Is this MCP only for large companies?

No. This MCP helps any team that uses vector databases. It provides the specific calculations needed to model costs, whether you're dealing with thousands or millions of vectors.

What kind of costs does it calculate?

It focuses on the full lifecycle cost. Specifically, it calculates the cost of generating embeddings, the recurring cost of storing vectors, and the cost associated with performing retrieval queries.

Do I need to know my model pricing beforehand?

Yes. The capabilities require you to input details like vector dimensions and model tiers so they can calculate the unit cost accurately based on current market rates.

Can I use this with my existing database?

The MCP models the economics of vector databases generally. You provide the volume and rates, and it gives you the financial estimate, helping you plan for your current setup.

How does dimensionality affect my costs?

Higher dimensionality increases both the initial generation cost via get_embedding_unit_cost and the long-term storage requirements calculated by calculate_storage_economics.

Can I predict the total cost of my vector database?

Yes, you can use calculate_storage_economics to estimate total storage costs based on your vector count, dimensionality, and the price per gigabyte.

What is a retrieval margin?

The retrieval margin is the ratio between the cost of generating an embedding and the cost of performing a search, which you can assess using evaluate_retrieval_viability.

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Connect AI Embedding Cost Structure 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 the true cost of every embedding and query.

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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 Embedding Cost Structure.

01
Generate full economic report
Provides a comprehensive summary of the entire embedding lifecycle cost
02
Get embedding unit cost
Determines the cost to generate a single embedding based on current model pricing and vector dimensions
03
Evaluate retrieval viability
Assesses if the cost of retrieval stays within profitable or budget-friendly limits given latency needs
04
Calculate storage economics
Estimates the total cost of storing a specific volume of vectors in a database

Real work

What teams actually run

New Product Feasibility Study

Before committing to a vector database, ask your agent to generate a full economic report to see if the projected costs align with your budget.

Optimizing Data Ingestion

Use the unit cost capability to compare different embedding models and find the cheapest way to generate your vector data.

Scaling for Growth

When planning for millions of records, run the storage economics capability to predict the total cost of scaling your vector index.

Pricing AI Features

Determine the cost per query using the retrieval viability capability, which helps you set profitable pricing for end-users.

One connector, every AI

AI Embedding Cost Structure 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 Embedding Cost Structure 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

894ms average. Fast in production.

AI Embedding Cost Structure is checked daily against the live service.

Daily averagePeak 1269ms

Oct 3Today

Fastest day: 629ms
Slowest day: 1269ms
14-day trend: Slowing+42%

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 Embedding Cost Structure, so you can see the experience inside your AI.

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

AI Embedding Cost Structure Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_j6hy4AkJNhAGst4g6xKdatBJ3EFFVtUBlMNGipyV/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 Embedding Cost Structure capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "ai-embedding-cost-structure-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_j6hy4AkJNhAGst4g6xKdatBJ3EFFVtUBlMNGipyV/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 Embedding Cost Structure in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Embedding Cost Structure owners hand off.

This MCP is built for technical roles that own the operational budget of AI systems. If you're designing a product that uses vector search, you need to know the true cost at scale. It helps you prove the financial viability of your LLM features.

01
ML Architect
Use this to model the total cost of ownership for a new vector database implementation.
02
ML Engineer
Use this to calculate the unit cost of embeddings and optimize your data pipeline spending.
03
Product Manager
Use this to forecast operational expenses and set realistic pricing models for AI features.

FAQ

Questions AI Embedding Cost Structure owners ask.

01
Is this MCP only for large companies?
No. This MCP helps any team that uses vector databases. It provides the specific calculations needed to model costs, whether you're dealing with thousands or millions of vectors.
02
What kind of costs does it calculate?
It focuses on the full lifecycle cost. Specifically, it calculates the cost of generating embeddings, the recurring cost of storing vectors, and the cost associated with performing retrieval queries.
03
Do I need to know my model pricing beforehand?
Yes. The capabilities require you to input details like vector dimensions and model tiers so they can calculate the unit cost accurately based on current market rates.
04
Can I use this with my existing database?
The MCP models the economics of vector databases generally. You provide the volume and rates, and it gives you the financial estimate, helping you plan for your current setup.
05
How does dimensionality affect my costs?
Higher dimensionality increases both the initial generation cost via get_embedding_unit_cost and the long-term storage requirements calculated by calculate_storage_economics.
06
Can I predict the total cost of my vector database?
Yes, you can use calculate_storage_economics to estimate total storage costs based on your vector count, dimensionality, and the price per gigabyte.
07
What is a retrieval margin?
The retrieval margin is the ratio between the cost of generating an embedding and the cost of performing a search, which you can assess using evaluate_retrieval_viability.

Explore

More in Vector Databases

Connect AI Embedding Cost Structure 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

Generate full economic report

Get embedding unit cost

Evaluate retrieval viability

Calculate storage economics

What teams actually run

New Product Feasibility Study

Optimizing Data Ingestion

Scaling for Growth

Pricing AI Features

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

AI Embedding Cost Structure Connector

Claude Desktop

ML Architect

ML Engineer

Product Manager

Is this MCP only for large companies?

What kind of costs does it calculate?

Do I need to know my model pricing beforehand?

Can I use this with my existing database?

How does dimensionality affect my costs?

Can I predict the total cost of my vector database?

What is a retrieval margin?

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