Gaur and the dbt Semantic Layer.
dbt SL turns your dbt models into governed metrics that query your warehouse. We ingest from your sources into our own engine and serve contracts to every consumer. Same goal, different operational model.
Tied to dbt, or decoupled.
Metric definitions on top of your dbt models.
The dbt Semantic Layer lets you declare metrics in YAML alongside your dbt models. MetricFlow plans queries and pushes optimized SQL to your warehouse. It also ships an API and a dbt MCP server. It's tightly integrated with dbt as the transformation tool of record, and aimed squarely at data teams and analytics engineers.
Ingest, contract, serve. No transformation-tool coupling.
We ingest from your sources into our own engine, let you define contracts directly on the ingested data, and serve them through a query endpoint, an OpenAI-compatible chat endpoint and an MCP server. We don't assume dbt or any other transformation tool, and we're built for AI engineers and application developers as much as data engineers.
Coupling and engine are the two real splits.
dbt SL is part of dbt: if you're already a dbt shop, the metric definitions live alongside your models and queries run on your warehouse. We're decoupled from your transformation tool and run in our own engine. Pick based on what's already in your stack and where you want analytical queries to actually execute.
The honest table.
Both define metrics once and ship an MCP server. The real splits are coupling, where queries run, and who each one is built for.
Coming from dbt Semantic Layer: if you keep dbt for transformations, Gaur can ingest the resulting tables and serve contracts on top. If you don't, define contracts directly on the raw sources.
Two honest bets, not a winner.
dbt is the standard for analytics engineering: a Git-native workflow, a huge install base, and the boring-correct choice for transforming data and serving analysts. Here's the call we'd actually make.
- Your team already runs dbt at production scale and replacing it costs more than it's worth.
- Your primary consumer is a BI tool or analyst, and AI isn't on the near-term roadmap.
- Your data needs to stay in the warehouse for compliance reasons.
- You want a Git-native workflow on the transformation layer itself: PRs, tests, lineage.
- You're building a customer-facing app or AI feature and want every consumer on one governed backend.
- AI agents and chat are part of the product, not an experiment.
- Warehouse cost or concurrency is a real constraint.
- You want metric consistency enforced by a typed contract, not by author discipline.
Where this comparison shades into the others.
You run your business.
We run your analytics.
Hop on a 30-minute call. We'll connect a sandbox to your warehouse and show you Gaur on your numbers.
