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.
| 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.
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:
Ignore feature matrices and score these five:
Most evaluations stop at 1. Numbers 3 and 5 are the ones that decide whether the thing ships in a regulated business.
The full breakdown — architecture-by-architecture comparison, cost profiles, and the migration implications of each — is here:
👉 [dbt Semantic Layer vs Cube vs AtScale: Choosing an Enterprise Semantic Layer](https://colrows.com/blogs/dbt-semantic-layer-vs-cube-vs-atscale/)
*Originally published at colrows.com/blogs/dbt-semantic-layer-vs-cube-vs-atscale*