BytePlus ModelArk → OpenObserve

Capture LLM call latency, token usage, model name, input messages, and output content for every BytePlus ModelArk inference call. BytePlus ModelArk exposes an OpenAI-compatible API. Instrumentation uses openinference-instrumentation-openai to automatically patch the OpenAI SDK pointed at the ModelArk endpoint and export spans to OpenObserve via OTLP.

Prerequisites

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

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>
BYTEPLUS_API_KEY=your-byteplus-api-key
BYTEPLUS_BASE_URL=https://ark.ap-southeast.bytepluses.com/api/v3
BYTEPLUS_ENDPOINT_ID=ep-xxxxxxxxxxxxxxxx-xxxxx

Create your API key under ModelArk Console → API Key Management and your endpoint ID under ModelArk Console → Online Inference → Create Inference Endpoint.

Instrumentation

Call OpenAIInstrumentor().instrument() before openobserve_init(), then import and configure the OpenAI client with the BytePlus base URL. Every chat.completions.create call is automatically traced.

from dotenv import load_dotenv
load_dotenv()

from openinference.instrumentation.openai import OpenAIInstrumentor
OpenAIInstrumentor().instrument()

from openobserve import openobserve_init, openobserve_shutdown
openobserve_init(resource_attributes={"service.name": "byteplus"})

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["BYTEPLUS_API_KEY"],
    base_url=os.environ["BYTEPLUS_BASE_URL"],
)

model = os.environ["BYTEPLUS_ENDPOINT_ID"]

response = client.chat.completions.create(
    model=model,
    messages=[{"role": "user", "content": "What is distributed tracing?"}],
    max_tokens=256,
)
print(response.choices[0].message.content)

openobserve_shutdown()

Run with:

python3 main.py

What Gets Captured

Viewing Traces

1. Log in to OpenObserve and navigate to Traces
2. Filter by service_name = byteplus to isolate BytePlus spans
3. Click any ChatCompletion span to inspect token counts and the resolved model name
4. Check llm_token_count_completion_details_reasoning to see how many tokens the model spent on reasoning
5. Filter by span_status = ERROR to find authentication or endpoint failures

Next Steps

With BytePlus ModelArk instrumented, every inference call is recorded in OpenObserve. From here you can build dashboards tracking token consumption over time, compare reasoning token usage across models, and set alerts on error rates or latency thresholds.

Read More

• LLM Observability Overview
• OpenAI (Python)
• Exploring Traces in OpenObserve
• Building Dashboards