Observability & Evaluation
AI Agent Monitoring Platform
Monitors agent health, failures and drift in production.
Observability & Evaluation
AI Usage Monitoring Platform
API call volumes, rate limits and throughput across your AI stack are tracked live for engineering and ops, not governance.
When an AI feature slows down or starts failing, engineers often find out from users, not from their own dashboards. AI Usage Monitoring Platform tracks call volumes, token throughput, rate limits and error rates across every model and provider you run. Patterns that need attention go to Slack, Grafana or a report. Your team decides whether to throttle, scale or call a provider.
- Call volume and throughput tracked per provider and model
- Warnings before traffic hits a provider's rate limit
- Rising error rates flagged where engineers already look
$2,900
20–30 working days
Optional maintenance $249/mo
What it is
AI Usage Monitoring Platform tracks the technical usage of your AI stack: API call volumes, rate limits, token throughput and error rates across every model and provider you run. It is built for engineering and ops teams watching infrastructure load, not for org-wide governance reporting.
Why businesses buy it
AI providers enforce rate limits and have bad days, and both show up as errors in your product. Without monitoring, engineers piece together what happened from scattered provider consoles after customers complain. After this build, the load on your AI stack is tracked in one place, with alerts when traffic nears a limit or errors climb, so your team can act before users notice. The Scope Card sets the systems, thresholds and alert routes in writing before you order, so you know exactly what will be watched.
Problems this solves
Engineers only learn about rate limits when requests start failing
The platform tracks API call volumes and rate limits for every model and provider you run. Up to five monitors warn your team as usage approaches a limit.
Token throughput and error rates sit in separate provider dashboards
Volumes, token throughput and error rates come together in one admin console or written report. Engineers stop flipping between provider dashboards to understand load or trace an error.
Capacity planning for AI workloads is guesswork
A running record of usage across up to three systems shows how load changes over time. That gives ops teams real numbers for planning, rather than estimates.
A good fit if
Your engineering team needs to see API call volume and throughput per model, endpoint or provider, not per person or department.
You are approaching a rate limit or quota with a provider and need live visibility before requests start failing.
You run AI calls through more than one provider and need a single view of throughput across all of them.
You want infrastructure-level usage data, such as call volume spikes or latency under load, routed to engineering rather than compliance.
Not the right fit if
You need org-wide visibility into who is using which AI tool and how often, for governance or licensing purposes. See AI Usage Monitoring, our business-facing tool for that.
You need to track AI spend and cost specifically, not raw call volume or throughput. See AI Cost Monitoring.
You need step-by-step tracing of what one agent did internally, not aggregate stack-wide call volume. See AI Agent Observability.
You need ongoing evaluation of model output quality, not usage volume. See AI Evaluation System.
How it works
- We connect to the AI providers and infrastructure in scope, typically OpenAI, Google Gemini, AWS Bedrock or Azure OpenAI, wherever your stack actually makes API calls.
- Call volume, token throughput, latency and error rate are tracked continuously per model, endpoint and provider, at the infrastructure level rather than by named user.
- Rate limit and quota thresholds are set with your technical owner, so you see usage climbing toward a provider limit before requests start failing in production.
- Patterns that matter to engineering are surfaced: a sudden spike in call volume, a rising error rate, or throughput drifting outside its normal range.
- Findings route to Slack, Grafana or a written report, wherever your engineering or ops team already watches infrastructure signals day to day.
- A test report confirms the tracking works against your agreed scenarios before handoff, alongside an admin guide covering every threshold and connection.
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 API accounts whose call volume and throughput should be tracked.
- Cloud or hosting access for wherever the tracking connections run.
- A technical owner who can confirm which rate limits and thresholds matter most.
- Written authorization for any testing of the tracking connections.
- The list of AI applications and systems in scope for monitoring.
- 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 report 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 systems to be tracked agreed with your technical owner.
Weeks 1–2
Tracking connections built and rate limit or quota thresholds agreed.
Week 3
Pattern detection configured and Slack, Grafana or report routing set up.
Weeks 3–4
Test report produced by running 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 Usage Monitoring Platform configured and deployed across up to three systems or AI applications
- Tracking of API call volumes, rate limits, token throughput and error rates per model and provider
- Up to five monitors and an admin console or written report for engineering and ops
- 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 give engineers early warning before limits are hit.
