Is this for general AI cost tracking?

No. This MCP focuses specifically on the economics of AI conversation memory. It helps you model the costs related to context window usage, storage, and retrieval overhead.

What kind of costs can I calculate?

You can calculate three main types of costs: the runtime cost of the conversation, the cost of persistent storage, and the cost of retrieving that stored data.

Do I need to know my token pricing beforehand?

Yes, the capabilities require you to input the token price and usage volume to generate an accurate cost estimate.

Can I use this to compare different memory methods?

Absolutely. You can use the simulation capabilities to compare methods like full context versus summarization, showing you potential percentage savings.

How can I reduce my LLM inference costs?

You can use analyze_optimization_opportunities to compare your current strategy against alternatives like summarization or pruning to identify potential savings.

Does this capability account for vector database costs?

Yes, by using calculate_storage_and_retrieval_overhead, you can estimate the persistent costs of maintaining history in different storage tiers and the cost of retrieval queries.

What is the difference between summarization and pruning?

Summarization compresses older turns into a condensed summary to save tokens, while pruning drops the oldest tokens entirely once a window limit is reached.

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Connect AI Memory Cost Analyzer 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. Optimize your LLM architecture budget instantly.

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 Memory Cost Analyzer.

01
Calculate storage and retrieval overhead
Calculate persistent storage and retrieval costs
02
Analyze optimization opportunities
Analyze potential cost savings by changing memory strategies
03
Simulate memory efficiency tradeoff
Simulate the tradeoff between window size and cost
04
Calculate conversation runtime cost
Calculate the runtime cost of a conversation based on length and memory strategy

Real work

What teams actually run

Scaling a Chatbot Product

Before launching a new chat feature, you use this MCP to model the cost of 1 million user interactions, ensuring profitability.

Comparing Memory Strategies

You compare the cost of full context vs. summarization, allowing you to choose the most cost-effective approach for your users.

Budgeting for New Features

A product manager asks, 'How much will this new feature cost?' You run a simulation to provide a concrete, data-backed answer.

Optimizing Context Windows

You test different window sizes to find the smallest context that still provides enough information, minimizing token usage and cost.

One connector, every AI

AI Memory Cost Analyzer 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 Memory Cost Analyzer 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

921ms average. Fast in production.

AI Memory Cost Analyzer is checked daily against the live service.

Daily averagePeak 1079ms

Oct 3Today

Fastest day: 655ms
Slowest day: 1079ms
14-day trend: Slowing+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 AI Memory Cost Analyzer, so you can see the experience inside your AI.

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

AI Memory Cost Analyzer Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_4MaqXgJc5713NJFvD3nfrG7ypHzOyFpIRGwcjqw6/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 Memory Cost Analyzer capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "ai-memory-cost-analyzer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_4MaqXgJc5713NJFvD3nfrG7ypHzOyFpIRGwcjqw6/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 Memory Cost Analyzer in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Memory Cost Analyzer owners hand off.

This MCP is essential for engineers and architects building large-scale AI applications. If your product relies on conversation memory, you need to know the cost implications. It helps you move beyond guesswork and build financially sound systems.

01
AI Architect
Designs the optimal memory architecture by modeling the financial trade-offs between context size and cost.
02
ML Engineer
Calculates the precise runtime and storage overheads to ensure the application stays within budget.
03
FinOps Specialist
Validates the economic viability of new features by running cost simulations against existing infrastructure.

FAQ

Questions AI Memory Cost Analyzer owners ask.

01
Is this for general AI cost tracking?
No. This MCP focuses specifically on the economics of AI conversation memory. It helps you model the costs related to context window usage, storage, and retrieval overhead.
02
What kind of costs can I calculate?
You can calculate three main types of costs: the runtime cost of the conversation, the cost of persistent storage, and the cost of retrieving that stored data.
03
Do I need to know my token pricing beforehand?
Yes, the capabilities require you to input the token price and usage volume to generate an accurate cost estimate.
04
Can I use this to compare different memory methods?
Absolutely. You can use the simulation capabilities to compare methods like full context versus summarization, showing you potential percentage savings.
05
How can I reduce my LLM inference costs?
You can use analyze_optimization_opportunities to compare your current strategy against alternatives like summarization or pruning to identify potential savings.
06
Does this capability account for vector database costs?
Yes, by using calculate_storage_and_retrieval_overhead, you can estimate the persistent costs of maintaining history in different storage tiers and the cost of retrieval queries.
07
What is the difference between summarization and pruning?
Summarization compresses older turns into a condensed summary to save tokens, while pruning drops the oldest tokens entirely once a window limit is reached.

Explore

More in Finance

Connect AI Memory Cost Analyzer 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

Calculate storage and retrieval overhead

Analyze optimization opportunities

Simulate memory efficiency tradeoff

Calculate conversation runtime cost

What teams actually run

Scaling a Chatbot Product

Comparing Memory Strategies

Budgeting for New Features

Optimizing Context Windows

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

AI Memory Cost Analyzer Connector

Claude Desktop

AI Architect

ML Engineer

FinOps Specialist

Is this for general AI cost tracking?

What kind of costs can I calculate?

Do I need to know my token pricing beforehand?

Can I use this to compare different memory methods?

How can I reduce my LLM inference costs?

Does this capability account for vector database costs?

What is the difference between summarization and pruning?

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