Does this MCP handle costs for different LLM providers?

The MCP focuses on the economics of the caching layer itself. You provide the cost per token and the total volume, and it models the savings based on those inputs.

What kind of data do I need to run the ROI calculation?

You need metrics like total token volume, the percentage of cacheable content, the current cache hit rate, and the cost per token.

Is this just a calculator, or does it give advice?

It does both. After running the numbers, the determine_optimal_strategy capability provides a concrete recommendation on whether you should implement, scale, or abandon the strategy.

Can I use this if I don't know my exact token volumes?

The capability requires volume data to calculate savings. If you don't have the data, you'll need to gather it from your logging or billing systems first.

How do I calculate if prompt caching is worth the cost?

You can use the calculate_savings_and_roi capability. By providing your total tokens processed, the percentage of tokens that are cacheable, your expected hit rate, and the cost of your cache infrastructure, the capability will return your total savings, net profit, and ROI.

What is the difference between hit rate and effective hit rate?

The hit rate is the probability of a successful retrieval, while the effective hit rate is that value adjusted for the frequency of cache invalidation. You can use analyze_cache_efficiency to see how invalidation impacts your performance.

Can this capability help me decide which storage tier to use?

Yes. By using estimate_storage_overhead, you can model the costs of different retention periods and token volumes, which helps in choosing between Ephemeral, Standard, or Persistent tiers.

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Connect AI Prompt Caching 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 token costs and maximize ROI.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

Waiting for input…

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 Prompt Caching Economics.

01
Estimate storage overhead
This capability calculates the storage costs based on the volume of cached data. You input the data volume, and it tells you the resulting infrastructure expense.
02
Analyze cache efficiency
Use this to evaluate how well your cache is performing. It measures efficiency relative to the total volume of data your application handles.
03
Calculate savings and roi
This is the core function. It determines the total monetary benefit and the financial return (ROI) of implementing a caching layer.
04
Determine optimal strategy
This capability reviews your current metrics and recommends a clear action: whether you should implement, scale, or abandon the caching strategy based on the economics.

Real work

What teams actually run

Evaluating a New Feature

Before launching a new LLM-dependent feature, run a simulation to calculate the projected ROI and total token cost.

Optimizing Existing Pipelines

If costs are rising, use the MCP to analyze cache efficiency and determine if a different caching strategy is needed.

Budget Planning

Determine the maximum storage overhead you can afford while maintaining a target cache hit rate.

Tech Debt Review

Use the optimal strategy capability to decide if an old, underperforming caching layer should be decommissioned.

One connector, every AI

AI Prompt Caching 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 Prompt Caching 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

870ms average. Fast in production.

AI Prompt Caching Economics is checked daily against the live service.

Daily averagePeak 1097ms

Oct 3Today

Fastest day: 756ms
Slowest day: 1097ms
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 Prompt Caching Economics, so you can see the experience inside your AI.

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

AI Prompt Caching Economics Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_YbonmKs5nD3366z2WW3RQc6NExYtD4OxLik0uJge/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 Prompt Caching Economics capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "ai-prompt-caching-economics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_YbonmKs5nD3366z2WW3RQc6NExYtD4OxLik0uJge/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 Prompt Caching Economics in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Prompt Caching Economics owners hand off.

This MCP is built for technical roles that own the budget and the architecture of LLM-powered products. If you need to justify the cost of running an AI service, this capability provides the necessary financial modeling.

01
ML Engineer
Use this to model the cost impact of different caching architectures before deployment.
02
Product Manager
Run simulations to prove the ROI of new features that rely on LLM calls.
03
FinOps Analyst
Calculate the total cost of ownership for your AI infrastructure, including storage and tokens.

FAQ

Questions AI Prompt Caching Economics owners ask.

01
Does this MCP handle costs for different LLM providers?
The MCP focuses on the economics of the caching layer itself. You provide the cost per token and the total volume, and it models the savings based on those inputs.
02
What kind of data do I need to run the ROI calculation?
You need metrics like total token volume, the percentage of cacheable content, the current cache hit rate, and the cost per token.
03
Is this just a calculator, or does it give advice?
It does both. After running the numbers, the determine_optimal_strategy capability provides a concrete recommendation on whether you should implement, scale, or abandon the strategy.
04
Can I use this if I don't know my exact token volumes?
The capability requires volume data to calculate savings. If you don't have the data, you'll need to gather it from your logging or billing systems first.
05
How do I calculate if prompt caching is worth the cost?
You can use the calculate_savings_and_roi capability. By providing your total tokens processed, the percentage of tokens that are cacheable, your expected hit rate, and the cost of your cache infrastructure, the capability will return your total savings, net profit, and ROI.
06
What is the difference between hit rate and effective hit rate?
The hit rate is the probability of a successful retrieval, while the effective hit rate is that value adjusted for the frequency of cache invalidation. You can use analyze_cache_efficiency to see how invalidation impacts your performance.
07
Can this capability help me decide which storage tier to use?
Yes. By using estimate_storage_overhead, you can model the costs of different retention periods and token volumes, which helps in choosing between Ephemeral, Standard, or Persistent tiers.

Explore

More in Finance

Connect AI Prompt Caching 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

Estimate storage overhead

Analyze cache efficiency

Calculate savings and roi

Determine optimal strategy

What teams actually run

Evaluating a New Feature

Optimizing Existing Pipelines

Budget Planning

Tech Debt Review

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

AI Prompt Caching Economics Connector

Claude Desktop

ML Engineer

Product Manager

FinOps Analyst

Does this MCP handle costs for different LLM providers?

What kind of data do I need to run the ROI calculation?

Is this just a calculator, or does it give advice?

Can I use this if I don't know my exact token volumes?

How do I calculate if prompt caching is worth the cost?

What is the difference between hit rate and effective hit rate?

Can this capability help me decide which storage tier to use?

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