How are Kumo foundation models different from an LLM or text-to-SQL?

Kumo foundation models predict outcomes from labeled examples and structured data without task-specific training. Kumo Tabular works with a single table; Kumo Relational also uses related tables.

An LLM generates or interprets language. Text-to-SQL translates a request into SQL that retrieves or aggregates existing records when executed.

Question	Typical approach
How many orders did this customer place last month?	SQL, written directly or generated from text.
Summarize this customer’s support conversations.	An LLM.
Will this customer place an order in the next 30 days?	A predictive model using historical data.

An application can combine an LLM to interpret requests, SQL to retrieve data, and a Kumo model to make predictions.

See Choose a Model for the available foundation models and Kumo Relational for its prediction workflow.