Databricks AI Functions

Official Docs: https://docs.databricks.com/large-language-models/ai-functions Individual function reference: https://docs.databricks.com/sql/language-manual/functions/

Overview

Databricks AI Functions are built-in SQL and PySpark functions that call Foundation Model APIs directly from your data pipelines — no model endpoint setup, no API keys, no boilerplate. They operate on table columns as naturally as UPPER() or LENGTH(), and are optimized for batch inference at scale.

Always prefer a task-specific function over ai_query. Reach for ai_query only when no task function fits (custom/external endpoints, multimodal, or JSON beyond ai_extract's limits). Every function below shares a baseline: DBR 15.1+ (notebooks) / 15.4 ML LTS (batch), not on SQL Warehouse Classic, and region must support AI Functions — the Prereqs column lists only what's additional.

Cost & speed — each call is an LLM inference (slow and billed per token). Run a function once per row and persist the result to a Delta table; never re-invoke it on every downstream query. In demos, avoid generating tables with millions of rows — sample the input when needed so the demo runs quickly. Materialize once, then query the cheap Delta output.

The Function column links to the in-repo deep reference (full options, schemas, examples); Docs links to the official page.