Deploying LLMs on Data Warehousing Platforms: A Step-by-Step Guide

Deploying large language models inside data warehousing platforms such as Snowflake, Databricks, Amazon Redshift, or Google BigQuery requires a clear separation between storage, compute, and inference. Data warehouses are optimized for parallel SQL execution, not GPU orchestration. The most reliable pattern is to expose LLMs as external functions that call a dedicated inference API, keeping warehouse cycles free for data processing while the inference layer handles model execution. This guide walks through that pattern end to end.

Step 1: Design the Integration Pattern

You have two options for running LLMs alongside warehouse data: native platform offerings or custom external functions via HTTP. Native AI features can work for basic tasks, but they often lock you into a narrow model catalog and charge by token or compute second. External functions give you full control over the model, parameters, and provider. They also let you route requests to an inference specialist such as Oxlo.ai rather than managing GPU clusters inside your warehouse.

Step 2: Build the Inference Handler

The handler is a lightweight Python function that accepts text from your warehouse, forwards it to an LLM API, and returns structured output. Because Oxlo.ai is fully OpenAI SDK compatible, you can use the same code you would use against OpenAI, with only the base URL changed.

import os

import requests

def warehouse_llm_handler(prompt: str, model: str = "llama-3.3-70b") -> str:

    response = requests.post(

        "https://api.ox