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Ramp is a finance automation platform that helps businesses manage expenses, track spending, and optimize costs. With Ramp’s AI usage tracking, you can monitor and control your organization’s LLM spending through OpenRouter.

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Step 1: Get your Ramp API key

In Ramp, navigate to your integration settings and generate an API key:
  1. Log in to your Ramp account
  2. Go to Settings > Integrations and search for “OpenRouter”
Search for OpenRouter integration
  1. Click the OpenRouter integration to view the details, then click Connect
OpenRouter integration detail
  1. Click Generate API Key and copy the token
Generate API Key

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Step 2: Enable Broadcast in OpenRouter

Go to Settings > Observability and toggle Enable Broadcast.
Enable Broadcast

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Step 3: Configure Ramp

Click the edit icon next to Ramp and enter:
  • API Key: Your Ramp API key
  • Base URL (optional): Default is https://api.ramp.com/developer/v1/ai-usage/openrouter. Only change if directed by Ramp
  • Headers (optional): Custom HTTP headers as a JSON object to include in requests to Ramp
Ramp Configuration

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Step 4: Test and save

Click Test Connection to verify the setup. The configuration only saves if the test passes.

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Step 5: Send a test trace

Make an API request through OpenRouter and verify that the AI usage data appears in your Ramp dashboard.
Ramp AI Spend Dashboard

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Trace Data

Ramp receives traces via the OpenTelemetry Protocol (OTLP). Each trace includes:
  • Token usage: Prompt tokens, completion tokens, and total tokens consumed
  • Cost information: The total cost of the request
  • Timing: Request start time, end time, and latency metrics
  • Model information: The model slug and provider name used for the request
  • Request and response content: The input messages and model output (unless Privacy Mode is enabled)

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Custom Metadata

Custom metadata from the trace field is sent as span attributes in the OTLP payload.

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Supported Metadata Keys

KeyOTLP MappingDescription
trace_idTrace IDGroup multiple requests into a single trace
trace_nameSpan NameCustom name for the root span
span_nameSpan NameName for intermediate spans in the hierarchy
generation_nameSpan NameName for the LLM generation span
parent_span_idParent Span IDLink to an existing span in your trace hierarchy

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Example

{
  "model": "openai/gpt-4o",
  "messages": [{ "role": "user", "content": "Analyze this expense report..." }],
  "user": "user_12345",
  "session_id": "session_abc",
  "trace": {
    "trace_id": "expense_analysis_001",
    "trace_name": "Expense Processing Pipeline",
    "generation_name": "Analyze Report",
    "department": "finance",
    "cost_center": "CC-1234"
  }
}

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Additional Context

  • The user field maps to user.id in span attributes
  • The session_id field maps to session.id in span attributes
  • Custom metadata keys from trace are included as span attributes under the trace.metadata.* namespace
  • Standard GenAI semantic conventions (gen_ai.*) are used for model, token usage, and cost attributes

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Privacy Mode

When Privacy Mode is enabled for this destination, prompt and completion content is excluded from traces. All other trace data — token usage, costs, timing, model information, and custom metadata — is still sent normally. See Privacy Mode for details.