MetricLayer vs dbt Semantic Layer

MetricLayer accelerates dbt Semantic Layer adoption with workbook parsers, industry templates, and CI guardrails. Use this guide to find the right mix for your team.

Criteria

MetricLayer

dbt Semantic Layer

Metric definition

Programmatic YAML generation from workbooks with lint rules enforcing naming contracts.

Manual YAML authoring in dbt Cloud or Core with optional Semantic Layer API.

Guardrails

CI checks for fan-out joins, naming drift, and workbook parity.

Tests and contracts you configure manually in dbt projects.

BI coverage

Tableau templates now; Looker and Power BI on roadmap.

Works with dbt semantic clients; BI alignment handled by your team.

Continue exploring

Semantic layer guide

Learn the foundations and decide how MetricLayer fits alongside dbt.

Read the guide →CLI guardrails

Automate workbook uploads, linting, and CI enforcement.

View docs →Success stories

See how teams solved drift and reconciled metrics before launches.

Read the blog →

FAQs

Is MetricLayer affiliated with dbt Labs?: No. We build independent tooling that outputs MetricFlow-compliant YAML so teams can adopt dbt Semantic Layer faster.
Can I use MetricLayer with dbt Core?: Yes. MetricLayer generates YAML you can commit to any repo. Run our CLI in CI to guard pull requests.
Do I still need dbt Cloud?: Use whatever orchestration you prefer. MetricLayer focuses on workbook parsing, templates, and linting.