To build an effective internal dashboard for tracking token burn rates across multiple custom or internal AI applications, you need a centralized architecture that aggregates usage telemetry (prompts, completions, caching stats, and dollar costs) from various LLM gateways or direct provider APIs.
Here is a clean, structured blueprint for designing and implementing an internal AI token burn-rate dashboard.
1. High-Level Architecture
To capture burn rates across all internal apps without heavily coupling your application code to a single monitoring vendor, use a gateway-plus-telemetry pattern:
The AI Gateway / Proxy Layer: Route all internal app LLM traffic through a reverse proxy or unified gateway (e.g., LiteLLM Proxy , Portkey, Langfuse, or a custom lightweight FastAPI/Envoy wrapper). This logs prompt tokens, completion tokens, and latency natively on every request.
The Ingestion & Time-Series Backend: Point your gateway or OpenTelemetry exporter to a time-series database. Standard choices include Prometheus (for real-time scraping/alerting) paired with (for visualization), or an LLM-native observability platform like , , or .[]()
3. Implementation Blueprint (The OpenTelemetry / Grafana Route)
If you want to spin this up quickly using open-source infrastructure:
Instrument via OpenTelemetry: Ensure your internal apps or proxy emit standard gen_ai.* semantic convention traces. Alternatively, tools like tokviz can capture local OpenTelemetry spans instantly.[](https://github.com/waldekmastykarz/tokviz)
Configure Grafana Panels:
Panel A (Stat) : Current aggregate Cost Rate ($ / hour).
Panel B (Time Series) : TPM (Tokens Per Minute) grouped by app_name tag over the last 6 hours.
Panel C (Bar Chart) : Total daily spend grouped by model_id.
Set Up Threshold Alerts:
Configure webhook alerts in Grafana/Prometheus to page your engineering channel if an individual app's burn rate exceeds $X/hour or spikes >300% over its historical moving average.
If you want to monitor token burn rate across all your internal AI apps, I’d build a centralized AI observability and cost dashboard rather than rely on each model provider’s usage console.
The goal is to see, in one place, which apps are consuming tokens, how quickly costs are accumulating, which models are driving usage, and when an app is likely to exceed its budget.
What your dashboard should show
AI Usage & Cost Monitor
(Illustrative design)
Tokens / hour
1.28M
Input + output tokens
Estimated daily spend
$384
Illustrative estimate
Active AI apps
12
Budget alerts
2
Apps near their limits
Token throughput
genui{"charts_widget_v2":{"content":{"chartType":"line","meta":{"title":"Tokens consumed per hour","description":"Illustrative data showing usage spikes over a workday."},"xKey":"time","series":[{"dataKey":"input","label":"Input tokens","valueFormat":"compact"},{"dataKey":"output","label":"Output tokens","valueFormat":"compact"}],"yAxisMin":0,"data":[{"time":"08:00","input":420000,"output":110000},{"time":"09:00","input":510000,"output":145000},{"time":"10:00","input":760000,"output":190000},{"time":"11:00","input":610000,"output":165000},{"time":"12:00","input":840000,"output":240000},{"time":"13:00","input":680000,"output":180000},{"time":"14:00","input":920000,"output":260000}]}}}
All figures and chart points above are mock data, not readings from your infrastructure.
Best tools to build it
These are the strongest starting points, depending on whether you want an off-the-shelf dashboard or a centralized monitoring layer.
1. Langfuse — best for AI application observability
Track token usage and cost across models, applications, users, and time; create dashboards and threshold alerts. A good choice when you need detailed traces to investigate unexpected consumption.
Route multiple AI applications through a common proxy and track usage and spend by endpoint. Its observability integrations can export token, cost, and latency telemetry. Especially useful if you can standardize how your apps call models.
My default recommendation: Langfuse for visibility, with LiteLLM as an optional gateway if you want to standardize traffic across providers. If your organization already runs Datadog, evaluate its existing observability stack before introducing another platform.
Metrics I would include
Metric
Why it matters
Tokens per second / minute
Detect sudden usage spikes
Input vs. output tokens
Separate prompt overhead from generation
Tokens by app, team, model
Identify the source of consumption
Spend per hour and projected daily spend
Forecast costs before the bill arrives
Cache-read tokens and cache hit rate
Identify caching opportunities
Tokens per request and per successful task
Detect inefficient prompts or agent loops
One important design choice: track actual token throughput separately from spend. Token burn rate can mean tokens consumed per second, or the rate at which your budget is being depleted. A useful dashboard should show both.
Recommended architecture
Your internal AI apps
Agents · RAG · Chatbots · Internal copilots
Shared telemetry layer
Provider usage data + app ID + model + timestamp
OpenTelemetry / ingestion pipeline
Normalize events and aggregate token rates
Live dashboard
Tokens/sec · cost · trends
Alerts
Budgets · anomalies · spikes
OpenTelemetry provides common GenAI telemetry conventions, including token usage metrics that can be grouped by provider and model.
For reliable cross-app monitoring, ensure every event has a consistent app_id, environment, provider, model, input_tokens, output_tokens, and timestamp. Avoid capturing sensitive prompts unless you explicitly need them.
Let's narrow down the implementation
I can help you choose the stack and map out a working dashboard. The most important question is:
