> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dodopayments.com/llms.txt
> Use this file to discover all available pages before exploring further.

# LLM Blueprint

> Effortlessly track LLM token usage for usage-based billing with automatic ingestion to Dodo Payments. Works with AI SDK, OpenAI, Anthropic, OpenRouter, Groq, and Google Gemini.

<CardGroup cols={2}>
  <Card title="Quick Start" icon="rocket" href="#quick-start">
    Get started in 2 minutes with automatic token tracking.
  </Card>

  <Card title="API Reference - Events Ingestion" icon="code" href="/api-reference/usage-events/ingest-events">
    Complete API documentation for ingesting usage events.
  </Card>

  <Card title="API Reference - Meters" icon="gauge" href="/api-reference/meters/create-meter">
    Learn how to create and configure meters for billing.
  </Card>

  <Card title="Usage-Based Billing Guide" icon="arrow-trend-up" href="/developer-resources/usage-based-billing-guide">
    Comprehensive guide to usage-based billing with meters.
  </Card>
</CardGroup>

<Info>
  Perfect for SaaS apps, AI chatbots, content generation tools, and any LLM-powered application that needs usage-based billing.
</Info>

## Quick Start

Get started with automatic LLM token tracking in just 2 minutes:

<Steps>
  <Step title="Install the SDK">
    Install the Dodo Payments Ingestion Blueprints:

    ```bash theme={null}
    npm install @dodopayments/ingestion-blueprints
    ```
  </Step>

  <Step title="Get Your API Keys">
    You'll need two API keys:

    * **Dodo Payments API Key**: Get it from [Dodo Payments Dashboard](https://app.dodopayments.com/developer/api-keys)
    * **LLM Provider API Key**: From AI SDK, OpenAI, Anthropic, Groq, etc.

    <Tip>
      Store your API keys securely in environment variables. Never commit them to version control.
    </Tip>
  </Step>

  <Step title="Create a Meter in Dodo Payments">
    Before tracking usage, create a meter in your Dodo Payments dashboard:

    1. **Login** to [Dodo Payments Dashboard](https://app.dodopayments.com/)
    2. **Navigate to** Products → Meters
    3. **Click** "Create Meter"
    4. **Configure your meter**:
       * **Meter Name**: Choose a descriptive name (e.g., "LLM Token Usage")
       * **Event Name**: Set a unique event identifier (e.g., `llm.chat_completion`)
       * **Aggregation Type**: Select `sum` to add up token counts
       * **Over Property**: Choose what to track:
         * `inputTokens` - Track input/prompt tokens
         * `outputTokens` - Track output/completion tokens (includes reasoning tokens when applicable)
         * `totalTokens` - Track combined input + output tokens

    <Info>
      The **Event Name** you set here must match exactly what you pass to the SDK (case-sensitive).
    </Info>

    For detailed instructions, see the [Usage-Based Billing Guide](/developer-resources/usage-based-billing-guide).
  </Step>

  <Step title="Track Token Usage">
    Wrap your LLM client and start tracking automatically:

    <CodeGroup>
      ```javascript AI SDK theme={null}
      import { createLLMTracker } from '@dodopayments/ingestion-blueprints';
      import { generateText } from 'ai';
      import { google } from '@ai-sdk/google';

      const llmTracker = createLLMTracker({
        apiKey: process.env.DODO_PAYMENTS_API_KEY,
        environment: 'test_mode',
        eventName: 'aisdk.usage',
      });

      const client = llmTracker.wrap({
        client: { generateText },
        customerId: 'customer_123'
      });

      const response = await client.generateText({
        model: google('gemini-2.0-flash'),
        prompt: 'Hello!',
        maxOutputTokens: 500
      });

      console.log('Usage:', response.usage);
      ```

      ```javascript OpenRouter theme={null}
      import { createLLMTracker } from '@dodopayments/ingestion-blueprints';
      import OpenAI from 'openai';

      const openrouter = new OpenAI({
        baseURL: 'https://openrouter.ai/api/v1',
        apiKey: process.env.OPENROUTER_API_KEY
      });

      const llmTracker = createLLMTracker({
        apiKey: process.env.DODO_PAYMENTS_API_KEY,
        environment: 'test_mode',
        eventName: 'openrouter.usage'
      });

      const client = llmTracker.wrap({
        client: openrouter,
        customerId: 'customer_123'
      });

      const response = await client.chat.completions.create({
        model: 'qwen/qwen3-max',
        messages: [{ role: 'user', content: 'Hello!' }],
        max_tokens: 500
      });

      console.log('Response:', response.choices[0].message.content);
      console.log('Usage:', response.usage);
      ```

