What is TCO, and why do I need this MCP?

TCO stands for Total Cost of Ownership. This MCP helps you calculate the full cost of an AI deployment, including hardware, energy, connectivity, and optimization efforts. It prevents you from underestimating your real-world budget.

Does this MCP only handle cloud costs?

No. It is designed for edge AI. It calculates costs for both local edge hardware and cloud resources, allowing you to find the most economical hybrid solution.

What kind of data does it use for cost modeling?

You provide the core inputs, such as the cost per inference, device lifespan, energy consumption rates, and data transfer rates. The MCP uses these figures to run the economic models.

Can I use this with my existing ML models?

Yes. You use the MCP to model the economics of your existing models. It helps you decide if optimization techniques, like quantization, are worth the effort financially.

How does this capability help with edge deployment decisions?

It provides precise calculations for unit costs, optimization ROI, and hybrid workload distribution to help balance performance and budget.

Can I calculate the impact of network costs?

Yes, using analyze_connectivity_impact, you can estimate monthly data transmission costs and bandwidth requirements.

What is a hybrid workload split?

It is the optimal distribution of tasks where some inferences run on edge devices and others run in the cloud to minimize total monthly costs.

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Connect AI Edge Deployment 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. Determine the optimal balance between cloud and local hardware.

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 Edge Economics Engine.

01
Analyze connectivity impact
This capability estimates the cost overhead introduced by data transmission requirements. You input data volume and transfer rates to see how much connectivity adds to your total cost.
02
Calculate hybrid workload split
Use this to find the ideal distribution of tasks between edge and cloud. You specify costs and capacities, and the MCP determines the split that minimizes your total operating expense.
03
Calculate inference unit cost
This capability determines the cost of a single inference event at the edge. You calculate the amortized hardware and energy costs to get a precise unit cost.
04
Calculate optimization roi
Evaluate if investing in model optimization is financially justified. You input current and projected costs to calculate the Return on Investment (ROI) for pruning or quantization efforts.

Real work

What teams actually run

Smart Retail Monitoring

You need to deploy object detection cameras across 50 stores. Use the MCP to model the TCO, factoring in local processing costs and data upload fees.

Industrial IoT Robotics

A factory floor uses AI robots. Use the MCP to determine if it's cheaper to process all data locally or send a fraction of it to the cloud.

Autonomous Vehicle Fleet

Model the cost of running perception models across a fleet of vehicles, factoring in both compute time and necessary data bandwidth.

Healthcare Remote Diagnostics

Calculate the cost of running image analysis models on remote edge devices, ensuring the solution is financially sustainable.

One connector, every AI

Edge Economics Engine 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 Edge Economics Engine 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

891ms average. Fast in production.

Edge Economics Engine is checked daily against the live service.

Daily averagePeak 1117ms

Oct 3Today

Fastest day: 676ms
Slowest day: 1117ms
14-day trend: Improving-12%

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 Edge Economics Engine, so you can see the experience inside your AI.

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

Edge Economics Engine Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_pZxizziR9WEPL7LngYpjhImKc0XAEg2j6DG8dUvN/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 — Edge Economics Engine capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "ai-edge-deployment-economics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_pZxizziR9WEPL7LngYpjhImKc0XAEg2j6DG8dUvN/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 Edge Economics Engine in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Edge Economics Engine owners hand off.

This MCP is essential for ML Engineers, CTOs, and Operations Directors who need to move beyond simple estimates. If your deployment budget depends on accurate TCO modeling, this capability gives you the data to justify your architecture decisions.

01
ML Engineer
Use this to calculate the precise cost per inference and determine the ideal hybrid workload split for deployment.
02
CTO / Decision Maker
Leverage this to model the total cost of ownership and justify large-scale infrastructure investments.
03
Operations Director
Run analyses to predict connectivity-related data costs and evaluate the ROI of model optimization.

FAQ

Questions Edge Economics Engine owners ask.

01
What is TCO, and why do I need this MCP?
TCO stands for Total Cost of Ownership. This MCP helps you calculate the full cost of an AI deployment, including hardware, energy, connectivity, and optimization efforts. It prevents you from underestimating your real-world budget.
02
Does this MCP only handle cloud costs?
No. It is designed for edge AI. It calculates costs for both local edge hardware and cloud resources, allowing you to find the most economical hybrid solution.
03
What kind of data does it use for cost modeling?
You provide the core inputs, such as the cost per inference, device lifespan, energy consumption rates, and data transfer rates. The MCP uses these figures to run the economic models.
04
Can I use this with my existing ML models?
Yes. You use the MCP to model the economics of your existing models. It helps you decide if optimization techniques, like quantization, are worth the effort financially.
05
How does this capability help with edge deployment decisions?
It provides precise calculations for unit costs, optimization ROI, and hybrid workload distribution to help balance performance and budget.
06
Can I calculate the impact of network costs?
Yes, using analyze_connectivity_impact, you can estimate monthly data transmission costs and bandwidth requirements.
07
What is a hybrid workload split?
It is the optimal distribution of tasks where some inferences run on edge devices and others run in the cloud to minimize total monthly costs.

Explore

More in Economics

Connect Edge Economics Engine 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

Analyze connectivity impact

Calculate hybrid workload split

Calculate inference unit cost

Calculate optimization roi

What teams actually run

Smart Retail Monitoring

Industrial IoT Robotics

Autonomous Vehicle Fleet

Healthcare Remote Diagnostics

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

Edge Economics Engine Connector

Claude Desktop

ML Engineer

CTO / Decision Maker

Operations Director

What is TCO, and why do I need this MCP?

Does this MCP only handle cloud costs?

What kind of data does it use for cost modeling?

Can I use this with my existing ML models?

How does this capability help with edge deployment decisions?

Can I calculate the impact of network costs?

What is a hybrid workload split?

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