Unity Gateway quickstart

Send your first request through Unity Gateway against the OpenAI-compatible endpoint.

Requirements

Authenticate

To get started, use a personal access token. OAuth is recommended for production use cases. Set DATABRICKS_TOKEN to the token for your workspace.

Personal access token

Export a personal access token:

export DATABRICKS_TOKEN=<your-personal-access-token>

OAuth

Replace <workspace-url> with your Azure Databricks workspace URL. Then log in with the Azure Databricks CLI and export a short-lived OAuth token:

databricks auth login --host https://<workspace-url>
export DATABRICKS_TOKEN=$(databricks auth token --host https://<workspace-url> | jq -r .access_token)

Pick a model

Azure Databricks provides ready-to-use models that you can browse in the UI under the system.ai schema, or see the complete list of supported models here.

This quickstart uses system.ai.gpt-5-2. To query a different model, swap in its fully qualified name.

Send your request

Replace <workspace-url> with your Azure Databricks workspace URL.

Bash

curl https://<workspace-url>/ai-gateway/mlflow/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DATABRICKS_TOKEN" \
  -d '{
    "model": "system.ai.gpt-5-2",
    "max_output_tokens": 256,
    "input": [
      {
        "role": "user",
        "content": [{"type": "input_text", "text": "Hello!"}]
      },
      {
        "role": "assistant",
        "content": [{"type": "output_text", "text": "Hello! How can I assist you today?"}]
      },
      {
        "role": "user",
        "content": [{"type": "input_text", "text": "What is Databricks?"}]
      }
    ]
  }'

Python

from openai import OpenAI
import os

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

response = client.responses.create(
  model="system.ai.gpt-5-2",
  max_output_tokens=256,
  input=[
    {"role": "user", "content": [{"type": "input_text", "text": "Hello!"}]},
    {"role": "assistant", "content": [{"type": "output_text", "text": "Hello! How can I assist you today?"}]},
    {"role": "user", "content": [{"type": "input_text", "text": "What is Databricks?"}]},
  ],
)

print(response.output)

TypeScript

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.DATABRICKS_TOKEN,
  baseURL: 'https://<workspace-url>/ai-gateway/mlflow/v1',
});

const response = await client.responses.create({
  model: 'system.ai.gpt-5-2',
  max_output_tokens: 256,
  input: [
    { role: 'user', content: [{ type: 'input_text', text: 'Hello!' }] },
    { role: 'assistant', content: [{ type: 'output_text', text: 'Hello! How can I assist you today?' }] },
    { role: 'user', content: [{ type: 'input_text', text: 'What is Databricks?' }] },
  ],
});

console.log(response.output);

See your request

Open the model you queried (system.ai.gpt-5-2) in Catalog Explorer and select the Metrics tab to see the request you just sent in the usage history, along with its token usage and latency. See Unity Gateway observability.

Next steps

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