Observability & Evaluation
AI Agent Monitoring Platform
Monitors agent health, failures and drift in production.
Observability & Evaluation
AI Model Performance Dashboard
Latency, accuracy and error rate across your models are shown live on one dashboard, so a change in performance is visible immediately.
Checking each AI provider's console one by one is slow, and it hides changes until someone complains. AI Model Performance Dashboard brings latency, accuracy and error rate for your models and workflows onto one live view, side by side. A drop in performance is obvious at a glance. You choose the metrics and layout, and the dashboard lives in your own tool.
- Latency, accuracy and error rate on one live view
- Models and providers compared side by side
- Performance drops visible at a glance, not after complaints
$3,500
20–30 working days
Optional maintenance $249/mo
What it is
AI Model Performance Dashboard visualizes latency, accuracy and error rate across the models and workflows you run, live, in one place. It is a visibility and reporting layer built to make performance changes obvious at a glance, not the underlying evaluation logic that decides what counts as accurate.
Why businesses buy it
AI performance changes without warning. A provider update adds latency, a model starts erring more often, or a new version scores worse, and the first sign is usually a user complaint. Piecing the story together from several consoles takes time nobody has. After this build, your team sees the key numbers for every model and workflow in one place, live, so changes are caught and discussed early. The Scope Card sets the systems, metrics and comparisons in writing before you buy, so you know exactly what the dashboard will show.
Problems this solves
Model performance data is spread across logs and provider consoles
The Performance Dashboard shows latency, accuracy and error rate for the models and workflows you run, live, in one place. Nobody has to assemble the picture by hand.
A slow drift in response time goes unnoticed for weeks
Trends are visible at a glance, so performance changes are obvious rather than hidden in raw data. Up to five monitors can flag changes that cross your agreed limits.
Leadership asks how the AI is performing and gets a vague answer
The dashboard gives a clear view across up to three systems or AI applications. It reports results; deciding what counts as accurate stays with your evaluation logic.
A good fit if
You want latency, accuracy and error rate visible on one dashboard, rather than checking each provider's own console separately.
You need to compare how two models or two prompt versions perform side by side, over time, not just once.
Different teams ask you how a model is performing, and you want a shared dashboard rather than answering the question by hand each time.
You already have accuracy or quality scores from another source and need them displayed and tracked over time.
Not the right fit if
You need to define and run the test cases that produce accuracy scores, not just display them. See AI Evaluation System, which builds the underlying benchmarks.
You need ongoing evaluation across multiple models with regression alerts, not a display layer. See AI Model Evaluation Platform.
You need step-by-step tracing of one agent's internal reasoning, not aggregate performance metrics. See AI Agent Observability.
You need to track dollar spend rather than performance metrics. See AI Cost Monitoring.
How it works
- We connect to the models and workflows in scope, typically running on OpenAI, Google Gemini, AWS Bedrock or Azure OpenAI, wherever performance data originates.
- Latency, accuracy and error rate are captured continuously per model and per workflow, so the dashboard reflects current performance, not a stale snapshot.
- The dashboard is built to your agreed layout, showing the comparisons that matter to your team, such as two models side by side or one model over time.
- Thresholds for what counts as a concerning change are set with you, so a dip in accuracy or a rise in latency stands out rather than blending into the noise.
- The dashboard runs in tools such as Grafana or as an admin console, wherever your team already looks for operational visibility.
- We test the dashboard against your agreed scenarios, produce a test report, and hand over an admin guide plus a recorded session on reading and adjusting it.
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 performance should be shown.
- Cloud or hosting access for wherever the dashboard will run.
- A technical owner who can confirm which metrics and comparisons matter most.
- Written authorization for any testing of the performance data connections.
- Any existing accuracy or quality scores you already have, if applicable.
- 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 dashboard layout and thresholds.
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 models to be shown on the dashboard agreed.
Weeks 1–2
Performance connections built and metric thresholds agreed with your technical owner.
Week 3
Dashboard layout and comparisons configured to your agreed view.
Weeks 3–4
Test report produced by running the dashboard against agreed scenarios.
Week 4
Your review and two rounds of changes to layout or thresholds.
