dbt Semantic Layer vs Cube vs AtScale: Choosing an Enterprise Semantic Layer

#ai #database #datascience #dataengineering

Three semantic layers, three architectures, three very different bills.

All three will define what a metric means. None of them proves an AI agent is allowed to run it.

Quick orientation

	dbt Semantic Layer	Cube	AtScale
Core idea	Metrics as version-controlled code	Headless API in front of metrics	OLAP-style aggregate acceleration
Strongest when	You want engineering discipline	Many apps consume the same numbers	Heavy, stable aggregate workloads
Modelling	Hand-authored YAML	Hand-authored data model	Hand-authored cubes
Cost driver	Plan tier + query volume	Pre-aggregation builds + compute	Quote-based licence + compute
Governance	Upstream, in the warehouse	In front of the API	On the cube

Each is competent at what it was built for. If your consumers are dashboards and analysts, any of the three will serve you.

The question none of them answers

An agent doesn't arrive with a metric name. It arrives with an intent in English and has to work out which entities, which grain, which joins, and whether it's entitled to any of it.

That exposes two gaps every one of these shares:

Undefined intent has no answer. Coverage is whatever someone remembered to model. Business questions don't respect that boundary.
Authorisation is checked around the query, not inside it. A filter applied after execution means the data already moved.