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Connect AI Platform Build Vs Buy Decision Engine 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. Get a data-driven recommendation on your next AI investment.

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 AI Platform Decision Engine.

  1. 01

    Analyze tco

    This capability compares the total economic investment required for building versus buying. It looks at long-term costs to find the true price of each path.

  2. 02

    Assess strategic fit

    This capability reconciles your financial data with qualitative business requirements. It helps you see if a solution actually meets your specific needs.

  3. 03

    Evaluate speed to market

    This capability determines which option gets you operational faster. It identifies the time advantage of buying over building.

  4. 04

    Generate final decision

    This capability aggregates all your calculated metrics into one recommendation. It pulls together cost, speed, and fit into a single output.

Real work

What teams actually run

01

Budget Planning

Compare the 5-year cost of a custom build against a subscription model to set annual budgets.

02

Launch Strategy

Decide between a quick vendor launch or a slower proprietary build based on market windows.

03

Risk Management

Evaluate if the risk of using a third-party vendor outweighs the cost of building in-house.

04

Strategic Roadmap

Determine if a platform's customization level matches your long-term product vision.

One connector, every AI

AI Platform Decision Engine 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 AI Platform Decision Engine 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

925ms average. Fast in production.

AI Platform Decision Engine is checked daily against the live service.

Daily averagePeak 1057ms
Oct 3Today
Fastest day
797ms
Slowest day
1057ms
14-day trend
Slowing+13%

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 Platform Decision Engine, so you can see the experience inside your AI.

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

AI Platform Decision Engine Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_FZjUNNAP9wVvx0HFc30MkUXhZq2ijTUYE1tU6mlx/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 — AI Platform Decision Engine capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "ai-platform-build-vs-buy-decision-engine-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_FZjUNNAP9wVvx0HFc30MkUXhZq2ijTUYE1tU6mlx/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 Platform Decision Engine in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Platform Decision Engine owners hand off.

This MCP is built for leaders making high-level technology and capital allocation decisions.

  • 01

    CTOs

    Hand off technical cost and development timeline data to get a strategic recommendation.

  • 02

    CFOs

    Provide budget constraints and maintenance ratios to evaluate long-term financial impact.

  • 03

    Product Leaders

    Input customization needs and market urgency to determine the best path to launch.

FAQ

Questions AI Platform Decision Engine owners ask.

  • 01

    What is the main purpose of this MCP?

    It helps you decide whether to build your own AI platform or buy one from a vendor by analyzing costs, speed, and strategic fit.

  • 02

    Which AI clients can I use with this MCP?

    You can use this with any MCP-compatible client, including Claude, Cursor, Windsurf, and VS Code.

  • 03

    Does this capability provide a single answer?

    Yes, the generate_final_decision capability aggregates all your inputs into one unified recommendation.

  • 04

    Can I include maintenance costs in the analysis?

    Yes, you can use the analyze_tco capability to include maintenance ratios in your long-term cost comparisons.

  • 05

    How does it handle qualitative data like customization needs?

    The assess_strategic_fit capability is specifically designed to reconcile financial numbers with qualitative business needs.

  • 06

    How is the Total Cost of Ownership calculated?

    The analyze_tco capability calculates the cumulative cost by adding the initial investment to the annual maintenance or licensing costs over your specified planning horizon.

  • 07

    Can I factor in strategic importance?

    Yes, the assess_strategic_fit capability allows you to input customization needs and strategic importance to ensure the decision aligns with long-term business goals.

  • 08

    What is the final output of the engine?

    The generate_final_decision capability provides a final verdict, a summary of the reasoning, and a risk profile based on all analyzed metrics.

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