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DeepSeek → OpenObserve

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

Prerequisites

  • Python 3.8+
  • An OpenObserve account (cloud or self-hosted)
  • Your OpenObserve organisation ID and Base64-encoded auth token
  • A DeepSeek 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>
DEEPSEEK_API_KEY=your-deepseek-api-key

Instrumentation

Call OpenAIInstrumentor().instrument() before creating the OpenAI client. Point the client at the DeepSeek base URL and pass your DeepSeek 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["DEEPSEEK_API_KEY"],
    base_url="https://api.deepseek.com/v1",
)

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

What Gets Captured

Attribute Description
llm_provider deepseek
llm_system openai (OpenAI-compatible client)
llm_model_name Resolved model returned by the API (e.g. deepseek-v4-flash)
llm_request_parameters_model Model name sent in the request (e.g. deepseek-chat)
llm_request_parameters_max_tokens max_tokens value from the request
gen_ai_response_model Same as llm_model_name
llm_observation_type GENERATION
llm_token_count_prompt Prompt tokens consumed
llm_token_count_completion Completion tokens returned
llm_token_count_total Total tokens consumed
llm_token_count_prompt_details_cache_read Prompt cache read tokens
llm_usage_tokens_input Input tokens (mirrors llm_token_count_prompt)
llm_usage_tokens_output Output tokens (mirrors llm_token_count_completion)
llm_usage_tokens_total Total tokens (mirrors llm_token_count_total)
openinference_span_kind LLM
operation_name ChatCompletion
input_mime_type application/json
output_mime_type application/json
duration End-to-end request latency
span_status OK on success, ERROR on failure

Viewing Traces

  1. Log in to OpenObserve and navigate to Traces
  2. Spans appear with operation_name: ChatCompletion and llm_provider: deepseek
  3. Note that deepseek-chat resolves to the actual model in llm_model_name (e.g. deepseek-v4-flash)
  4. Filter by llm_provider to isolate DeepSeek spans from other providers

DeepSeek trace in OpenObserve

Next Steps

With DeepSeek instrumented, every inference call is recorded in OpenObserve. From here you can monitor token usage, compare latency across model aliases, and set alerts on error spans.

Read More