      ```javascript OpenAI theme={null}
      import { createLLMTracker } from '@dodopayments/ingestion-blueprints';
      import OpenAI from 'openai';

      // 1. Create your LLM client (normal way)
      const openai = new OpenAI({ 
        apiKey: process.env.OPENAI_API_KEY 
      });

      // 2. Create tracker ONCE at startup
      const tracker = createLLMTracker({
        apiKey: process.env.DODO_PAYMENTS_API_KEY,
        environment: 'test_mode', // Use 'live_mode' for production
        eventName: 'llm.chat_completion' // Match your meter's event name
      });

      // 3. Wrap & use - automatic tracking!
      const client = tracker.wrap({ 
        client: openai, 
        customerId: 'customer_123' 
      });

      // Every API call is now automatically tracked
      const response = await client.chat.completions.create({
        model: 'gpt-4',
        messages: [{ role: 'user', content: 'Hello!' }]
      });

      // ✨ Usage automatically sent to Dodo Payments!
      console.log('Tokens used:', response.usage);
      ```

      ```javascript Anthropic theme={null}
      import { createLLMTracker } from '@dodopayments/ingestion-blueprints';
      import Anthropic from '@anthropic-ai/sdk';

      const anthropic = new Anthropic({ 
        apiKey: process.env.ANTHROPIC_API_KEY 
      });

      const tracker = createLLMTracker({
        apiKey: process.env.DODO_PAYMENTS_API_KEY,
        environment: 'test_mode',
        eventName: 'anthropic.usage'
      });

      const client = tracker.wrap({ 
        client: anthropic, 
        customerId: 'customer_123' 
      });

      const response = await client.messages.create({
        model: 'claude-sonnet-4-0',
        max_tokens: 1024,
        messages: [{ role: 'user', content: 'Hello Claude!' }]
      });

      console.log('Tokens used:', response.usage);
      ```

      ```javascript Groq theme={null}
      import { createLLMTracker } from '@dodopayments/ingestion-blueprints';
      import Groq from 'groq-sdk';

      const groq = new Groq({ 
        apiKey: process.env.GROQ_API_KEY 
      });

      const tracker = createLLMTracker({
        apiKey: process.env.DODO_PAYMENTS_API_KEY,
        environment: 'test_mode',
        eventName: 'groq.usage'
      });

      const client = tracker.wrap({ 
        client: groq, 
        customerId: 'customer_123' 
      });

      const response = await client.chat.completions.create({
        model: 'llama-3.1-8b-instant',
        messages: [{ role: 'user', content: 'Hello!' }]
      });

      console.log('Tokens:', response.usage);
      ```

      ```javascript Google Gemini theme={null}
      import { createLLMTracker } from '@dodopayments/ingestion-blueprints';
      import { GoogleGenAI } from '@google/genai';

      const googleGenai = new GoogleGenAI({
        apiKey: process.env.GOOGLE_GENERATIVE_AI_API_KEY
      });

      const llmTracker = createLLMTracker({
        apiKey: process.env.DODO_PAYMENTS_API_KEY,
        environment: 'test_mode',
        eventName: 'gemini.usage'
      });

      const client = llmTracker.wrap({
        client: googleGenai,
        customerId: 'customer_123'
      });

      const response = await client.models.generateContent({
        model: 'gemini-2.5-flash',
        contents: 'Why is the sky blue?'
      });

      console.log('Response:', response.text);
      console.log('Usage:', response.usageMetadata);
      ```
    </CodeGroup>

    <Check>
      That's it! Every API call now automatically tracks token usage and sends events to Dodo Payments for billing.
    </Check>
  </Step>
</Steps>

***

## Configuration

### Tracker Configuration

Create a tracker once at application startup with these required parameters:

<ParamField path="apiKey" type="string" required>
  Your Dodo Payments API key. Get it from the [API Keys page](https://app.dodopayments.com/developer/api-keys).

  ```javascript theme={null}
  apiKey: process.env.DODO_PAYMENTS_API_KEY
  ```
</ParamField>

<ParamField path="environment" type="string" required>
  The environment mode for the tracker.

