Observability & Evaluation
AI Agent Monitoring Platform
Monitors agent health, failures and drift in production.
Observability & Evaluation
AI Cost Audit
See where your AI model spend goes and get a ranked plan to cut waste without hurting quality.
AI model bills can grow faster than usage or revenue, and the cause is rarely obvious. AI Cost Audit shows where the money goes. We break 30 days of spend down by workflow, model and call type, then check model choice, prompt size, caching, batching and retries. You get a ranked savings plan with the estimated impact, effort and quality risk of each change.
- Spend broken down by workflow, model and call type
- Up to 10 workflows checked for model, prompt and caching waste
- Ranked savings plan with impact, effort and quality-risk notes
$2,575
7–17 working days
What it is
AI Cost Audit is a one-time review of your AI model API spend. Using 30 days of usage data from up to 2 providers, we break costs down by workflow and model, check up to 10 workflows for waste, and give you a ranked savings plan with a quality-risk note for each change.
Why businesses buy it
Most teams see one total on their AI provider bill and little detail behind it. Costs creep up through oversized prompts, premium models used for simple tasks, missing caching and retries that quietly double calls. Finding these takes time and log access that most teams never set aside. This audit does that work using your real usage exports and request logs. Each saving is estimated against those logs and paired with a note on quality risk. That way you do not cut cost at the expense of results. Estimates are estimates, not promises, and the plan is yours to act on.
Problems this solves
Your AI model bill grows faster than usage or revenue
We break 30 days of spend down by workflow, model and call type. You see which parts of your product drive the bill, instead of one total.
Premium models may be handling tasks a cheaper model could do
Up to 10 workflows are checked for model choice, prompt size and context use. The savings plan shows where a different model could fit, with a quality-risk note.
Retries and repeated calls quietly multiply your costs
The audit checks caching, batching and retries in each workflow. Estimates are tested against your real request logs, so each saving reflects how your product actually behaves.
Your team does not know which change to make first
A ranked savings plan lists every change with its estimated monthly impact and effort. A written walkthrough explains the order of work.
A good fit if
Your monthly bill from AI model APIs keeps growing, and nobody can explain why.
You use the OpenAI API, Google Gemini, Azure OpenAI or AWS Bedrock in a live product or internal workflow.
You have at least 30 days of real usage history and request logs.
Your team can make the changes once it knows which ones matter most.
Not the right fit if
You want a live dashboard that tracks AI spend every day. See AI Cost Monitoring.
You want calls routed to cheaper models automatically. See AI Model Router.
You want one controlled gateway for all model traffic, with limits and logging. See AI Gateway.
You only have a prototype with no real usage history yet. See AI Proof-of-Concept Sprint.
How it works
- You share 30 days of usage exports from up to two model providers and read-only access to request logs or your AI gateway.
- We break spend down by workflow, model and call type, so you can see which parts of your product drive the bill.
- Up to 10 workflows or prompt chains are checked for model choice, prompt size, caching, batching, retries and context use.
- Each possible saving is estimated against your real request logs and given an effort rating and a quality-risk note.
- You receive the ranked savings plan with a written walkthrough that explains the order of work.
- For 14 days afterwards your team can send follow-up questions while making changes.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- 30 days of usage exports from each model provider you use, up to two.
- Read-only access to request logs or your AI gateway, such as Helicone or Langfuse, by invite or a key you control.
- A technical contact who knows the workflows and answers our follow-up questions in writing.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
Order placed. Usage exports, log access and your technical contact's details are due within 72 hours.
Week 1
Usage data loaded and spend broken down by workflow, model and call type.
Week 1
Up to 10 workflows checked for model choice, prompt size, caching, batching and retries.
Week 1
Savings estimated against your request logs, ranked and given quality-risk notes.
Go-live
Savings plan delivered with its written walkthrough.
