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Databricks → OpenObserve

Automatically capture token usage, latency, and model metadata for every call to Databricks Model Serving endpoints. Databricks Model Serving exposes an OpenAI-compatible API, so instrumentation uses the standard OpenAI instrumentor pointed at your workspace serving endpoint.

Prerequisites

  • Python 3.8+
  • An OpenObserve account (cloud or self-hosted)
  • Your OpenObserve organisation ID and Base64-encoded auth token
  • A Databricks workspace with Model Serving enabled
  • A Databricks personal access token with model-serving permissions

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>
DATABRICKS_HOST=https://adb-1234567890123456.7.azuredatabricks.net
DATABRICKS_TOKEN=dapiXXXXXXXXXXXXXXXXXXXXXXXXXXXX
DATABRICKS_MODEL=databricks-llama-4-maverick

DATABRICKS_HOST is your workspace URL. DATABRICKS_MODEL is the serving endpoint name: either a Foundation Model API name (e.g. databricks-llama-4-maverick, databricks-claude-sonnet-4-6) or a custom endpoint name from your workspace.

Instrumentation

Call OpenAIInstrumentor().instrument() before creating the OpenAI client. Authenticate with your Databricks personal access token and point the client at the workspace serving endpoint.

from dotenv import load_dotenv
load_dotenv()

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

OpenAIInstrumentor().instrument()
openobserve_init()

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["DATABRICKS_TOKEN"],
    base_url=f"{os.environ['DATABRICKS_HOST'].rstrip('/')}/serving-endpoints",
)

response = client.chat.completions.create(
    model=os.environ.get("DATABRICKS_MODEL", "databricks-llama-4-maverick"),
    messages=[{"role": "user", "content": "Explain distributed tracing in one sentence."}],
    max_tokens=100,
)
print(response.choices[0].message.content)
trace.get_tracer_provider().force_flush()

What Gets Captured

Attribute Description
llm_system openai (OpenAI-compatible client)
llm_model_name Resolved model name returned by the API (e.g. meta-llama-4-maverick-040225)
llm_request_parameters_model Endpoint name sent in the request (e.g. databricks-llama-4-maverick)
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_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
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_system: openai
  3. Note that the endpoint alias (e.g. databricks-llama-4-maverick) appears in llm_request_parameters_model, while the resolved model version (e.g. meta-llama-4-maverick-040225) appears in llm_model_name
  4. Filter by llm_request_parameters_model to compare latency across different serving endpoints

Databricks trace in OpenObserve

Next Steps

With Databricks Model Serving instrumented, every inference call is recorded in OpenObserve. From here you can monitor latency per endpoint, track token usage across Foundation Model API endpoints, and set alerts on error spans.

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