> ## Documentation Index
> Fetch the complete documentation index at: https://portkey-docs-add-third-party-integration-issues-fixes.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Databricks

> Integrate Databricks Model Serving with Portkey's AI Gateway

Portkey provides a robust and secure gateway to integrate various Large Language Models (LLMs) into applications, including [Databricks Model Serving](https://docs.databricks.com/en/machine-learning/model-serving/index.html) endpoints.

With Portkey, take advantage of features like fast AI gateway access, observability, prompt management, and more, while securely managing API keys through [Model Catalog](/product/model-catalog).

<Note>
  Provider Slug: `databricks`
</Note>

## Quick Start

Get Databricks working in 3 steps:

<CodeGroup>
  ```python Python icon="python" theme={null}
  from portkey_ai import Portkey

  # 1. Install: pip install portkey-ai
  # 2. Add @databricks provider in model catalog
  # 3. Use it:

  portkey = Portkey(api_key="PORTKEY_API_KEY")

  response = portkey.chat.completions.create(
      model="@databricks/databricks-meta-llama-3-1-70b-instruct",
      messages=[{"role": "user", "content": "Say this is a test"}]
  )

  print(response.choices[0].message.content)
  ```

  ```js Javascript icon="square-js" theme={null}
  import Portkey from 'portkey-ai'

  // 1. Install: npm install portkey-ai
  // 2. Add @databricks provider in model catalog
  // 3. Use it:

  const portkey = new Portkey({
      apiKey: "PORTKEY_API_KEY"
  })

  const response = await portkey.chat.completions.create({
      model: "@databricks/databricks-meta-llama-3-1-70b-instruct",
      messages: [{ role: "user", content: "Say this is a test" }]
  })

  console.log(response.choices[0].message.content)
  ```

  ```python OpenAI Py icon="python" theme={null}
  from openai import OpenAI
  from portkey_ai import PORTKEY_GATEWAY_URL

  # 1. Install: pip install openai portkey-ai
  # 2. Add @databricks provider in model catalog
  # 3. Use it:

  client = OpenAI(
      api_key="PORTKEY_API_KEY",  # Portkey API key
      base_url=PORTKEY_GATEWAY_URL
  )

  response = client.chat.completions.create(
      model="@databricks/databricks-meta-llama-3-1-70b-instruct",
      messages=[{"role": "user", "content": "Say this is a test"}]
  )

  print(response.choices[0].message.content)
  ```

  ```js OpenAI JS icon="square-js" theme={null}
  import OpenAI from "openai"
  import { PORTKEY_GATEWAY_URL } from "portkey-ai"

  // 1. Install: npm install openai portkey-ai
  // 2. Add @databricks provider in model catalog
  // 3. Use it:

  const client = new OpenAI({
      apiKey: "PORTKEY_API_KEY",  // Portkey API key
      baseURL: PORTKEY_GATEWAY_URL
  })

  const response = await client.chat.completions.create({
      model: "@databricks/databricks-meta-llama-3-1-70b-instruct",
      messages: [{ role: "user", content: "Say this is a test" }]
  })

  console.log(response.choices[0].message.content)
  ```

  ```sh cURL icon="square-terminal" theme={null}
  # 1. Add @databricks provider in model catalog
  # 2. Use it:

  curl https://api.portkey.ai/v1/chat/completions \
    -H "Content-Type: application/json" \
    -H "x-portkey-api-key: $PORTKEY_API_KEY" \
    -d '{
      "model": "@databricks/databricks-meta-llama-3-1-70b-instruct",
      "messages": [
        { "role": "user", "content": "Say this is a test" }
      ]
    }'
  ```
</CodeGroup>

<Note>
  **Tip:** You can also set `provider="@databricks"` in `Portkey()` and use just `model="databricks-meta-llama-3-1-70b-instruct"` in the request.
</Note>

## Add Provider in Model Catalog

1. Go to [**Model Catalog → Add Provider**](https://app.portkey.ai/model-catalog/providers)
2. Select **Databricks**
3. Choose existing credentials or create new by entering your [Databricks personal access token](https://docs.databricks.com/en/dev-tools/auth/pat.html) and workspace name
4. Name your provider (e.g., `databricks-prod`)

### Configuration Parameters

| Parameter             | Description                                                                                       | Required |
| --------------------- | ------------------------------------------------------------------------------------------------- | -------- |
| `apiKey`              | Databricks personal access token                                                                  | Yes      |
| `databricksWorkspace` | Databricks workspace name (used to construct the URL: `https://<workspace>.cloud.databricks.com`) | Yes      |

When using headers directly, pass the workspace name via the `x-portkey-databricks-workspace` header.

