How to set up OpenRouter LLM observability

Ian Vanagas

Feb 20, 2025

OpenRouter makes it easy to use a range of different LLMs. No matter which you use, understanding API usage, costs, and latency is crucial for understanding how users interact with your AI features. In this tutorial, we'll show you how to monitor important metrics such as:

Total cost across different models
Average cost per user
Average API response time
Model performance comparisons

We'll build a basic Next.js app, implement the OpenRouter API, and capture these events automatically using PostHog.

1. Creating a Next.js app

To make requests to OpenRouter and display the responses, we’ll create a basic Next.js app. To do this, start by ensuring Node.js is installed (version 18.0 or newer) then run the following command. Say no to TypeScript, yes to app router, and the defaults for other options.

Terminal

npx create-next-app@latest openrouter-observability

After creating your app, go into the newly created folder and install the required dependencies:

Terminal

cd openrouter-observability
npm install --save posthog-node @posthog/ai openai

Now, we can create our frontend. It will be:

A form with a textfield and button for user input
A label to show the AI output
A dropdown to select different OpenRouter models
An API call to our backend to generate a response

We can do this all in app/page.js like this:

JavaScript

'use client'
import React, { useState } from 'react';

const models = ['openai/gpt-4o', 'anthropic/claude-3-haiku', 'meta-llama/llama-2-70b-chat'];

export default function Home() {
  const [userInput, setUserInput] = useState('');
  const [aiResponse, setAiResponse] = useState('');
  const [selectedModel, setSelectedModel] = useState(models[0]);

  const fetchAIResponse = async () => {
    try {
      setAiResponse('Generating...');

      const res = await fetch('/api/generate', {
        method: 'POST',
        headers: {
          'Content-Type': 'application/json',
        },
        body: JSON.stringify({ input: userInput, model: selectedModel }),
      })
      const response = await res.json();
      setAiResponse(response.content);
    } catch (error) {
      setAiResponse(error.message);
    }
  };

  const handleInputChange = (event) => {
    setUserInput(event.target.value);
  };

  const handleModelChange = (event) => {
    setSelectedModel(event.target.value);
  };

  const handleSubmit = (event) => {
    event.preventDefault();
    fetchAIResponse();
  };

  return (
    <div style={{ display: 'flex', flexDirection: 'column', alignItems: 'center', justifyContent: 'center', minHeight: '100vh', gap: '20px' }}>
      <form onSubmit={handleSubmit}>
        <input
          type="text"
          value={userInput}
          onChange={handleInputChange}
          placeholder="Type your message"
        />
        <button type="submit">Send</button>
      </form>
      <select value={selectedModel} onChange={handleModelChange}>
        {models.map((model, index) => (
          <option key={index} value={model}>
            {model}
          </option>
        ))}
      </select>
      <label>AI Response:</label>
      <label>{aiResponse}</label>
    </div>
  );
};

Once saved, run npm run dev to see your new frontend in action.

2. Adding and tracking the generate API route

Next in the app directory, create an api directory, a generate directory inside it, and a route.js file inside that. In app/api/generate/route.js, create your API route to call OpenRouter through the OpenAPI client and generate a response. We’ll use PostHog’s OpenAI provider to capture all the details of the call.

Altogether, this looks like this:

JavaScript

import { NextResponse } from 'next/server';
import { OpenAI } from '@posthog/ai'
import { PostHog } from 'posthog-node'

const phClient = new PostHog(
  '<ph_project_api_key>',
  { host: '<ph_api_client_host>' }
)

const openai = new OpenAI({
  baseURL: 'https://openrouter.ai/api/v1',
  apiKey: '<openrouter_api_key>',
  posthog: phClient,
});

export async function POST(request) {
  try {
    const body = await request.json();
    const { input, model } = body;

    const completion = await openai.chat.completions.create({
      messages: [{ role: "user", content: input }],
      model: model
    });

    return NextResponse.json({
      content: completion.choices[0].message.content
    });

  } catch (error) {
    console.error('OpenRouter API error:', error);
    return NextResponse.json(
      { error: 'There was an error processing your request' },
      { status: 500 }
    );
  }
}

Now, when you run npm run dev again, you can choose your model, enter your message, and press send to get a response. This also captures an $ai_generation event in PostHog.

3. Viewing generations in PostHog

After generating a few responses with different models, go to PostHog to access the LLM analytics dashboard to see:

Overview of all AI interactions
Cost breakdowns by model
Response latency comparisons
Token usage across different providers
Full conversation traces

Head to the generations tab to get details on each generation as well as model, cost, token usage, latency, and more. You can even see the conversation input and output.

How to set up OpenRouter LLM observability

Contents

1. Creating a Next.js app

2. Adding and tracking the generate API route

3. Viewing generations in PostHog

Further reading

Community questions