Observability & Evaluation
AI Agent Monitoring Platform
Monitors agent health, failures and drift in production.
Observability & Evaluation
AI Cost Monitoring
AI spend is tracked by provider, model and workflow, with alerts before a budget is overrun, not after the invoice arrives.
AI bills tend to arrive as one large number with no clue which feature caused it. AI Cost Monitoring tracks what your AI stack spends by provider, model and workflow, and alerts you when spending approaches a budget you set. Finance and engineering see the same current numbers. The monitor reports; your team decides whether to pause, tune or carry on.
- Spend broken down by provider, model and workflow
- Budget alerts arrive before the overrun, not after
- One shared view of AI spend for finance and engineering
$2,900
20–30 working days
Optional maintenance $249/mo
What it is
AI Cost Monitoring tracks what your AI stack actually spends, broken down by provider, model and workflow, and alerts you before a budget is overrun rather than after the invoice lands. It gives finance and engineering a shared, current view of AI spend across every account you run.
Why businesses buy it
AI costs can climb quietly. A batch job runs twice, a chat feature gets popular, or a prompt grows longer, and the first sign is a surprise invoice. By then the money is spent and nobody knows which change caused it. After this build, spend is attributed to the workflow behind it and budget thresholds trigger alerts early, so you can respond while it still matters. The Scope Card sets the providers, workflows and thresholds in writing before you buy, so the scope is clear from day one.
Problems this solves
AI spend is only discovered when the invoice arrives
Cost Monitoring tracks what your AI stack spends as it happens. Alerts fire before a budget is overrun, so you can act while there is still room to adjust.
Nobody can say which workflow or model is driving cost
Spend is broken down by provider, model and workflow across up to three systems. The admin console or written report shows exactly where the money goes each month.
Finance and engineering argue from different spend numbers
Both teams read the same current view of AI spend across every account in scope. Up to five budget monitors are set from the limits you agree.
A good fit if
You run AI calls across more than one provider and cannot easily see combined spend without checking each billing dashboard separately.
A single workflow or feature has caused a cost surprise before, and you want an alert before it happens again.
You need to attribute AI spend to specific workflows or teams for budgeting, not just a total monthly provider bill.
Finance and engineering both need the same current spend numbers, rather than a spreadsheet someone updates by hand.
Not the right fit if
You need to track raw API call volume and throughput, not dollar spend specifically. See AI Usage Monitoring Platform.
You need org-wide visibility into who uses which AI tool for governance reasons rather than a cost figure. See AI Usage Monitoring, our business-facing tool for that.
You need to know how well your models are performing, not what they cost. See AI Model Performance Dashboard.
You need full tracing of one agent's internal steps rather than a cost figure per workflow. See AI Agent Observability.
How it works
- We connect to the billing and usage data of the providers you run, typically OpenAI, Google Gemini, AWS Bedrock or Azure OpenAI, wherever spend accrues.
- Spend is attributed down to the workflow or feature level, not just a provider total, so you can see which specific AI feature is driving cost.
- Budget thresholds are set with you, per provider, per model or per workflow, matched to how your team actually plans AI spend.
- Cost data is tracked continuously, so a spend spike from a runaway workflow or an unusually expensive model call surfaces the same day it happens.
- Alerts route to Slack or a written report, whichever finance and engineering already use, with enough detail to identify the workflow behind the spend.
- A test report confirms the cost tracking is accurate against your agreed scenarios, alongside an admin guide and a recorded handoff session.
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.
- Your business information (company basics, hours and brand voice), shared during intake, or confirmation we can reuse what you gave us on an earlier order.
- The AI providers and billing accounts whose spend should be tracked.
- Cloud or hosting access for wherever the cost tracking connections run.
- A technical or finance owner who can confirm which budget thresholds matter.
- Written authorization for any testing of the billing and usage connections.
- The list of workflows or features you want spend broken down by.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- One decision-maker who signs off the final thresholds and alert routing.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
business information confirmed and the providers to be tracked agreed.
Weeks 1–2
Billing connections built and budget thresholds agreed with your finance owner.
Week 3
Spend attribution by workflow configured and alert routing set up.
Weeks 3–4
Test report produced by running cost tracking against agreed scenarios.
Week 4
Your review and two rounds of changes to thresholds or routing.
Go-live
Live, recorded handoff session, and the 30 days of support begins.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $2,900 |
| Express | 15–25 working days | $3,400 |
| Rush | 10–20 working days | $3,900 |
What you receive at handover
- AI Cost Monitoring configured and deployed across up to three systems or provider accounts
- Spend broken down by provider, model and workflow in an admin console or written report
- Up to five budget alerts or monitors set from your limits
- A test report with a control test log for the agreed scenarios
- An admin guide and a recorded 60-minute handoff session
- 30 days of post-launch support
Expected benefits and how to measure them
- Can help you avoid surprise AI bills.
