Request tagging

Attach custom key-value tags to individual requests using the Databricks-Ai-Gateway-Request-Tags HTTP header. Unity Gateway logs request tags to the request_tags column in both the usage tracking system table and inference tables, enabling you to track costs, attribute usage, and filter analytics by project, team, environment, or another dimension.

On requests where service policies run, custom policies can also evaluate these tags as event:context.request_tags. For example, a policy can require a tag to be present before allowing a request. See Request tags in service policies.

The header value must be a JSON object mapping string keys to string values. For example:

{ "project": "chatbot", "team": "ml-platform", "environment": "production" }

Tag a model service request

Use the extra_headers parameter (Python) or pass the header directly (REST API) to attach tags to a model service request:

Python (OpenAI SDK)

from openai import OpenAI
import json
import os

DATABRICKS_TOKEN = os.environ.get('DATABRICKS_TOKEN')

client = OpenAI(
  api_key=DATABRICKS_TOKEN,
  base_url="https://<workspace-url>/ai-gateway/mlflow/v1"
)

request_tags = {"project": "chatbot", "team": "ml-platform"}

chat_completion = client.chat.completions.create(
  messages=[
    {"role": "user", "content": "What is Databricks?"},
  ],
  model="<model-service>",
  max_tokens=256,
  extra_headers={
    "Databricks-Ai-Gateway-Request-Tags": json.dumps(request_tags)
  }
)

Python (Anthropic SDK)

import anthropic
import json
import os

DATABRICKS_TOKEN = os.environ.get('DATABRICKS_TOKEN')

request_tags = {"project": "chatbot", "team": "ml-platform"}

client = anthropic.Anthropic(
  api_key="unused",
  base_url="https://<workspace-url>/ai-gateway/anthropic",
  default_headers={
    "Authorization": f"Bearer {DATABRICKS_TOKEN}",
    "Databricks-Ai-Gateway-Request-Tags": json.dumps(request_tags),
  },
)

message = client.messages.create(
  model="<model-service>",
  max_tokens=256,
  messages=[
    {"role": "user", "content": "What is Databricks?"},
  ],
)

REST API

curl \
  -u token:$DATABRICKS_TOKEN \
  -X POST \
  -H "Content-Type: application/json" \
  -H 'Databricks-Ai-Gateway-Request-Tags: {"project": "chatbot", "team": "ml-platform"}' \
  -d '{
    "model": "<model-service>",
    "max_tokens": 256,
    "messages": [
      {"role": "user", "content": "What is Databricks?"}
    ]
  }' \
  https://<workspace-url>/ai-gateway/mlflow/v1/chat/completions

Replace <workspace-url> with your Azure Databricks workspace URL and <model-service> with the fully qualified name of your model service.

Tag a model provider service request

When you query a model provider service directly, send the tags header alongside the Databricks-Model-Provider-Service header:

Python

from openai import OpenAI
import json

client = OpenAI(
    api_key="<databricks-token>",
    base_url="https://<workspace-url>/ai-gateway/openai/v1",
    default_headers={"Databricks-Model-Provider-Service": "main.default.openai_prod"},
)

request_tags = {"project": "chatbot", "team": "ml-platform"}

response = client.chat.completions.create(
    model="gpt-5.5",
    messages=[{"role": "user", "content": "What is Databricks?"}],
    extra_headers={"Databricks-Ai-Gateway-Request-Tags": json.dumps(request_tags)},
)

REST

curl https://<workspace-url>/ai-gateway/openai/v1/chat/completions \
  -H "Authorization: Bearer $DATABRICKS_TOKEN" \
  -H "Databricks-Model-Provider-Service: main.default.openai_prod" \
  -H "Content-Type: application/json" \
  -H 'Databricks-Ai-Gateway-Request-Tags: {"project": "chatbot", "team": "ml-platform"}' \
  -d '{
    "model": "gpt-5.5",
    "messages": [{"role": "user", "content": "What is Databricks?"}]
  }'