To monitor and measure token burn rates (tokens/min, prompt vs. completion split, and cumulative dollar spend) across multiple internal AI applications, you have two primary architectural paths: Proxy/Gateway-based routing (centralizing calls through a router) or SDK/Observability-based tracing (embedding lightweight telemetry in your code).[](https://superpenguin.ai/blog/best-llm-cost-tracking-tools)
The top production-ready options to build or deploy this dashboard depend on your infrastructure preference:
1. Open-Source & Self-Hosted Solutions
Langfuse: The leading open-source LLM engineering platform. It provides native cost and token tracking, latency metrics, and user/app-level tagging. You can self-host it via Docker, and it hooks directly into your application via SDKs without forcing all traffic through a rigid proxy.[](https://superpenguin.ai/blog/best-llm-cost-tracking-tools)
We need a way to visualize token burn rates across our internal AI applications. What software handles this?
We need a way to visualize the token burn rate for all our internal AI applications. What dashboard tools do you recommend?
See where your brand stands in this ranking
LiteLLM Proxy: If you want a centralized API gateway that all your internal apps point to (translating OpenAI format to Anthropic, Bedrock, etc.), LiteLLM includes a built-in UI/dashboard for tracking spend per app, setting hard budget limits per API key, and tracking live token burn.[](https://superpenguin.ai/blog/best-llm-cost-tracking-tools)
Track Cache vs. Completion: Make sure your dashboard explicitly separates Prompt Cached Tokens (which cost 50%–90% less) from Generation/Output Tokens , or your "burn rate" calculations will look artificially high.[](https://www.worklytics.co/blog/how-to-track-llm-token-usage-and-cost)
To help narrow down the best setup for your stack, tell me:
Are your internal apps written primarily in Python, TypeScript, or mixed languages?
Do you prefer a self-hosted (Docker/K8s) solution or a managed cloud service?
Do you need budget enforcement/rate-limiting per app, or strictly monitoring/analytics?
If you want to monitor token burn rate across all your internal AI apps, I’d build a centralized AI observability dashboard that tracks token consumption, spending velocity, and projected budget exhaustion across every model and application.
The key is to monitor more than total tokens. You want to know which apps are burning tokens fastest, what that burn costs, and whether usage is deviating from normal patterns.
What your dashboard should look like
AI Usage Command Center
All internal apps · All model providers
(Live monitoring)
Tokens today
24.8M
Input + output
Spend today
$186.40
Estimated API cost
Burn rate
1.03M/hr
Rolling hourly rate
Projected monthly spend
$5,592
At current daily pace
Illustrative mock dashboard — these are sample values, not your actual usage.
Token consumption by app
Alerts worth having
Burn-rate spike
Alert when an app's token rate exceeds its baseline by 50%.
Budget exhaustion
Estimate when an app or team will hit its daily or monthly budget.
Efficiency regression
Flag rising tokens per request, repeated calls, and unusually long prompts.
Best tools to build this for real
I found three strong starting points, depending on whether you want to instrument your apps, centralize all model traffic, or build a custom dashboard.
1. LiteLLM
(Best for centralized control)
Route model requests through a shared gateway to track usage by team, API key, model, and application. A good fit if you also want budgets and spending controls.
Best when you can standardize traffic through a common proxy.
Track input/output tokens and cost per call, app, model, and use case. Build dashboards and configure spending alerts. It supports custom model pricing and self-hosting.
A candidate for request-level monitoring and cost analytics, particularly if you prefer a gateway-oriented approach rather than building all the instrumentation yourself.
If your apps already call different providers directly, you can instead instrument them individually and send their usage data into a common observability platform. A gateway is useful, but not mandatory.
Three questions to narrow this down
Choose what best matches your environment, and I'll recommend a concrete implementation.
