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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
DeepSeekMistralCursorVS CodeWindsurfJetBrainsClineLovableVercel AI SDKLangChain

Use Portkey with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. AI gateway observability: monitor logs, costs, and manage LLM configurations via agents.

Included with plan

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

Complete set · 10 capabilities

The complete Portkey capability set.

These are the exact actions your AI can choose when you ask it to work with Portkey.

Capability set01 / 03

01-04

4 capabilities in this set.

Part of 10 available through Portkey.

  1. 01

    Create policy

    Requires policy name, budget limit (USD or token count), and optionally the target users or virtual keys to restrict. Returns the created policy details. Use this to enforce cost controls on specific teams or projects using the gateway. Create a new budget or usage policy for AI gateway access

  2. 02

    Delete policy

    Requires the policy ID. Use this when a project ends or budget constraints are no longer needed. Remove a budget or usage policy from Portkey

  3. 03

    Export logs

    Optionally filters by date range, model, or user. Returns an export ID or download URL. Use this for audit trails, cost reporting, or offline analysis of AI usage patterns. Export AI gateway logs for external analysis or compliance reporting

  4. 04

    Get log details

    Requires the log ID from list_logs results. Use this for deep debugging of specific AI interactions. Get detailed information about a specific AI gateway log entry

Capability set02 / 03

05-07

3 capabilities in this set.

Part of 10 available through Portkey.

  1. 05

    Get virtual keys

    Virtual keys map to underlying provider keys (OpenAI, Anthropic, etc.) with metadata, usage limits, and policy associations. Returns key IDs, names, provider targets, current usage, and status. Use this to audit API key usage or identify keys approaching limits. List all virtual API keys managed by Portkey

  2. 06

    List configs

    Returns config IDs, names, creation dates, and associated virtual keys. Use this to review how LLM requests are routed or to audit gateway behavior. List all gateway configurations stored in Portkey

  3. 07

    List logs

    Returns log IDs, timestamps, model names, token usage, latency, costs, and status codes. Use this to monitor AI usage, identify expensive calls, or debug latency issues. Supports pagination via limit/offset. List recent AI gateway logs and traces from Portkey

Capability set03 / 03

08-10

3 capabilities in this set.

Part of 10 available through Portkey.

  1. 08

    List models

    ). Returns model names, provider names, supported endpoints (chat, embeddings, etc.), and capabilities. Use this to discover which models are routable via your gateway. List all LLM models supported by the Portkey gateway

  2. 09

    List policies

    Returns policy names, limits, current consumption, and affected users/keys. Use this to review guardrails preventing runaway AI costs. List all budget and usage policies defined in Portkey

  3. 10

    Submit feedback

    Requires the log ID, rating (LIKE, DISLIKE, or UNLIKE to remove), and optional text feedback. Use this to build RLHF datasets or monitor user satisfaction with AI outputs. Submit user feedback (Like/Dislike) for a specific AI response log

Observed, not estimated

837ms average. Fast in production.

Portkey is checked daily against the live service.

Daily averagePeak 1203ms
Aug 20Today
Fastest day
703ms
Slowest day
1203ms
14-day trend
Stable-1%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 10 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 Portkey, so you can see the experience inside your AI.

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

Portkey Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_BpHtQMK896fkGyepmah9cM552NpibU6fUP5BakNG/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 — Portkey capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "portkey-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_BpHtQMK896fkGyepmah9cM552NpibU6fUP5BakNG/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

FAQ

Questions Portkey owners ask.

  • 01

    Which LLM providers does Portkey support?

    Portkey supports 1,600+ LLMs including OpenAI, Anthropic, Google, Mistral, Azure OpenAI, AWS Bedrock, Cohere, Hugging Face, and many more. Use the list_models capability to see the full catalog available via your gateway.

  • 02

    How does Portkey help control AI costs?

    Portkey provides granular visibility into token usage, latency, and costs per model, team, or virtual key. You can create budget policies with hard limits to prevent runaway spending. The gateway also supports caching to reduce duplicate calls and fallbacks to cheaper models when appropriate.

  • 03

    Can I track feedback on AI responses?

    Yes! Portkey allows you to submit Like/Dislike feedback for any logged LLM call. This data helps improve model selection, evaluate agent performance, and build RLHF datasets for fine-tuning.

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