Go-live
Live, recorded handoff session, and the 30 days of support begins.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $3,500 |
| Express | 15–25 working days | $4,100 |
| Rush | 10–20 working days | $4,750 |
What you receive at handover
- AI Model Performance Dashboard configured and deployed across up to three systems or AI applications
- Live views of latency, accuracy and error rate per model and workflow
- Up to five configured monitors for performance changes
- 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 make performance changes obvious as they happen.
- Helps engineers and leadership talk from the same numbers.
- Saves time spent pulling figures from several tools.
- Latency per workflow
- Read it from the dashboard weekly and compare with figures gathered by hand before launch.
- Time to notice a performance drop
- For each drop, compare when it started with when someone acted, before and after launch.
- Time spent preparing performance reports
- Time the last manual report and compare with the effort using the dashboard.
Before you start: Gather current latency and error figures for each workflow, however rough. Note how long the last performance report took to prepare.
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 dashboard afterwards costs roughly $20 to $300 a month in your own accounts, covering hosting plus a dashboard tool such as Grafana.
AI model usage that generates the underlying performance data is also billed directly to your own provider accounts, so ownership of every provider relationship stays with your team.
Ask about running costsCustom requirements
If you need the dashboard to cover more than three connected systems, or more than five metrics or comparisons, add a note at checkout. You get a written Change Quote, and your original order keeps its normal timeline. Larger or bespoke projects use the Custom AI request form instead.
Request a custom versionKeep it running
Optional monthly maintenance covers monitoring, fixes, model and platform updates, a monthly health report and monthly change hours for a new metric or comparison as your model lineup 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 analytics platform bundles this into a broader subscription | A custom dashboard build is quoted after discovery | Ours is one fixed price, shown on this page before you buy |
| Scope | An analytics platform leaves layout and metrics to you | An agency agrees scope during the project | Ours is written to the models and comparisons you name |
| Delivery | An analytics platform still needs real configuration before it is useful | An agency build usually takes several weeks, set per project | Ours has a fixed delivery time shown before you buy, with faster options |
| Focus | A general BI tool is not built for model-specific performance metrics | An agency deliverable depends on what was agreed | Ours is built for latency, accuracy and error rate specifically |
Questions about this product
How much does AI Model Performance Dashboard 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: a live dashboard drawing on up to three connected systems, up to five metrics or comparisons in the layout you agree, and an admin guide with a recorded handoff session.
What does AI Model Performance Dashboard cost to run each month?
Running the dashboard afterwards costs roughly $20 to $300 a month in your own accounts, covering hosting plus a dashboard tool such as Grafana. There is no separate platform license on top of that figure. The exact cost depends on how many models and workflows the dashboard covers.
How is AI Model Performance Dashboard different from AI Evaluation System?
AI Evaluation System builds and runs the actual test cases that produce accuracy scores. AI Model Performance Dashboard displays performance metrics, including accuracy scores from an evaluation, on a live view so they are visible over time. The two fit together well: one measures quality, and the other shows it clearly to the whole team.
Can AI Model Performance Dashboard show more than one AI provider at once?
Yes. It connects to whichever providers your models run on, such as OpenAI, Google Gemini, AWS Bedrock or Azure OpenAI, and displays their latency, accuracy and error rate side by side. That saves your team from checking each provider's console separately and makes it easy to see which model is slipping.
What access does AI Model Performance Dashboard need to my systems?
We need admin access and API keys for every system whose performance data feeds the dashboard, shared by adding our team as a user or creating a key you control, never by email. A technical owner who can confirm which metrics and comparisons matter most speeds up the build, since dashboard layout is one of the main decisions during intake.
Can we add metrics or change the dashboard layout later?
Yes. Adjusting an existing metric or comparison, or restyling part of the layout, usually fits inside the monthly change hours included with maintenance. Adding a genuinely new model or a fourth or fifth connected system beyond the three in scope goes through a Change Quote agreed in writing before that work starts.
Can we cancel AI Model Performance Dashboard maintenance?
Yes. Maintenance has no lock-in, so you can cancel any month once the 30 days of included support has ended. It covers monitoring, fixes, model and platform updates, a monthly health report and monthly change hours to keep the dashboard current.
See model performance without checking five consoles.
Order AI Model Performance Dashboard and one live view of latency, accuracy and error rate is up 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 Evaluation System
Builds test sets and automated evaluations for your AI features.