After delivery
14 days of follow-up questions while your team makes changes.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 7–17 working days | $2,575 |
What you receive at handover
- A spend breakdown by workflow, model and call type for 30 days
- A review of up to 10 workflows or prompt chains (recommendations; fixes are not included)
- A ranked savings plan with estimated monthly impact and effort
- A quality-risk note for each proposed change
- A written walkthrough of the findings and 14 days of follow-up questions
Expected benefits and how to measure them
- Helps you see which workflows drive AI spend
- Can reduce model costs where waste is found
- Helps you cut cost without losing answer quality
- Monthly AI API spend
- Compare provider invoices for the month before changes with each month after.
- Cost per request
- Divide spend by request count from your logs or gateway, before and after each change.
- Answer quality
- Re-run a fixed set of test prompts before and after each change and compare the results.
Before you start: Save the provider invoices and request counts for the audit's 30 days. Note any quality checks you already run on AI outputs.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
There are no running costs. The audit uses read-only access to data you already have, so nothing is installed and no new tools are needed. If you later act on the plan with a tool such as a gateway or router, that is a separate decision.
Ask about running costsCustom requirements
Add a note at checkout if you need something specific. If it fits within the Scope Card it is simply included. If it goes beyond it, you get a written Change Quote within two working days, and that work starts only once you accept it, with its time added to your delivery date.
Request a custom versionKeep it running
This is a one-time audit, so there is nothing to keep running. The spend breakdown and savings plan are yours to act on. If you want changes built for you, AI Model Router and AI Gateway put them into practice, and AI Cost Monitoring tracks spend afterwards. Other changes can be quoted through a Change Quote.
How this compares
| Do it yourself | Hire a traditional agency | Upmark Labs | |
|---|---|---|---|
| Cost | Engineer time pulled away from product work | A consultancy quotes after scoping | One fixed price shown before you buy |
| Breakdown | A single total on the provider bill | Depends on the access given | Spend split by workflow, model and call type |
| Checks | Whatever someone happens to notice | Varies by consultant | Model choice, prompt size, caching, batching, retries and context use |
| Quality | Savings can quietly hurt results | May not be considered | A quality-risk note for every proposed change |
| Output | A list of ideas | A slide deck | A ranked plan with estimated monthly impact and effort |
Questions about this product
How much can an AI cost audit save us?
It depends on how your workflows are built, so we do not promise a figure. The plan estimates the monthly impact of each change, checked against your own request logs. Savings tend to be larger where premium models handle simple tasks or caching is missing. Either way, you see where the money goes and what each change involves.
Which AI providers does the AI Cost Audit cover?
Up to two model providers, such as the OpenAI API, Google Gemini API, Azure OpenAI or AWS Bedrock. If you route traffic through a gateway or observability tool such as Helicone or Langfuse, we can read its logs instead. More than two providers or 10 workflows are quoted through a Change Quote.
Will cutting AI costs make our product worse?
Not if changes are chosen with care, which is why every recommendation has a quality-risk note. Some changes, such as caching repeated calls, carry little risk. Others, such as moving a task to a smaller model, need testing first. The note tells you which is which, so you can decide what to try and how to check it.
Do you make the cost-saving changes after the audit?
No. The audit is advisory, and making the changes is not included. Your team can act on the plan directly. If you want the work done for you, AI Model Router and AI Gateway cover routing and control, and other changes can be quoted through a Change Quote. You get 14 days of follow-up questions either way.
Why does the AI Cost Audit need 30 days of usage data?
Because a shorter window can hide patterns. A month of data usually captures normal weekly cycles, busy periods and occasional spikes from retries or batch jobs. Savings estimates are only as good as the data behind them. If your product is too new to have a month of real usage, this audit is not the right fit yet.
Does the AI Cost Audit look at our cloud hosting bill?
No. The audit covers spend on AI model APIs only. Cloud infrastructure costs such as servers, storage and databases are outside its scope. If those costs worry you too, mention it in your follow-up questions, and we can point you to the right next step or quote it through a Change Quote.
Is our prompt data safe during the AI cost audit?
Access is read-only and limited to what the audit needs. We work from usage exports and request logs, shared by invite or a key you control. Logs can contain prompts, so tell us if any include sensitive content, and we agree together how to handle it. You can remove access once the savings plan is delivered.
Find out where your AI budget goes
Order the AI Cost Audit for a clear breakdown of your model spend and a ranked plan to cut waste, with quality risks noted.
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