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Vinkius

Portkey Connector for AI agents.

10 live capabilities

Manage LLM costs and gateway observability with natural language commands.

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AI Agent

Why people use Portkey

Portkey LLM Cost Management for Enterprise Teams

This Connector puts that data into your agent's hands. You can ask for a cost summary, check for policy violations, or export logs for an audit without ever leaving your chat interface. You get a single source of truth for your entire AI infrastructure.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You move from manual dashboard clicking to natural language commands for your entire LLM infrastructure.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Spiking Costs

    A FinOps analyst notices a spend spike and asks the agent to list_policies to see if a team exceeded their budget.

  2. Real-world use case 02

    Debugging Latency

    An engineer asks the agent to list_logs and get_log_details to find out why a specific request took 10 seconds.

  3. Real-world use case 03

    Compliance Audit

    A governance officer asks the agent to export_logs for the last 30 days to satisfy a quarterly security review.

Complete set · 10capabilities

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 Capability

    Create policy

    Create a new budget or usage policy for AI gateway access. Use this to enforce cost controls on specific teams or projects.

  2. 02 Capability

    Delete policy

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

  3. 03 Capability

    Export logs

    Export logs for external analysis or compliance reporting. You can filter by date, model, or user to get a specific download URL.

  4. 04 Capability

    Get log details

    Get detailed information about a specific AI gateway log entry. Use this for deep debugging of specific interactions.

Capability set02 / 03

05—07

3 capabilities in this set.

Part of 10 available through Portkey.

  1. 05 Capability

    Get virtual keys

    List all virtual API keys managed by Portkey. This lets you audit usage and identify keys approaching their limits.

  2. 06 Capability

    List configs

    List all gateway configurations stored in Portkey. Use this to review how requests are routed or to audit behavior.

  3. 07 Capability

    List logs

    List recent AI gateway logs and traces from Portkey. Use this to monitor usage, identify expensive calls, or debug latency.

Capability set03 / 03

08—10

3 capabilities in this set.

Part of 10 available through Portkey.

  1. 08 Capability

    List models

    List all LLM models supported by the Portkey gateway. Use this to discover which models are routable via your gateway.

  2. 09 Capability

    List policies

    List all budget and usage policies defined in Portkey. Use this to review guardrails preventing runaway AI costs.

  3. 10 Capability

    Submit feedback

    Submit user feedback for a specific AI response log. Use this to build RLHF datasets or monitor user satisfaction.

Set up in minutes

One URL. Then ask Portkey to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Portkey from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_BpHtQMK896fkGyepmah9cM552NpibU6fUP5BakNG/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it Portkey, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Portkey for the conversation.

Where the request belongs

Work Portkey can move forward.

Built around the request

For the LLM ops engineer who's tired of manual cost tracking and the FinOps analyst trying to keep AI spending from spiraling out of control.

01

LLM Ops Engineer

Monitors gateway health and debugs latency issues on a daily basis.

02

FinOps Analyst

Tracks spend across multiple departments and enforces budget policies.

03

AI Governance Officer

Audits logs for compliance and ensures API keys aren't being overused.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
Helicone (LLM Observability) logo
01 10 capabilities

Helicone (LLM Observability)

Monitor LLM usage via Helicone. track requests, analyze costs, measure latency, and manage prompts.

View Connector
LiteLLM (LLM Proxy & Spend Tracking) logo
02 10 capabilities

LiteLLM (LLM Proxy & Spend Tracking)

Manage your LLM gateway via LiteLLM. generate API keys, track spending, and orchestrate model fallback paths.

View Connector
Keywords AI logo
03 11 capabilities

Keywords AI

Monitor and optimize your LLM API usage with a unified gateway that tracks costs, latency, and model performance across providers.

View Connector
LangSmith logo
04 3 capabilities

LangSmith

Observability and evaluation platform for LLM applications. monitor traces, debug agent runs, and track performance metrics across your AI stack.

View Connector
Eden AI logo
05 6 capabilities

Eden AI

Equip your AI agent to manage unified AI workflows, track providers, and monitor API usage via the Eden AI platform.

View Connector
Datadog AI (LLM Observability) logo
06 10 capabilities

Datadog AI (LLM Observability)

Monitor LLM performance via Datadog. track token usage, audit prompts, and monitor AI model metrics directly from any AI agent.

View Connector

Bring your own AI

Change the model, client or framework. Keep Portkey connected.

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Before you connect

Questions about Portkey.

The practical details behind the request, access and result.

Can the Portkey MCP help me track my AI spending?

Yes, it connects to your gateway to show real-time costs and token usage across all your models.

How does Portkey MCP manage my API keys?

It uses virtual keys to let you manage provider keys in one place while tracking usage limits.

Can I use Portkey MCP to set spending limits?

You can create and manage budget policies to cap how much specific teams or projects can spend.

How do I debug failed AI requests with Portkey MCP?

Your agent can pull detailed logs and traces for specific interactions to see exactly what went wrong.

Can Portkey MCP export my data for audits?

Yes, it can generate export IDs for your logs so you can perform offline compliance checks.

Does Portkey MCP support multiple LLM providers?

It provides a unified view for all your providers, including OpenAI, Anthropic, and Google.

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.

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.

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.

One connection away

Give your agent a direct line to Portkey.

Connect Portkey once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available