- Helps ops teams plan capacity from real usage data.
- Makes error spikes easier to spot and trace to a provider.
- Rate-limit incidents
- Count incidents per month from your incident log, comparing the quarter before launch with the quarter after.
- Error rate by provider
- Read it from the platform each week and note changes after fixes or provider updates.
- Warnings raised before a limit was hit
- Count monitor warnings that came before an incident, versus incidents with no warning.
Before you start: Gather last month's call volumes and error figures from each provider. List rate-limit incidents from the past quarter.
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 platform afterwards costs roughly $20 to $300 a month in your own accounts, mostly hosting and the logging or monitoring tools, such as Grafana, that hold the throughput data.
AI model usage during build and testing also bills to your own provider accounts, so ownership of every provider relationship stays with your team.
Ask about running costsCustom requirements
Need to track usage across more than three connected systems, or more than five 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, updates, a monthly health report and change hours for a new threshold or an added provider as call volume shifts. 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 | An APM or observability tool bundles this into a broader subscription | A consultancy bills for an infrastructure review | Ours is one fixed price, shown on this page before you buy |
| Scope | An APM tool tracks whatever you wire up yourself | An agency review's scope depends on how it was billed | Ours is written to the AI stack and providers you name |
| Delivery | An APM tool still needs real setup before data is meaningful | An infrastructure review is often a point-in-time snapshot | Ours has a fixed delivery time shown before you buy, with faster options |
| Audience | A general observability tool is not built for AI call patterns specifically | An agency deliverable depends on what was agreed | Ours is built for API call volume, rate limits and throughput |
Questions about this product
How much does AI Usage Monitoring Platform cost?
The fixed price for each delivery speed is shown in the price and delivery panel on this page, so you see it before you order. It covers everything on the Scope Card: tracking across up to three connected systems or AI applications, up to five thresholds or monitors, alert routing to your tools and an admin guide with a recorded handoff session.
Is AI Usage Monitoring Platform the same as AI Usage Monitoring?
No, they track different things. AI Usage Monitoring is the business-facing tool: it tracks who across your organization uses which AI tool and how often, for governance and licensing decisions. AI Usage Monitoring Platform tracks infrastructure signals instead, such as API call volume, rate limits and throughput across your technical AI stack, for engineering and ops teams.
What does AI Usage Monitoring Platform cost to run each month?
Running the tracking afterwards costs roughly $20 to $300 a month in your own accounts, mostly hosting and the logging or monitoring tools, such as Grafana, that hold the throughput data. There is no separate platform license on top of that figure. The exact cost depends on how many providers and systems you track.
Does AI Usage Monitoring Platform track more than one AI provider?
Yes. It connects to whichever providers your stack calls, typically a mix of OpenAI, Google Gemini, AWS Bedrock or Azure OpenAI, and tracks call volume and throughput per provider and per model. That matters most when different agents or features in your stack lean on different providers under different rate limits.
What happens when AI Usage Monitoring Platform flags a usage pattern?
A spike in call volume, a rising error rate, or throughput approaching a rate limit routes to Slack, Grafana or a written report, wherever your engineering team already watches infrastructure signals. The platform surfaces the pattern; deciding whether to throttle, scale or contact a provider stays a decision for your own engineering team.
Can we track more systems or adjust thresholds later?
Yes. Adjusting a rate limit threshold, or refining what counts as a pattern worth flagging, usually fits inside the monthly change hours in maintenance. Tracking a fourth or fifth connected system beyond the three in scope goes through a Change Quote agreed in writing before that extra tracking work starts.
Can we cancel AI Usage Monitoring Platform maintenance?
Yes. Maintenance runs month to month with no lock-in, so you can cancel any month once the 30 days of included support has ended. If your call volume stays steady, you may not need it every month. While active, its change hours cover a new threshold or an added provider as call volume shifts.
Who owns the throughput and usage data AI Usage Monitoring Platform collects?
You do. Usage records live inside your own connected systems, such as the logging and monitoring tools you already hold accounts with, not a platform we control. We build the tracking and hand it over with an admin guide and a recorded session, and every threshold, report and connection stays fully visible to your team.
See your AI stack's real call volume before a rate limit does.
Order AI Usage Monitoring Platform and infrastructure-level usage tracking across your named providers is live on a fixed delivery date.
Works well with
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