  * `test_mode` - Use for development and testing
  * `live_mode` - Use for production

  ```javascript theme={null}
  environment: 'test_mode' // or 'live_mode'
  ```

  <Warning>
    Always use `test_mode` during development to avoid affecting production metrics.
  </Warning>
</ParamField>

<ParamField path="eventName" type="string" required>
  The event name that triggers your meter. Must match exactly what you configured in your Dodo Payments meter (case-sensitive).

  ```javascript theme={null}
  eventName: 'llm.chat_completion'
  ```

  <Info>
    This event name links your tracked usage to the correct meter for billing calculations.
  </Info>
</ParamField>

### Wrapper Configuration

When wrapping your LLM client, provide these parameters:

<ParamField path="client" type="object" required>
  Your LLM client instance (OpenAI, Anthropic, Groq, etc.).

  ```javascript theme={null}
  client: openai
  ```
</ParamField>

<ParamField path="customerId" type="string" required>
  The unique customer identifier for billing. This should match your customer ID in Dodo Payments.

  ```javascript theme={null}
  customerId: 'customer_123'
  ```

  <Tip>
    Use your application's user ID or customer ID to ensure accurate billing per customer.
  </Tip>
</ParamField>

<ParamField path="metadata" type="object">
  Optional additional data to attach to the tracking event. Useful for filtering and analysis.

  ```javascript theme={null}
  metadata: {
    feature: 'chat',
    userTier: 'premium',
    sessionId: 'session_123',
    modelVersion: 'gpt-4'
  }
  ```
</ParamField>

### Complete Configuration Example

<CodeGroup>
  ```javascript Full Configuration theme={null}
  import { createLLMTracker } from "@dodopayments/ingestion-blueprints";
  import { generateText } from "ai";
  import { google } from "@ai-sdk/google";
  import "dotenv/config";

  async function aiSdkExample() {
    console.log("🤖 AI SDK Simple Usage Example\n");

    try {
      // 1. Create tracker
      const llmTracker = createLLMTracker({
        apiKey: process.env.DODO_PAYMENTS_API_KEY!,
        environment: "test_mode",
        eventName: "your_meter_event_name",
      });

      // 2. Wrap the ai-sdk methods
      const client = llmTracker.wrap({
        client: { generateText },
        customerId: "customer_123",
        metadata: {
          provider: "ai-sdk",
        },
      });

      // 3. Use the wrapped function
      const response = await client.generateText({
        model: google("gemini-2.5-flash"),
        prompt: "Hello, I am a cool guy! Tell me a fun fact.",
        maxOutputTokens: 500,
      });

      console.log(response);
      console.log(response.usage);
      console.log("✅ Automatically tracked for customer\n");
    } catch (error) {
      console.error(error);
    }
  }

  aiSdkExample().catch(console.error);
  ```
</CodeGroup>

<Info>
  **Automatic Tracking:** The SDK automatically tracks token usage in the background without modifying the response. Your code remains clean and identical to using the original provider SDKs.
</Info>

***

## Supported Providers

The LLM Blueprint works seamlessly with all major LLM providers and aggregators:

<AccordionGroup>
  <Accordion title="AI SDK (Vercel)" icon="code">
    Track usage with the Vercel AI SDK for universal LLM support.

    <CodeGroup>
      ```javascript AI SDK Integration theme={null}
      import { createLLMTracker } from '@dodopayments/ingestion-blueprints';
      import { generateText } from 'ai';
      import { google } from '@ai-sdk/google';

      const llmTracker = createLLMTracker({
        apiKey: process.env.DODO_PAYMENTS_API_KEY,
        environment: 'test_mode',
        eventName: 'aisdk.usage',
      });

      const client = llmTracker.wrap({
        client: { generateText },
        customerId: 'customer_123',
        metadata: {
          model: 'gemini-2.0-flash',
          feature: 'chat'
        }
      });

      const response = await client.generateText({
        model: google('gemini-2.0-flash'),
        prompt: 'Explain neural networks',
        maxOutputTokens: 500
      });

      console.log('Usage:', response.usage);
      ```
    </CodeGroup>

    **Tracked Metrics:**

    * `inputTokens` → `inputTokens`
    * `outputTokens` + `reasoningTokens` → `outputTokens`
    * `totalTokens` → `totalTokens`
    * Model name

    <Note>
      When using reasoning-capable models through AI SDK (like Google's Gemini 2.5 Flash with thinking mode), reasoning tokens are automatically included in the `outputTokens` count for accurate billing.
    </Note>
  </Accordion>

  <Accordion title="OpenRouter" icon="route">
    Track token usage across 200+ models via OpenRouter's unified API.

