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Novita AI → OpenObserve

Automatically capture token usage, latency, and model metadata for every Novita AI inference call in your Python application. Novita AI exposes an OpenAI-compatible API, so instrumentation uses the standard OpenAI instrumentor pointed at the Novita endpoint.

Prerequisites

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

Installation

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

Configuration

Create a .env file in your project root:

# OpenObserve instance URL
# Default for self-hosted: http://localhost:5080
OPENOBSERVE_URL=https://api.openobserve.ai/

# Your OpenObserve organisation slug or ID
OPENOBSERVE_ORG=your_org_id

# Basic auth token — Base64-encoded "email:password"
OPENOBSERVE_AUTH_TOKEN=Basic <your_base64_token>

# Novita AI API key
NOVITA_API_KEY=your-novita-api-key

Instrumentation

Call OpenAIInstrumentor().instrument() before creating the OpenAI client. Point the client at the Novita AI base URL and pass your Novita API key.

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["NOVITA_API_KEY"],
    base_url="https://api.novita.ai/v3/openai",
)

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

What Gets Captured

Attribute Description
llm_model_name Model name (e.g. meta-llama/llama-3.1-8b-instruct)
gen_ai_response_model Model that served the response
llm_token_count_prompt Tokens in the prompt
llm_token_count_completion Tokens in the response
llm_token_count_total Total tokens consumed
llm_system openai (the client library used)
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 and the full request payload
  3. Filter by llm_model_name to compare latency across different Novita-hosted models

Novita AI trace in OpenObserve

Next Steps

With Novita AI instrumented, every inference call is recorded in OpenObserve. From here you can compare token throughput across open-source models, monitor latency per model variant, and set alerts on error spans.

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