Use AI Training Economics with your AI.
Connect your account once and let the AI you already use work with it, without building another integration. Analyze the financial impact of AI training, including compute costs, efficiency, and ROI.
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.
Complete set · 4 capabilities
The complete AI Training Economics capability set.
These are the exact actions your AI can choose when you ask it to work with AI Training Economics.
01-04
4 capabilities in this set.
Part of 4 available through AI Training Economics.
- 01
Calculate training run cost
Determines the total financial outlay for a single training session
- 02
Compare deployment strategies
Helps decide between Cloud and On-Premise based on projected usage
- 03
Analyze compute efficiency
Evaluates how productive the compute resources are relative to the complexity of the task
- 04
Calculate training roi
Measures the economic viability of the training project
One connector, every AI
AI Training 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.
Claude
ChatGPT
Gemini
Perplexity
Grok
Microsoft Copilot
Cursor
VS Code
Windsurf
JetBrains
Cline
LangChain
Vercel AI SDK
Lovable
Z.ai
Raycast
Qwen Code
Kimi Code
Le ChatBuilding your own app? The connector is yours to use.
You don't need a client to put AI Training 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
838ms average. Fast in production.
AI Training Economics is checked daily against the live service.
- Fastest day
- 838ms
- Slowest day
- 838ms
- 14-day trend
- Stable0%
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 Training Economics, so you can see the experience inside your AI.
It does not authenticate your account with AI Training Economics. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.
AI Training Economics Connector
You're all set. Choose your MCP client and follow the setup instructions.
https://edge.vinkius.com/vk_preview_5Vz7UHDYJ0p7SWVSlQJ3IshsVUpE1N1a1CVGwSez/mcpClaude 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 Training Economics capabilities are ready to use.
{
"mcpServers": {
"ai-model-training-cost-economics-mcp": {
"url": "https://edge.vinkius.com/vk_preview_5Vz7UHDYJ0p7SWVSlQJ3IshsVUpE1N1a1CVGwSez/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? link.label
FAQ
Questions AI Training Economics owners ask.
- 01
How do I calculate the cost of a single training session?
You can use the calculate_training_run_cost capability by providing the GPU type, total compute hours, deployment model, and personnel costs.
- 02
Can I compare Cloud and On-Premise costs?
Yes, the compare_deployment_strategies capability allows you to compare total costs between Cloud and On-Premise based on your projected compute hours and hourly rates.
- 03
How is compute efficiency measured?
Efficiency is evaluated using analyze_compute_efficiency, which compares successful training cycles against the total compute hours and model complexity.
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