- Helps teams find and fix the workflows that cost the most.
- Gives finance and engineering one shared set of numbers.
- Spend against budget
- Compare monthly spend with budget from the console, and count months that stayed within limits.
- Cost per workflow
- Track each workflow's monthly cost and note changes after optimization work.
- Budget alerts acted on
- Count alerts each month and how many led to a change before the invoice.
Before you start: Collect the last three months of AI invoices by provider. Note any budget overruns and how they were found.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build price is a one-time cost. Running the monitoring afterwards costs roughly $20 to $300 a month in your own accounts, mostly hosting and the logging tools that hold the spend data.
AI model usage during build and testing also bills to your own provider accounts, so ownership of every provider relationship and every invoice stays with your team.
Ask about running costsCustom requirements
Need to track spend across more than three connected systems, or more than five budget thresholds? Add a note at checkout for a written Change Quote, while your original order keeps its normal timeline. Larger or bespoke projects use the Custom AI request form.
Request a custom versionKeep it running
Monthly maintenance covers monitoring, fixes, model and platform updates, a monthly health report and monthly hours for changes, which covers a new budget threshold or an added workflow as spend patterns shift. It starts after the 30 days of support and can be cancelled any month.
$249/mo (the minimum fee, includes 2 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A FinOps platform bundles this into a broader subscription | A consultancy bills for a cost audit | Ours is one fixed price, shown on this page before you buy |
| Scope | A FinOps platform tracks whatever cloud spend you wire up yourself | An agency audit's scope depends on how it was billed | Ours is written to the AI providers and workflows you name |
| Delivery | A FinOps platform still needs real configuration before figures are trusted | A cost audit is often a point-in-time snapshot | Ours has a fixed delivery time shown before you buy, with faster options |
| Attribution | A general FinOps tool is not built for per-model AI cost detail | An agency deliverable depends on what was agreed | Ours breaks spend down by provider, model and workflow |
Questions about this product
How much does AI Cost Monitoring cost?
The fixed price for each delivery speed is shown in the price and delivery panel on this page before you order. It covers everything on the Scope Card: spend tracking across up to three providers or systems, up to five budget thresholds, alerts routed to Slack or a written report, and an admin guide with a recorded handoff session.
What does AI Cost Monitoring cost to run each month?
Running the monitoring afterwards costs roughly $20 to $300 a month in your own accounts, mostly hosting and the logging tools that hold the spend data. There is no separate platform license on top of that figure. The exact cost depends on how many providers and workflows you are tracking spend across.
Can AI Cost Monitoring break spend down by workflow, not just by provider?
Yes. Spend is attributed to the specific workflow or feature that generated it, up to five thresholds configured across your stack, not just totalled at the provider level. That matters when one feature, such as a chat agent or a batch job, is driving cost that a provider-level bill alone would not show you.
How is AI Cost Monitoring different from AI Usage Monitoring Platform?
AI Usage Monitoring Platform tracks raw API call volume, rate limits and throughput across your AI stack. AI Cost Monitoring tracks money instead, broken down by provider, model and workflow, with budget alerts. The two answer different questions, call volume and cost, so they can run side by side if you need both views of the same stack.
What happens when AI Cost Monitoring flags a budget threshold?
An alert routes to Slack or a written report identifying the provider, model or workflow behind the spend, so finance or engineering can look into it quickly. The monitor reports the overrun; it does not pause a workflow or cut off a provider account, because that decision stays with your own team.
Can we adjust budget thresholds or track more providers later?
Yes. Adjusting an existing threshold, or refining what counts as a spend spike worth flagging, usually fits inside the monthly change hours in maintenance. Tracking a fourth or fifth provider beyond the three in scope goes through a Change Quote agreed in writing before that extra tracking work starts.
Can we cancel AI Cost Monitoring maintenance later?
Yes. Maintenance bills monthly with no lock-in, so you can cancel any month once the 30 days of included support has ended. If your spend pattern stays steady, you may decide the thresholds stay current without it, and you can start again later if things change.
Catch an AI spend spike before the invoice does.
Order AI Cost Monitoring and spend tracking by provider, model and workflow is live on a fixed delivery date.
Works well with
Observability & Evaluation
AI Model Evaluation Platform
Runs ongoing evaluations across models and prompts with regression alerts.
Observability & Evaluation
AI Model Performance Dashboard
Compares model quality, latency and cost across workflows.