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Connect ML Experiment Tracking Cost Analyzer to your AI

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. Model and forecast your machine learning operational expenses.

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.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

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.

A futuristic MCP client docking bay
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 ML Experiment Costing.

  1. 01

    Evaluate archival efficiency

    This capability calculates the actual savings gained by moving data from active storage to archival tiers. It helps you decide if your data retention policies are working.

  2. 02

    Get knowledge utility score

    This capability assigns a value to your experiment history based on its importance for research reproducibility. It helps you weigh cost against scientific value.

  3. 03

    Predict storage trajectory

    This capability forecasts your total storage volume requirements over a set period. Use it to prevent sudden capacity issues as your experiment count grows.

  4. 04

    Calculate monthly tracking cost

    This capability determines your total monthly operational spend for tracking infrastructure. It provides the baseline for your MLOps budget.

Real work

What teams actually run

01

Budget Forecasting

Calculate how much your experiment tracking will cost as you scale from 10 to 100 experiments per month.

02

Storage Tiering Decisions

Determine if moving old experiment data to cold storage provides enough savings to justify the move.

03

Resource Planning

Project your total storage needs for the next year to ensure your infrastructure can handle the load.

04

Research Audit

Check the utility score of your experiment history to ensure you aren't deleting critical data.

One connector, every AI

ML Experiment Costing 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.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

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

You don't need a client to put ML Experiment Costing 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

915ms average. Fast in production.

ML Experiment Costing is checked daily against the live service.

Daily averagePeak 1138ms
Oct 3Today
Fastest day
714ms
Slowest day
1138ms
14-day trend
Stable+3%

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 ML Experiment Costing, so you can see the experience inside your AI.

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

ML Experiment Costing Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_LpCrDK7YbLXumDZ1Yg3SufrVlCdTOf49sRuTis94/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — ML Experiment Costing capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "ml-experiment-tracking-cost-analyzer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_LpCrDK7YbLXumDZ1Yg3SufrVlCdTOf49sRuTis94/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 ML Experiment Costing in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work ML Experiment Costing owners hand off.

This MCP is built for teams managing large-scale machine learning workloads who need to keep infrastructure costs under control.

  • 01

    MLOps Engineer

    Uses the capability to forecast storage growth and manage infrastructure budgets.

  • 02

    Data Scientist

    Uses the capability to evaluate the value of experiment history for future research.

  • 03

    Infrastructure Manager

    Uses the capability to optimize storage tiering and reduce monthly operational spend.

FAQ

Questions ML Experiment Costing owners ask.

  • 01

    What can this MCP calculate?

    It calculates monthly tracking costs, future storage trajectories, archival savings, and research utility scores.

  • 02

    How does it help with storage management?

    It predicts how much storage you will need over time and evaluates if moving data to archival tiers will save money.

  • 03

    Can I use this with Claude or Cursor?

    Yes, you can connect this MCP to any MCP-compatible client including Claude, Cursor, and Windsurf.

  • 04

    Does this MCP require me to host anything?

    No, Vinkius hosts and manages the MCP for you. You just connect your client and start using the capabilities.

  • 05

    What is a knowledge utility score?

    It is a metric that quantifies how much value your experiment history provides for research reproducibility.

  • 06

    How do I calculate my monthly budget for experiment tracking?

    You can use the calculate_monthly_tracking_cost capability by providing your monthly experiment volume, average storage per experiment, metadata complexity, and search requirements.

  • 07

    Can I predict how much storage I will need in six months?

    Yes, use the predict_storage_trajectory capability. It calculates projected growth based on your current experiment rate and retention policy.

  • 08

    How does archival storage affect my costs?

    You can assess the savings by using evaluate_archival_efficiency, which calculates potential monthly savings when moving aged data to lower-cost storage tiers.

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