CI native
Make quality part of every pull request
Run the same rule set locally and in CI. Annotate failures where your team already reviews code and block only what matters.
GitHub Actions · GitLab CI · any pipeline
CI checks are passing
The quality gate for your dbt codebase.
Catch risky SQL, enforce team conventions, and stop broken models before merge—without turning every pull request into a style debate.
- No credit card
- Works with your CI
- Actionable rule output
feature/customer-ltv
RUN #1842
Pull request quality check
terminal · dbtlint
$dbtlint check ./analytics
↳42 models · 18 tests · 6 sources
✓models/staging/stg_orders.sql
!L016: keyword must be lowercase
models/marts/fct_orders.sql:14
×L003: expected 4-space indentation
42
models scanned
1.8s
total runtime
94
quality score
Built for the review loop
Quality checks your team will actually keep enabled.
DBTLint turns your team's standards into fast, repeatable feedback—from the first local check to the final merge gate.
SQL aware
Rules that understand dbt
Check models, sources, tests, macros, naming, and formatting with context—not brittle text matching.
Models · YAML · Jinja
Configurable
Standards that fit your stack
Start from sensible presets, tune severity by rule, and extend the system with conventions unique to your warehouse.
Presets · overrides · custom packs
Team aligned
One rule set, fewer review loops
Share configuration across projects so reviewers can focus on business logic instead of whitespace and naming debates.
Shared config · consistent output
Fast feedback
Built for the inner loop
Scan projects in seconds and return file-level findings with line numbers, severity, and a clear path to resolution.
Fast scans · precise locations
From code to confidence
A quality gate, not another workflow.
Bring DBTLint to the way your team already ships. The feedback stays fast, scoped, and close to the code that caused it.
- 01ConnectProject discovered
Point DBTLint at your project
Upload a dbt project or run the checker from your existing developer workflow. Your folder structure and config stay intact.
› $ dbtlint check ./analytics - 02Inspect66 assets checked
Scan every relevant asset
Models, tests, sources, macros, and YAML are checked against the rule set your team selected.
› 42 models · 18 tests · 6 sources - 03ResolveReady for review
Fix findings before they merge
Review exact files, line numbers, severity, and remediation guidance. Re-run locally or let CI verify the patch.
› quality_score: 94 / 100
Clear pricing, clean code
Start small. Standardize when your team is ready.
Every plan includes the core linting experience. Upgrade for shared governance and CI automation.
Free
For individual analytics engineers building better habits.
$0
forever
- 1 dbt project
- Core SQL style checks
- Local CLI linting
- Community rule packs
Team
For data teams that want one standard across every project.
$19
per month
- Unlimited dbt projects
- CI/CD integration
- Shared rule sets
- Custom rule packs
- Priority support
Enterprise
For governed data platforms with security requirements.
Custom
built around your org
- SSO & SAML
- Audit logs
- Dedicated support
- On-prem deployment
- SLA guarantees
Plans are billed monthly. Cancel or change tiers at any time.