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Groq → OpenObserve

Automatically capture token usage, latency, and model metadata for every Groq inference call in your Python application.

Prerequisites

  • Python 3.9+
  • A Groq account with an API key
  • An OpenObserve account (cloud or self-hosted)
  • Your OpenObserve organisation ID and Base64-encoded auth token

Installation

pip install openobserve-telemetry-sdk openinference-instrumentation-groq groq python-dotenv

Configuration

Create a .env file in your project root:

OPENOBSERVE_URL=https://api.openobserve.ai/
OPENOBSERVE_ORG=your_org_id
OPENOBSERVE_AUTH_TOKEN=Basic <your_base64_token>
GROQ_API_KEY=your-groq-api-key

Instrumentation

Call GroqInstrumentor().instrument() before importing the Groq client.

from dotenv import load_dotenv
load_dotenv()

from openinference.instrumentation.groq import GroqInstrumentor
from openobserve import openobserve_init

GroqInstrumentor().instrument()
openobserve_init()

import os
from groq import Groq

client = Groq(api_key=os.environ["GROQ_API_KEY"])

response = client.chat.completions.create(
    model="llama-3.1-8b-instant",
    messages=[{"role": "user", "content": "Explain observability in one sentence."}],
)
print(response.choices[0].message.content)

What Gets Captured

Attribute Description
llm_model_name Model used (e.g. llama-3.1-8b-instant)
gen_ai_response_model Model that served the response
llm_usage_tokens_input Prompt token count
llm_usage_tokens_output Completion token count
llm_usage_tokens_total Total tokens consumed
openinference_span_kind LLM
operation_name Completions
span_status OK or ERROR
status_message Error details if the request failed
duration Request latency

Viewing Traces

  1. Log in to OpenObserve and navigate to Traces in the left sidebar
  2. Filter by llm_model_name to find Groq spans
  3. Click any span to inspect token counts, model name, and latency
  4. Error spans show span_status = ERROR with the full error message in status_message

Groq trace in OpenObserve

Next Steps

With Groq instrumented, every inference call is recorded in OpenObserve. From here you can compare latency across Groq models, track token consumption over time, and set alerts on error rates.

Read More