AnomalyArmor vs dbt tests
dbt tests catch what you define in schema.yml. AnomalyArmor catches what you did not anticipate — drift, freshness breaks, silent schema changes.
When dbt tests are enough
If every table your team cares about flows through dbt and you are happy with schema.yml tests as your full quality story, you may not need AnomalyArmor. Most teams discover their biggest incidents come from tables dbt does not own — source systems, reverse-ETL destinations, external tables — which is where AnomalyArmor pays for itself.
dbt tests are a great build-time gate. They fail your pipeline when the assertions you wrote do not hold. The problem they do not solve is the one that catches most teams off-guard: the silent failure. A source table stops updating. An upstream engineer adds a nullable column. A reverse-ETL job duplicates rows. None of those fail a dbt test you wrote six months ago.
dbt tests vs AnomalyArmor
Where we overlap, where we are different, and where dbt tests wins.
| Feature | dbt tests | AnomalyArmor(you are here) |
|---|---|---|
| Where checks run | At build time, inside dbt | Against warehouse, continuous |
| Auto-baseline monitors | ||
| Schema drift detection | ||
| Freshness SLAs | Manual | |
| Covers non-dbt tables | ||
| schema.yml import path | Native | Via dbt-odcs |
| Hosted alerts (Slack / PagerDuty) | DIY |
The pricing delta
dbt itself is free. dbt Cloud is per-seat. AnomalyArmor is $5 per monitored table per month and runs beside dbt rather than inside it — a 100-table warehouse is $6,000/yr flat.
dbt itself is free and there is no reason to stop using it. AnomalyArmor sits beside dbt in the $5/table tier; a typical 100-table mid-market warehouse is $6,000/yr all in. Compared to engineering time spent authoring and maintaining additional dbt test coverage across the non-dbt surface, the tradeoff is usually obvious.
What is different, specifically
- Where the checks run. dbt tests run at build time inside your transformation layer. AnomalyArmor runs continuously against your warehouse, independent of any pipeline. Different failure modes, different coverage.
- Coverage of non-dbt tables. dbt tests only apply to models inside your dbt project. Source tables from Fivetran, Airbyte, or direct loads are invisible to dbt tests. AnomalyArmor covers all of them.
- Auto-baseline monitors. AnomalyArmor proposes the first cut of monitors from observed warehouse patterns. dbt requires you to write every assertion explicitly.
- Import path via ODCS. The community
dbt-odcspackage translatesschema.ymltests to ODCS YAML. Full recipe at /migrate/dbt. - Keep dbt tests running. AnomalyArmor is additive, not a replacement. The two cover different failure modes and do not overlap.
Add monitoring without touching dbt
AnomalyArmor runs beside dbt, not inside it. Connect a warehouse and start catching the silent failures dbt cannot see.
dbt tests vs AnomalyArmor