<Card title="Complete Setup Guide →" href="/product/model-catalog">
  See all setup options, code examples, and detailed instructions
</Card>

***

## Supported Endpoints

| Endpoint            | Support   |
| ------------------- | --------- |
| `/chat/completions` | Supported |
| `/completions`      | Supported |
| `/embeddings`       | Supported |
| `/responses`        | Supported |
| `/messages`         | Supported |

## Supported Features

| Feature            | Support                                                    |
| ------------------ | ---------------------------------------------------------- |
| Thinking/Reasoning | Supported via `thinking` and `reasoning_effort` parameters |
| Streaming          | Supported                                                  |

***

## Embeddings

Generate embeddings using Databricks-hosted embedding models:

<CodeGroup>
  ```python Python theme={null}
  from portkey_ai import Portkey

  portkey = Portkey(api_key="PORTKEY_API_KEY")

  response = portkey.embeddings.create(
      model="@databricks/databricks-bge-large-en",
      input="The quick brown fox jumps over the lazy dog"
  )

  print(response.data[0].embedding[:5])
  ```

  ```js Javascript theme={null}
  import Portkey from 'portkey-ai'

  const portkey = new Portkey({
      apiKey: "PORTKEY_API_KEY"
  })

  const response = await portkey.embeddings.create({
      model: "@databricks/databricks-bge-large-en",
      input: "The quick brown fox jumps over the lazy dog"
  })

  console.log(response.data[0].embedding.slice(0, 5))
  ```

  ```sh cURL theme={null}
  curl https://api.portkey.ai/v1/embeddings \
    -H "Content-Type: application/json" \
    -H "x-portkey-api-key: $PORTKEY_API_KEY" \
    -d '{
      "model": "@databricks/databricks-bge-large-en",
      "input": "The quick brown fox jumps over the lazy dog"
    }'
  ```
</CodeGroup>

***

## Responses API

Use the OpenAI Responses API format with Databricks models:

<CodeGroup>
  ```python Python theme={null}
  from portkey_ai import Portkey

  portkey = Portkey(api_key="PORTKEY_API_KEY")

  response = portkey.responses.create(
      model="@databricks/databricks-meta-llama-3-1-70b-instruct",
      input="Tell me a three sentence bedtime story about a unicorn."
  )

  print(response)
  ```

  ```js Javascript theme={null}
  import Portkey from 'portkey-ai'

  const portkey = new Portkey({
      apiKey: "PORTKEY_API_KEY"
  })

  const response = await portkey.responses.create({
      model: "@databricks/databricks-meta-llama-3-1-70b-instruct",
      input: "Tell me a three sentence bedtime story about a unicorn."
  })

  console.log(response)
  ```

  ```sh cURL theme={null}
  curl https://api.portkey.ai/v1/responses \
    -H "Content-Type: application/json" \
    -H "x-portkey-api-key: $PORTKEY_API_KEY" \
    -d '{
      "model": "@databricks/databricks-meta-llama-3-1-70b-instruct",
      "input": "Tell me a three sentence bedtime story about a unicorn."
    }'
  ```
</CodeGroup>

***

## Messages API

Use the Anthropic Messages API format with Databricks models:

<CodeGroup>
  ```python Python theme={null}
  from portkey_ai import Portkey

  portkey = Portkey(api_key="PORTKEY_API_KEY")

  response = portkey.with_options(
      headers={"x-portkey-anthropic-version": "2023-06-01"}
  ).post(
      "/v1/messages",
      body={
          "model": "@databricks/databricks-meta-llama-3-1-70b-instruct",
          "max_tokens": 250,
          "messages": [{"role": "user", "content": "Say this is a test"}]
      },
      cast_to=object
  )

  print(response)
  ```

  ```js Javascript theme={null}
  import Portkey from 'portkey-ai'

  const portkey = new Portkey({
      apiKey: "PORTKEY_API_KEY"
  })

  const response = await portkey.post("/v1/messages", {
      body: {
          model: "@databricks/databricks-meta-llama-3-1-70b-instruct",
          max_tokens: 250,
          messages: [{ role: "user", content: "Say this is a test" }]
      },
      headers: { "x-portkey-anthropic-version": "2023-06-01" }
  })

  console.log(response)
  ```

  ```sh cURL theme={null}
  curl https://api.portkey.ai/v1/messages \
    -H "Content-Type: application/json" \
    -H "x-portkey-api-key: $PORTKEY_API_KEY" \
    -d '{
      "model": "@databricks/databricks-meta-llama-3-1-70b-instruct",
      "max_tokens": 250,
      "messages": [
        { "role": "user", "content": "Say this is a test" }
      ]
    }'
  ```
</CodeGroup>

***

## Supported Models

Databricks Model Serving supports a variety of foundation models and custom endpoints:

* **Meta Llama Models**: `databricks-meta-llama-3-1-70b-instruct`, `databricks-meta-llama-3-1-405b-instruct`
* **DBRX**: `databricks-dbrx-instruct`
* **Embedding Models**: `databricks-bge-large-en`, `databricks-gte-large-en`
* **Custom Endpoints**: Any model deployed as a Databricks serving endpoint

For the complete list of available models, refer to the [Databricks Foundation Model APIs documentation](https://docs.databricks.com/en/machine-learning/foundation-model-apis/index.html).

***

## Next Steps

<CardGroup cols={2}>
  <Card title="Add Metadata" icon="tags" href="/product/observability/metadata">
    Add metadata to your Databricks requests
  </Card>

  <Card title="Gateway Configs" icon="gear" href="/product/ai-gateway/configs">
    Add gateway configs to your Databricks requests
  </Card>

  <Card title="Tracing" icon="chart-line" href="/product/observability/traces">
    Trace your Databricks requests
  </Card>

  <Card title="Fallbacks" icon="arrow-rotate-left" href="/product/ai-gateway/fallbacks">
    Setup fallback strategies with Databricks
  </Card>
</CardGroup>

For complete SDK documentation:

<Card title="SDK Reference" icon="code" href="/api-reference/sdk/list">
  Complete Portkey SDK documentation
</Card>