

Bigeye and dbt Cloud are both Data Observability tools. Compare features, pricing, and ratings below to find the best fit for your team.
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
Bigeye's auto-threshold engine analyzes historical column distributions to set anomaly bounds, eliminating the need to hand-tune expected ranges for hundreds of columns.
dbt Cloud's column-level lineage traces a specific sensitive field from its source table through every transformation to the final reporting table, satisfying data lineage requirements for financial audits.
Bigeye's data SLA feature tracks freshness and completeness against defined business SLAs, giving data teams a measurable reliability metric to report to stakeholders.
dbt Explorer surfaces model execution time trends, failed runs, and source freshness in a visual dependency graph, letting data engineers spot degrading models before pipelines miss SLAs.
Bigeye runs alongside existing dbt tests, adding statistical distribution monitoring that catches subtle data drift that row-count and null-check tests miss.
dbt Cloud's job scheduler runs dbt test and build on every PR, blocking merges that break downstream model dependencies or fail data quality tests before they reach production.
Best for: Fastest way to get started
Best for: First dbt project, pay-as-you-go
Best for: Scale for analytics + AI
Best for: Maximum control + security
7 differences found across 15 standardized features
Evaluative strengths and weaknesses: not feature lists
dbt Cloud added a new "Enterprise+" plan (Custom pricing)
Plan added · May 26, 2026
dbt Cloud removed the "Team" plan
Plan removed · May 26, 2026
dbt Cloud added a new "Starter" plan at $100/user/mo
Plan added · May 26, 2026