Does this MCP calculate the cost of the AI model itself?

Yes. It calculates the direct operational cost to process a single query, factoring in model parameters and hardware efficiency.

Can I use this for general business forecasting?

No. This MCP is specialized for AI infrastructure costs. It focuses specifically on unit economics, margins, and scaling related to LLM deployment.

What kind of inputs does get_profitability_analysis require?

You must provide two key figures: your calculated cost per query and the proposed price you plan to charge your users.

Is this MCP compatible with all AI clients?

It works with any MCP-compatible client, including Claude, Cursor, Windsurf, and VS Code. You connect once to access the full catalog.

What does 'unit economics' mean in this context?

It means calculating the cost associated with a single unit of service—in this case, the cost of processing one query.

How does model size affect my costs?

Larger models require more memory bandwidth and compute, which increases the cost per query. You can use get_unit_cost to see how specific parameter counts impact your budget.

Can I predict savings as my user base grows?

Yes, the get_scale_economics_projection capability predicts how unit cost reduction occurs as volume increases due to better batch utilization.

How does latency impact my operational budget?

Lower latency often requires smaller batch sizes or more powerful hardware, which increases costs. Use get_latency_cost_tradeoff to model this impact.

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Connect AI Inference Cost Economics 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 your true cost per query before deployment.

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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 Inference Cost Economics.

01
Get latency cost tradeoff
Analyzes the financial impact of choosing faster response times
02
Get profitability analysis
Determines the financial viability of a specific pricing strategy
03
Get scale economics projection
Predicts how cost efficiency changes as the business scales its volume
04
Get unit cost
Calculates the direct operational cost to process a single query

Real work

What teams actually run

Launching a New Chatbot Feature

You need to know if the cost of running the model with high throughput will allow you to charge enough to cover expenses and make a profit.

Optimizing Existing API Pricing

Your current pricing is underperforming. You use this MCP to test new price points and calculate the resulting margin percentage.

Scaling to Enterprise Volume

You are moving from 1 million to 10 million queries per month. You use this to project the expected drop in unit cost.

Performance vs. Budget Decisions

Your users demand sub-second responses, but it costs more. You use this to quantify exactly how much faster response time will cost you.

One connector, every AI

AI Inference Cost Economics 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 Inference Cost Economics 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

847ms average. Fast in production.

AI Inference Cost Economics is checked daily against the live service.

Daily averagePeak 1055ms

Oct 3Today

Fastest day: 673ms
Slowest day: 1055ms
14-day trend: Slowing+11%

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 Inference Cost Economics, so you can see the experience inside your AI.

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

AI Inference Cost Economics Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_UNk4ChO1CS3YQL7UdVx04v9ViFcqXKNjVSH2VsQr/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 Inference Cost Economics capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "ai-inference-cost-economics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_UNk4ChO1CS3YQL7UdVx04v9ViFcqXKNjVSH2VsQr/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 Inference Cost Economics in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Inference Cost Economics owners hand off.

This MCP is built for technical and financial decision-makers who are deploying AI models at scale. If you need to justify the budget or optimize the pricing model for an LLM product, this is your capability. It gives you hard numbers, not just estimates.

01
ML Engineer
Use this to calculate the precise unit cost and understand how hardware choices affect deployment expenses.
02
Product Manager
Use this to model different pricing strategies and ensure the product's revenue goals are met.
03
FinOps Analyst
Use this to project long-term cost efficiency and understand the impact of scaling volume.

FAQ

Questions AI Inference Cost Economics owners ask.

01
Does this MCP calculate the cost of the AI model itself?
Yes. It calculates the direct operational cost to process a single query, factoring in model parameters and hardware efficiency.
02
Can I use this for general business forecasting?
No. This MCP is specialized for AI infrastructure costs. It focuses specifically on unit economics, margins, and scaling related to LLM deployment.
03
What kind of inputs does get_profitability_analysis require?
You must provide two key figures: your calculated cost per query and the proposed price you plan to charge your users.
04
Is this MCP compatible with all AI clients?
It works with any MCP-compatible client, including Claude, Cursor, Windsurf, and VS Code. You connect once to access the full catalog.
05
What does 'unit economics' mean in this context?
It means calculating the cost associated with a single unit of service—in this case, the cost of processing one query.
06
How does model size affect my costs?
Larger models require more memory bandwidth and compute, which increases the cost per query. You can use get_unit_cost to see how specific parameter counts impact your budget.
07
Can I predict savings as my user base grows?
Yes, the get_scale_economics_projection capability predicts how unit cost reduction occurs as volume increases due to better batch utilization.
08
How does latency impact my operational budget?
Lower latency often requires smaller batch sizes or more powerful hardware, which increases costs. Use get_latency_cost_tradeoff to model this impact.

Explore

More in Finance

Connect AI Inference Cost Economics 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

Get latency cost tradeoff

Get profitability analysis

Get scale economics projection

Get unit cost

What teams actually run

Launching a New Chatbot Feature

Optimizing Existing API Pricing

Scaling to Enterprise Volume

Performance vs. Budget Decisions

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

AI Inference Cost Economics Connector

Claude Desktop

ML Engineer

Product Manager

FinOps Analyst

Does this MCP calculate the cost of the AI model itself?

Can I use this for general business forecasting?

What kind of inputs does get_profitability_analysis require?

Is this MCP compatible with all AI clients?

What does 'unit economics' mean in this context?

How does model size affect my costs?

Can I predict savings as my user base grows?

How does latency impact my operational budget?

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