    <CodeGroup>
      ```javascript OpenRouter Integration theme={null}
      import { createLLMTracker } from '@dodopayments/ingestion-blueprints';
      import OpenAI from 'openai';

      // OpenRouter uses OpenAI-compatible API
      const openrouter = new OpenAI({
        baseURL: 'https://openrouter.ai/api/v1',
        apiKey: process.env.OPENROUTER_API_KEY
      });

      const tracker = createLLMTracker({
        apiKey: process.env.DODO_PAYMENTS_API_KEY,
        environment: 'test_mode',
        eventName: 'openrouter.usage'
      });

      const client = tracker.wrap({ 
        client: openrouter, 
        customerId: 'user_123',
        metadata: { provider: 'openrouter' }
      });

      const response = await client.chat.completions.create({
        model: 'qwen/qwen3-max',
        messages: [{ role: 'user', content: 'What is machine learning?' }],
        max_tokens: 500
      });

      console.log('Response:', response.choices[0].message.content);
      console.log('Usage:', response.usage);
      ```
    </CodeGroup>

    **Tracked Metrics:**

    * `prompt_tokens` → `inputTokens`
    * `completion_tokens` → `outputTokens`
    * `total_tokens` → `totalTokens`
    * Model name

    <Tip>
      OpenRouter provides access to models from OpenAI, Anthropic, Google, Meta, and many more providers through a single API.
    </Tip>
  </Accordion>

  <Accordion title="OpenAI" icon="robot">
    Track token usage from OpenAI's GPT models automatically.

    <CodeGroup>
      ```javascript OpenAI Integration theme={null}
      import { createLLMTracker } from '@dodopayments/ingestion-blueprints';
      import OpenAI from 'openai';

      const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

      const tracker = createLLMTracker({
        apiKey: process.env.DODO_PAYMENTS_API_KEY,
        environment: 'test_mode',
        eventName: 'openai.usage'
      });

      const client = tracker.wrap({ 
        client: openai, 
        customerId: 'user_123' 
      });

      // All OpenAI methods work automatically
      const response = await client.chat.completions.create({
        model: 'gpt-4',
        messages: [{ role: 'user', content: 'Explain quantum computing' }]
      });

      console.log('Total tokens:', response.usage.total_tokens);
      ```
    </CodeGroup>

    **Tracked Metrics:**

    * `prompt_tokens` → `inputTokens`
    * `completion_tokens` → `outputTokens`
    * `total_tokens` → `totalTokens`
    * Model name
  </Accordion>

  <Accordion title="Anthropic Claude" icon="robot">
    Track token usage from Anthropic's Claude models.

    <CodeGroup>
      ```javascript Anthropic Integration theme={null}
      import { createLLMTracker } from '@dodopayments/ingestion-blueprints';
      import Anthropic from '@anthropic-ai/sdk';

      const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });

      const tracker = createLLMTracker({
        apiKey: process.env.DODO_PAYMENTS_API_KEY,
        environment: 'test_mode',
        eventName: 'anthropic.usage'
      });

      const client = tracker.wrap({ 
        client: anthropic, 
        customerId: 'user_123' 
      });

      const response = await client.messages.create({
        model: 'claude-sonnet-4-0',
        max_tokens: 1024,
        messages: [{ role: 'user', content: 'Explain machine learning' }]
      });

      console.log('Input tokens:', response.usage.input_tokens);
      console.log('Output tokens:', response.usage.output_tokens);
      ```
    </CodeGroup>

    **Tracked Metrics:**

    * `input_tokens` → `inputTokens`
    * `output_tokens` → `outputTokens`
    * Calculated `totalTokens`
    * Model name
  </Accordion>

  <Accordion title="Groq" icon="gauge-high">
    Track ultra-fast LLM inference with Groq.

