Together AI → OpenObserve

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

Prerequisites

Python 3.8+
An OpenObserve account (cloud or self-hosted)
Your OpenObserve organisation ID and Base64-encoded auth token
A Together 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_URL=https://api.openobserve.ai/
OPENOBSERVE_ORG=your_org_id
OPENOBSERVE_AUTH_TOKEN=Basic <your_base64_token>
TOGETHER_API_KEY=your-together-ai-key

Instrumentation

Call OpenAIInstrumentor().instrument() before creating the OpenAI client. Point the client at the Together AI base URL and pass your Together AI 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["TOGETHER_API_KEY"],
    base_url="https://api.together.xyz/v1",
)

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V3.1",
    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. `deepseek-ai/DeepSeek-V3.1`)
`gen_ai_response_model`	Model that served the response
`llm_provider`	`together`
`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
`llm_request_parameters_model`	Model requested
`llm_request_parameters_max_tokens`	Max tokens parameter
`openinference_span_kind`	`LLM`
`operation_name`	`ChatCompletion`
`duration`	End-to-end request latency
`span_status`	`OK` or error status

Viewing Traces

Log in to OpenObserve and navigate to Traces
Filter by operation_name = ChatCompletion to find Together AI spans
Click any span to inspect token counts and the full request/response payload
Filter by llm_model_name to compare latency across different Together AI models

Together AI trace in OpenObserve

Next Steps

With Together 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.