# DataRobot integrations

> Call the DataRobot API from any Tray.ai workflow with the HTTP Connector.

**Canonical page:** https://tray.ai/connectors/datarobot-integrations/
**Categories:** AI
**Connects through:** HTTP Connector

## Overview

DataRobot is an AI platform for building, deploying, and monitoring machine learning models and generative AI applications, with automated modeling and governance tools. Tray.ai connects to DataRobot through the HTTP Connector, so your workflows can call the DataRobot API alongside the rest of your stack.

## Use cases

### Projects

Read, create, update, and delete projects in DataRobot from any Tray.ai workflow. The DataRobot API covers calls such as list projects and create a project.

- List projects
- Create a project
- Update a project

### Datasets

Read, create, and delete datasets in DataRobot from any Tray.ai workflow. The DataRobot API covers calls such as list datasets and create a dataset from a file.

- List datasets
- Create a dataset from a file
- Create a dataset from a URL

### Batch predictions

Read and delete batch predictions in DataRobot from any Tray.ai workflow. The DataRobot API covers calls such as list batch prediction jobs and launch a batch prediction job.

- List batch prediction jobs
- Launch a batch prediction job
- Cancel a batch prediction job

### Use cases

Read, update, and delete use cases in DataRobot from any Tray.ai workflow. The DataRobot API covers calls such as list use cases and update a use case.

- List use cases
- Update a use case
- Delete a use case

## Challenges Tray.ai solves

### Keeping DataRobot credentials out of logs

A raw API call usually means pasting a key or token into the request, where it ends up in step inputs, logs, and screenshots.

**How Tray.ai helps:** Store DataRobot credentials once as a Tray.ai authentication and reference them from the HTTP Connector. Tray.ai masks authentication values in logs, so the secret never shows up in a step's input or output.

### Going from the DataRobot API docs to a working call

Translating an example request from API docs into a workflow step means copying the method, URL, headers, and body by hand.

**How Tray.ai helps:** Paste a cURL example from the DataRobot API docs into the HTTP Connector and Tray.ai fills in the method, URL, headers, and body for you.

### Handling files and non-JSON responses

Not every API answers in JSON. Some return XML, files, or binary data, or expect a multipart upload.

**How Tray.ai helps:** The HTTP Connector parses JSON and XML responses, returns files you can pass to the next step, and sends multipart form data for uploads.

## Frequently asked questions

### Does Tray.ai connect to DataRobot?

Yes. Tray.ai connects to DataRobot through the HTTP Connector, which calls the DataRobot API directly. Any call the DataRobot API documents can run as a step in a Tray.ai workflow.

### How does Tray.ai authenticate with DataRobot?

The DataRobot API uses a bearer token. You save it once as a Tray.ai authentication, and the HTTP Connector adds it to every request.

### Can I get a dedicated DataRobot connector?

DataRobot runs through the HTTP Connector rather than a dedicated connector. If your team wants one, the Tray.ai connector SDK can build a dedicated DataRobot connector from the DataRobot API.

## Learn more

- Intelligent Integration: https://tray.ai/platform/intelligent-ipaas/
- Merlin Agent Builder: https://tray.ai/platform/merlin-agent-builder/
- Agent Gateway for MCP: https://tray.ai/platform/agent-gateway/
- Book a demo: https://tray.ai/contact/