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DataRobot is an enterprise AI platform that enables organizations to build, deploy, and govern predictive and generative AI models at scale. It provides an end-to-end environment for model lifecycle management, including custom LLM deployments, RAG blueprints, and agentic workflows.

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Overview

Integrate SambaNova models directly into DataRobot Workbench as custom managed LLMs. Once connected, you can:
  • Use SambaNova models inside RAG and Agentic Workflows
  • Interact with models via Playground chat
  • Manage deployments with full governance and monitoring through DataRobot
For detailed implementation guidance, see the Full SambaNova DataRobot Integration guide.

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Prerequisites

Before you begin, ensure you have:
  • A SambaNova Cloud account with an API key
  • Access to DataRobot Workbench, Console, and Registry

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Required files

Download the following files from the SambaNova integrations repository:

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Setup

Follow these steps to deploy a SambaNova-powered model inside DataRobot:
  1. Create a Custom Model
    • Navigate to Registry → Workshop → + Add Model
    • Select Proxy type with TextGeneration target type
    • Upload your custom.py, requirements.txt, and model-metadata.yaml files
  2. Build Environment
    • Select [GenAI] Python 3.12 with Moderations as the base environment
    • Click Build and wait for “Environment built successfully” confirmation
  3. Configure Runtime Parameters Open the runtime parameter editor and configure the following:
    • SAMBANOVA_API_KEY → your SambaNova API token
    • SAMBANOVA_API_BASE → https://api.sambanova.ai/v1
    • SAMBANOVA_MODEL → gpt-oss-120b DataRobot Custom model setting page
  4. Register your custom SambaNova model in the DataRobot Registry.
  5. Deploy the Model
    • Go to Registry → Models → Deploy
    • Select your previously registered custom model
    • Wait until the deployment status shows Active DataRobot model deployment page
  6. Link to RAG or Agentic Workflow
    • In Workbench, create a new GenAI RAG use case
    • Navigate to Playground → Create LLM blueprint → Add deployed LLM
    • Select your deployment (e.g., SambaNova Chat)
    • Set Chat model ID to the SambaNova model name (e.g., gpt-oss-120b)
    • Validate and add the configuration
    • Configure your vector store, system prompt, and history management settings
Your RAG workflow is now powered by a live SambaNova model routed through DataRobot’s managed deployment layer. DataRobot RAG Playground with SambaNova models

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Additional resources

Learn more about DataRobot’s capabilities:
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