    <CodeGroup>
      ```javascript Groq Integration theme={null}
      import { createLLMTracker } from '@dodopayments/ingestion-blueprints';
      import Groq from 'groq-sdk';

      const groq = new Groq({ apiKey: process.env.GROQ_API_KEY });

      const tracker = createLLMTracker({
        apiKey: process.env.DODO_PAYMENTS_API_KEY,
        environment: 'test_mode',
        eventName: 'groq.usage'
      });

      const client = tracker.wrap({ 
        client: groq, 
        customerId: 'user_123' 
      });

      const response = await client.chat.completions.create({
        model: 'llama-3.1-8b-instant',
        messages: [{ role: 'user', content: 'What is AI?' }]
      });

      console.log('Tokens:', response.usage);
      ```
    </CodeGroup>

    **Tracked Metrics:**

    * `prompt_tokens` → `inputTokens`
    * `completion_tokens` → `outputTokens`
    * `total_tokens` → `totalTokens`
    * Model name
  </Accordion>

  <Accordion title="Google Gemini" icon="sparkles">
    Track token usage from Google's Gemini models via the Google GenAI SDK.

    <CodeGroup>
      ```javascript Google Gemini Integration theme={null}
      import { createLLMTracker } from '@dodopayments/ingestion-blueprints';
      import { GoogleGenAI } from '@google/genai';

      const googleGenai = new GoogleGenAI({ 
        apiKey: process.env.GOOGLE_GENERATIVE_AI_API_KEY 
      });

      const tracker = createLLMTracker({
        apiKey: process.env.DODO_PAYMENTS_API_KEY,
        environment: 'test_mode',
        eventName: 'gemini.usage'
      });

      const client = tracker.wrap({ 
        client: googleGenai, 
        customerId: 'user_123' 
      });

      const response = await client.models.generateContent({
        model: 'gemini-2.5-flash',
        contents: 'Explain quantum computing'
      });

      console.log('Response:', response.text);
      console.log('Usage:', response.usageMetadata);
      ```
    </CodeGroup>

    **Tracked Metrics:**

    * `promptTokenCount` → `inputTokens`
    * `candidatesTokenCount` + `thoughtsTokenCount` → `outputTokens`
    * `totalTokenCount` → `totalTokens`
    * Model version

    <Note>
      **Gemini Thinking Mode:** When using Gemini models with thinking/reasoning capabilities (like Gemini 2.5 Pro), the SDK automatically includes `thoughtsTokenCount` (reasoning tokens) in `outputTokens` to accurately reflect the full computational cost.
    </Note>
  </Accordion>
</AccordionGroup>

***

## Advanced Usage

### Multiple Providers

Track usage across different LLM providers with separate trackers:

<CodeGroup>
  ```javascript Multiple Provider Setup theme={null}
  import { createLLMTracker } from '@dodopayments/ingestion-blueprints';
  import OpenAI from 'openai';
  import Groq from 'groq-sdk';
  import Anthropic from '@anthropic-ai/sdk';
  import { GoogleGenAI } from '@google/genai';

  // Create separate trackers for different providers
  const openaiTracker = createLLMTracker({
    apiKey: process.env.DODO_PAYMENTS_API_KEY,
    environment: 'live_mode',
    eventName: 'openai.usage'
  });

  const groqTracker = createLLMTracker({
    apiKey: process.env.DODO_PAYMENTS_API_KEY,
    environment: 'live_mode',
    eventName: 'groq.usage'
  });

  const anthropicTracker = createLLMTracker({
    apiKey: process.env.DODO_PAYMENTS_API_KEY,
    environment: 'live_mode',
    eventName: 'anthropic.usage'
  });

  const geminiTracker = createLLMTracker({
    apiKey: process.env.DODO_PAYMENTS_API_KEY,
    environment: 'live_mode',
    eventName: 'gemini.usage'
  });

  const openrouterTracker = createLLMTracker({
    apiKey: process.env.DODO_PAYMENTS_API_KEY,
    environment: 'live_mode',
    eventName: 'openrouter.usage'
  });

  // Initialize clients
  const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
  const groq = new Groq({ apiKey: process.env.GROQ_API_KEY });
  const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
  const googleGenai = new GoogleGenAI({ apiKey: process.env.GOOGLE_GENERATIVE_AI_API_KEY });
  const openrouter = new OpenAI({ 
    baseURL: 'https://openrouter.ai/api/v1',
    apiKey: process.env.OPENROUTER_API_KEY 
  });

  // Wrap clients
  const trackedOpenAI = openaiTracker.wrap({ client: openai, customerId: 'user_123' });
  const trackedGroq = groqTracker.wrap({ client: groq, customerId: 'user_123' });
  const trackedAnthropic = anthropicTracker.wrap({ client: anthropic, customerId: 'user_123' });
  const trackedGemini = geminiTracker.wrap({ client: googleGenai, customerId: 'user_123' });
  const trackedOpenRouter = openrouterTracker.wrap({ client: openrouter, customerId: 'user_123' });

  // Use whichever provider you need
  const response = await trackedOpenAI.chat.completions.create({...});
  // or
  const geminiResponse = await trackedGemini.models.generateContent({...});
  // or
  const openrouterResponse = await trackedOpenRouter.chat.completions.create({...});
  ```
</CodeGroup>

