Skip to main content
Auto-instrumentation covers OpenAI, Anthropic, and other major providers. For unsupported providers or custom implementations, create LLM spans manually.

​
When You Need This

  • Self-hosted or fine-tuned models
  • Providers Laminar doesn’t instrument yet
  • Direct HTTP calls to LLM APIs
  • Custom inference servers

​
Supported Provider Names (for Cost Calculation)

When you manually instrument an LLM span, set the provider name to one of the supported values below so Laminar can match pricing.
ProviderProvider nameExample model
OpenAIopenaigpt-4o, gpt-4o-2024-11-20
Anthropicanthropicclaude-3-5-sonnet, claude-3-5-sonnet-20241022
Google Geminigemini, google-genaimodels/gemini-1.5-pro
Azure OpenAIazure-openaigpt-4o-mini-2024-07-18
AWS Bedrock (Anthropic)bedrock-anthropicclaude-3-5-sonnet-20241022-v2:0
Mistralmistralmistral-large-2407
Groqgroqllama-3.1-70b-versatile
If your provider isn’t listed, you can still record token usage and set explicit cost attributes (see below).

​
Required Attributes

For Laminar to calculate costs and display LLM-specific UI, set these attributes:
AttributeDescription
ProviderProvider name (e.g., openai, anthropic, custom)
Request modelModel name you requested
Response modelModel name returned by the API
Input token countNumber of input tokens
Output token countNumber of output tokens
Set spanType: 'LLM' when creating the span. Without this, the span appears as a generic operation.

​
Example: Manually Instrument an LLM Call

import { Laminar, LaminarAttributes } from '@lmnr-ai/lmnr';

const span = Laminar.startSpan({ name: 'custom_llm_call', spanType: 'LLM' });

try {
  const response = await fetch('https://api.custom-llm.com/v1/completions', {
    method: 'POST',
    body: JSON.stringify({
      model: 'custom-model-1',
      messages: [{ role: 'user', content: 'What is the longest river in the world?' }],
    }),
  }).then((res) => res.json());

  span.setAttributes({
    [LaminarAttributes.PROVIDER]: 'custom-provider',
    [LaminarAttributes.REQUEST_MODEL]: 'custom-model-1',
    [LaminarAttributes.RESPONSE_MODEL]: response.model,
    [LaminarAttributes.INPUT_TOKEN_COUNT]: response.usage?.input_tokens ?? 0,
    [LaminarAttributes.OUTPUT_TOKEN_COUNT]: response.usage?.output_tokens ?? 0,
    // Optional: explicit costs (override calculated pricing)
    [LaminarAttributes.INPUT_COST]: 0.001,
    [LaminarAttributes.OUTPUT_COST]: 0.002,
    [LaminarAttributes.TOTAL_COST]: 0.003,
  });

  return response;
} catch (error) {
  span.recordException(error as Error);
  throw error;
} finally {
  span.end();
}

​
Model Name Formats

Use the exact model string as returned by the provider API.
  • OpenAI: gpt-4o, gpt-4o-mini-2024-07-18
  • Anthropic: claude-3-5-sonnet-20241022, claude-3-5-sonnet-20241022-v2:0
  • Gemini: models/gemini-1.5-pro, models/gemini-1.5-flash

​
Custom Providers and Explicit Costs

If Laminar can’t look up pricing for your provider/model, you can still attach explicit cost attributes (see the example above). If explicit cost attributes are present, they take precedence over calculated costs.

​
How Laminar Calculates Cost

Laminar computes cost from:
  • Provider name
  • Model name
  • Token counts (input/output)
For providers that support it, Laminar can also account for cached tokens when pricing is available.

​
Viewing Costs

Costs show up in the Laminar UI on:
  • Trace details (sum of all LLM calls in the trace)
  • Individual LLM spans (per-call cost)
  • Analytics dashboards (aggregated by provider/model)

​
Pricing Data

Laminar maintains current pricing for supported providers. A snapshot of pricing seed data is available in the open-source repo at frontend/lib/db/initial-data.json (table llm_prices).

​
Best Practices

  • Always set provider + response model if you want cost calculation.
  • Use exact model names from the API response (don’t “simplify” them).
  • Handle missing usage data gracefully (set token counts only if present).