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Connect Inference Latency Token Tradeoff Calculat 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. Predict and optimize your model performance and throughput.

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 Inference Latency Calculator.

  1. 01

    Calculate inference metrics

    This capability predicts TTFT, TTLT, and total latency for a specific request configuration.

  2. 02

    Optimize output length

    Use this to find the maximum number of tokens you can generate while staying within a specific latency target.

  3. 03

    Simulate batching and queues

    This capability models how batching and request arrival patterns change your latency distributions.

  4. 04

    Validate sla compliance

    This capability checks if your current configuration meets your business requirements for speed and efficiency.

Real work

What teams actually run

01

Budgeting Token Length

You need to know the maximum output length possible to stay under a 2000ms latency limit.

02

SLA Verification

You want to confirm if a 1500ms latency configuration meets a 2000ms business requirement.

03

Throughput Modeling

You need to see how different request arrival patterns affect your p99 latency.

04

Performance Benchmarking

You want to calculate predicted latency for specific input and output token counts.

One connector, every AI

Inference Latency Calculator 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 Inference Latency Calculator 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

879ms average. Fast in production.

Inference Latency Calculator is checked daily against the live service.

Daily averagePeak 1105ms
Oct 3Today
Fastest day
628ms
Slowest day
1105ms
14-day trend
Slowing+9%

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 Inference Latency Calculator, so you can see the experience inside your AI.

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

Inference Latency Calculator Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_5CWOuc4BCXfaA8LyFnbZ9i7aouvSsN4UnziC8onN/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 — Inference Latency Calculator capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "inference-latency-token-tradeoff-calculator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_5CWOuc4BCXfaA8LyFnbZ9i7aouvSsN4UnziC8onN/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 Inference Latency Calculator in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Inference Latency Calculator owners hand off.

This MCP is built for engineers managing LLM infrastructure and application performance.

  • 01

    ML Engineers

    They use it to model how batching affects inference latency.

  • 02

    Backend Developers

    They use it to ensure LLM responses stay within application latency budgets.

  • 03

    DevOps Engineers

    They use it to validate that system throughput meets business SLAs.

FAQ

Questions Inference Latency Calculator owners ask.

  • 01

    What can this MCP calculate?

    It calculates TTFT, TTLT, total latency, and maximum token lengths based on your specific constraints.

  • 02

    How does it handle tail latency?

    You can use the simulation capability to model how batching and queues affect p50, p95, and p99 latency distributions.

  • 03

    Can I use this to check SLA compliance?

    Yes, the validation capability checks if your configurations meet your specific business requirements for speed and efficiency.

  • 04

    Which clients work with this MCP?

    It works with any MCP-compatible client including Claude, Cursor, Windsurf, and VS Code.

  • 05

    Is this a real-time inference capability?

    No, this is a deterministic modeling capability used to predict and optimize performance metrics.

  • 06

    How do I calculate the maximum tokens I can generate within my SLA?

    You can use the optimize_output_length capability. It calculates the remaining latency budget after the prefill phase and multiplies it by the generation speed to find the optimal token count.

  • 07

    What is the difference between TTFT and TTLT?

    TTFT (Time to First Token) is the latency before the first token appears, driven by prefill speed. TTLT (Time to Last Token) is the total end-to-end duration including the generation phase.

  • 08

    Can I model the impact of multiple concurrent requests?

    Yes, use the simulate_batching_and_queues capability to predict how batch size and request arrival patterns affect latency percentiles like p95 and p99.

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