<Tip>
  Use different event names for different providers to track usage separately in your meters.
</Tip>

### Express.js API Integration

Complete example of integrating LLM tracking into an Express.js API:

<CodeGroup>
  ```javascript Express.js Server theme={null}
  import express from 'express';
  import { createLLMTracker } from '@dodopayments/ingestion-blueprints';
  import OpenAI from 'openai';

  const app = express();
  app.use(express.json());

  // Initialize OpenAI client
  const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

  // Create tracker once at startup
  const tracker = createLLMTracker({
    apiKey: process.env.DODO_PAYMENTS_API_KEY,
    environment: process.env.NODE_ENV === 'production' ? 'live_mode' : 'test_mode',
    eventName: 'api.chat_completion'
  });

  // Chat endpoint with automatic tracking
  app.post('/api/chat', async (req, res) => {
    try {
      const { message, userId } = req.body;
      
      // Validate input
      if (!message || !userId) {
        return res.status(400).json({ error: 'Missing message or userId' });
      }
      
      // Wrap client for this specific user
      const trackedClient = tracker.wrap({
        client: openai,
        customerId: userId,
        metadata: { 
          endpoint: '/api/chat',
          timestamp: new Date().toISOString()
        }
      });
      
      // Make LLM request - automatically tracked
      const response = await trackedClient.chat.completions.create({
        model: 'gpt-4',
        messages: [{ role: 'user', content: message }],
        temperature: 0.7
      });
      
      const completion = response.choices[0].message.content;
      
      res.json({ 
        message: completion,
        usage: response.usage
      });
    } catch (error) {
      console.error('Chat error:', error);
      res.status(500).json({ error: 'Internal server error' });
    }
  });

  app.listen(3000, () => {
    console.log('Server running on port 3000');
  });
  ```
</CodeGroup>

***

## What Gets Tracked

Every LLM API call automatically sends a usage event to Dodo Payments with the following structure:

<CodeGroup>
  ```json Event Structure theme={null}
  {
    "event_id": "llm_1673123456_abc123",
    "customer_id": "customer_123",
    "event_name": "llm.chat_completion",
    "timestamp": "2024-01-08T10:30:00Z",
    "metadata": {
      "inputTokens": 10,
      "outputTokens": 25,
      "totalTokens": 35,
      "model": "gpt-4",
    }
  }
  ```
</CodeGroup>

### Event Fields

<ParamField path="event_id" type="string">
  Unique identifier for this specific event. Automatically generated by the SDK.

  Format: `llm_[timestamp]_[random]`
</ParamField>

<ParamField path="customer_id" type="string">
  The customer ID you provided when wrapping the client. Used for billing.
</ParamField>

<ParamField path="event_name" type="string">
  The event name that triggers your meter. Matches your tracker configuration.
</ParamField>

<ParamField path="timestamp" type="string">
  ISO 8601 timestamp when the event occurred.
</ParamField>

<ParamField path="metadata" type="object">
  Token usage and additional tracking data:

  * `inputTokens` - Number of input/prompt tokens used
  * `outputTokens` - Number of output/completion tokens used (includes reasoning tokens when applicable)
  * `totalTokens` - Total tokens (input + output)
  * `model` - The LLM model used (e.g., "gpt-4")
  * `provider` - The LLM provider (if included in wrapper metadata)
  * Any custom metadata you provided when wrapping the client

  <Note>
    **Reasoning Tokens:** For models with reasoning capabilities, `outputTokens` automatically includes both the completion tokens and reasoning tokens.
  </Note>
</ParamField>

<Info>
  Your Dodo Payments meter uses the `metadata` fields (especially `inputTokens`, `outputTokens` or `totalTokens`) to calculate usage and billing.
</Info>

***