Does this MCP track costs for every single AI model I use?

Yes. This MCP provides deep visibility into how AI model resources are consumed across your product, allowing you to calculate cost attribution per feature.

What is the difference between usage concentration and cost breakdown?

Usage concentration tells you which features are the primary drivers of overall AI model calls. The cost breakdown gives you the exact monetary expenditure for each individual feature.

Can I use this with my existing AI stack?

Since this MCP is hosted on Vinkius, you connect your preferred AI client—like Claude, Cursor, or Windsurf—once, and you get access to all the analytics capabilities.

Is this only for cost tracking, or can it help with efficiency?

It does both. You can find cost-saving opportunities by identifying optimization targets, or you can evaluate model selection effectiveness using the routing efficiency score.

How does this capability help reduce AI costs?

By using identify_optimization_targets, you can find features where high costs don't match user engagement, allowing you to switch to more efficient models.

Can I see which features are using the most models?

Yes, the analyze_usage_concentration capability identifies which features are the primary drivers of AI model consumption.

How is routing efficiency measured?

The get_routing_efficiency_score capability compares the actual cost incurred by a feature against the theoretical minimum cost of using the most efficient model for that task.

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Connect AI Model Usage Analytics to ChatGPT and Claude

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Know exactly where every dollar of compute goes.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 4 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through AI Model Usage Analytics.

01
Identify optimization targets
To find features where costs are high but user engagement is low, or where model selection appears inefficient
02
Get feature cost breakdown
To determine the exact monetary expenditure for each individual product feature
03
Get routing efficiency score
To evaluate how effectively the system is selecting models for specific feature tasks
04
Analyze usage concentration
To identify which features are the primary drivers of AI model consumption

Real work

What teams actually run

Budget Overruns

Your AI costs spiked last month. You use this MCP to run a usage concentration analysis and find the exact feature responsible for the spike.

Feature Prioritization

You need to decide which features to build next. You check the cost breakdown to see which features offer the best usage return for the cost.

Model Upgrade Review

You suspect you're paying too much for a simple task. You run the routing efficiency score to confirm if a smaller, cheaper model will suffice.

Cost Reduction Initiative

The CFO wants to cut spending. You use the optimization targets capability to find high-cost, low-engagement features for immediate review.

One connector, every AI

AI Model Usage Analytics works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put AI Model Usage Analytics to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

895ms average. Fast in production.

AI Model Usage Analytics is checked daily against the live service.

Daily averagePeak 1043ms

Oct 3Today

Fastest day: 656ms
Slowest day: 1043ms
14-day trend: Stable-3%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 4 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of AI Model Usage Analytics, so you can see the experience inside your AI.

It does not authenticate your account with AI Model Usage Analytics. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

AI Model Usage Analytics Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_1tbd24ReYBC3SDpWJ579WdByT3lsZc0AwncKcSyG/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — AI Model Usage Analytics capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "ai-model-usage-analytics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_1tbd24ReYBC3SDpWJ579WdByT3lsZc0AwncKcSyG/mcp"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up AI Model Usage Analytics in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Model Usage Analytics owners hand off.

This MCP is essential for anyone managing a product that uses AI models. If you need to justify AI spending, track costs by feature, or find ways to cut compute expenses, this is for you. It gives you the data to talk to engineering and finance.

01
Product Manager
Uses this to understand which features are driving the most usage and cost, helping prioritize development.
02
Engineering Lead
Uses this to pinpoint inefficient model usage and identify technical areas that need optimization.
03
FinOps Analyst
Uses this to calculate precise cost attribution per feature, making budget reports accurate.

FAQ

Questions AI Model Usage Analytics owners ask.

01
Does this MCP track costs for every single AI model I use?
Yes. This MCP provides deep visibility into how AI model resources are consumed across your product, allowing you to calculate cost attribution per feature.
02
What is the difference between usage concentration and cost breakdown?
Usage concentration tells you which features are the primary drivers of overall AI model calls. The cost breakdown gives you the exact monetary expenditure for each individual feature.
03
Can I use this with my existing AI stack?
Since this MCP is hosted on Vinkius, you connect your preferred AI client—like Claude, Cursor, or Windsurf—once, and you get access to all the analytics capabilities.
04
Is this only for cost tracking, or can it help with efficiency?
It does both. You can find cost-saving opportunities by identifying optimization targets, or you can evaluate model selection effectiveness using the routing efficiency score.
05
How does this capability help reduce AI costs?
By using identify_optimization_targets, you can find features where high costs don't match user engagement, allowing you to switch to more efficient models.
06
Can I see which features are using the most models?
Yes, the analyze_usage_concentration capability identifies which features are the primary drivers of AI model consumption.
07
How is routing efficiency measured?
The get_routing_efficiency_score capability compares the actual cost incurred by a feature against the theoretical minimum cost of using the most efficient model for that task.

Explore

More in Analytics

Connect AI Model Usage Analytics to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-04

Identify optimization targets

Get feature cost breakdown

Get routing efficiency score

Analyze usage concentration

What teams actually run

Budget Overruns

Feature Prioritization

Model Upgrade Review

Cost Reduction Initiative

Building your own app? The connector is yours to use.

AI Model Usage Analytics Connector

Claude Desktop

Product Manager

Engineering Lead

FinOps Analyst

Does this MCP track costs for every single AI model I use?

What is the difference between usage concentration and cost breakdown?

Can I use this with my existing AI stack?

Is this only for cost tracking, or can it help with efficiency?

How does this capability help reduce AI costs?

Can I see which features are using the most models?

How is routing efficiency measured?

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