Skip to content
OpenObserve
Get Demo Star
OpenObserve
Get Demo Star
Open Menu Close Menu

Portkey → OpenObserve

Automatically capture token usage, latency, and model metadata for every request routed through the Portkey AI gateway. Portkey exposes an OpenAI-compatible API, so instrumentation uses the standard OpenAI instrumentor pointed at the Portkey endpoint.

Prerequisites

  • Python 3.8+
  • An OpenObserve account (cloud or self-hosted)
  • Your OpenObserve organisation ID and Base64-encoded auth token
  • A Portkey API key and a configured provider

Installation

pip install openobserve-telemetry-sdk openinference-instrumentation-openai openai 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>
PORTKEY_API_KEY=your-portkey-api-key
OPENAI_API_KEY=your-openai-api-key

Instrumentation

Call OpenAIInstrumentor().instrument() before creating the OpenAI client. Pass the Portkey API key and provider routing headers.

from dotenv import load_dotenv
load_dotenv()

from openinference.instrumentation.openai import OpenAIInstrumentor
from openobserve import openobserve_init

OpenAIInstrumentor().instrument()
openobserve_init()

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["PORTKEY_API_KEY"],
    base_url="https://api.portkey.ai/v1",
    default_headers={
        "x-portkey-api-key": os.environ["PORTKEY_API_KEY"],
        "x-portkey-provider": "openai",
        "Authorization": f"Bearer {os.environ['OPENAI_API_KEY']}",
    },
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Explain distributed tracing in one sentence."}],
)
print(response.choices[0].message.content)

Using Portkey virtual keys

If you have configured virtual keys in Portkey, you can use them instead of passing provider credentials directly:

client = OpenAI(
    api_key=os.environ["PORTKEY_API_KEY"],
    base_url="https://api.portkey.ai/v1",
    default_headers={
        "x-portkey-api-key": os.environ["PORTKEY_API_KEY"],
        "x-portkey-virtual-key": os.environ["PORTKEY_VIRTUAL_KEY"],
    },
)

What Gets Captured

Attribute Description
llm_model_name Resolved model name (e.g. gpt-4o-mini-2024-07-18)
llm_request_parameters_model Requested model (e.g. gpt-4o-mini)
gen_ai_response_model Model that served the response
llm_system openai (the client library used)
llm_token_count_prompt Tokens in the prompt
llm_token_count_completion Tokens in the response
llm_token_count_total Total tokens consumed
openinference_span_kind LLM
operation_name ChatCompletion
duration End-to-end request latency
span_status OK or error status

Viewing Traces

  1. Log in to OpenObserve and navigate to Traces
  2. Click any span to inspect token counts, model, and latency
  3. Use llm_model_name to compare different providers routed through Portkey

Portkey trace in OpenObserve

Next Steps

With Portkey instrumented, every routed request is recorded in OpenObserve. From here you can compare latency and cost across providers, monitor fallback behaviour, and set alerts on error rates.

Read More