Use AI Model Distillation ROI with your AI.
Connect your account once and let the AI you already use work with it, without building another integration. Financial modeling engine to evaluate the economic viability of model distillation.
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 Model Distillation ROI capability set.
These are the exact actions your AI can choose when you ask it to work with AI Model Distillation ROI.
01-04
4 capabilities in this set.
Part of 4 available through AI Model Distillation ROI.
- 01
Calculate distillation roi tool
Determines the primary economic viability of a distillation project
- 02
Compare deployment strategies tool
Compares the financial outcome of using the teacher model versus the student model across different scales
- 03
Evaluate performance impact tool
Quantifies the qualitative loss in utility based on how performance retention correlates with business value
- 04
Predict maintenance burden tool
Estimates the long-term recurring costs of keeping the student model relevant
One connector, every AI
AI Model Distillation ROI 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 Model Distillation ROI 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
887ms average. Fast in production.
AI Model Distillation ROI is checked daily against the live service.
- Fastest day
- 887ms
- Slowest day
- 966ms
- 14-day trend
- Improving-8%
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 Model Distillation ROI, so you can see the experience inside your AI.
It does not authenticate your account with AI Model Distillation ROI. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.
AI Model Distillation ROI Connector
You're all set. Choose your MCP client and follow the setup instructions.
https://edge.vinkius.com/vk_preview_3O59L24yJhjYALPkuIBC1aM6zyZzmIDy0AxVavuQ/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 Model Distillation ROI capabilities are ready to use.
{
"mcpServers": {
"ai-model-distillation-roi-mcp": {
"url": "https://edge.vinkius.com/vk_preview_3O59L24yJhjYALPkuIBC1aM6zyZzmIDy0AxVavuQ/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 Model Distillation ROI owners ask.
- 01
What is the primary purpose of this MCP?
It provides capabilities to calculate the return on investment (ROI) when distilling a large teacher model into a smaller student model.
- 02
How do I calculate the break-even point?
You can use the compare_deployment_strategies_tool to find the specific scale where the student model becomes more cost-effective than the teacher model.
- 03
Does this account for model drift?
Yes, the predict_maintenance_burden_tool helps estimate the recurring costs required to keep the student model relevant over time.
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