings are realistic?
It varies, but common wins are oversized context windows, calls running on a more expensive model than they need, runaway retries, and missing response caching. The report estimates the saving on each so you can decide what's worth doing.
Which stacks do you support?
Anything talking to an API-based LLM — the major hosted providers, cloud gateways, or a self-hosted model. It works whether you're on a framework like LangChain or LlamaIndex or a hand-rolled agent loop.
What happens after the audit?
You own the observability setup and the report. Your team can implement the changes directly, or I can bid a fixed-price project or roll it into a fractional engagement — no obligation to continue.
Ready to start?
Audit your token spend, fixed price
Find out where your AI spend actually goes — and get it under control — without ripping out what you've built.