At a glance
| Supported data platforms | dbt adapters: Snowflake, BigQuery, Redshift, Databricks, Postgres, Athena, ClickHouse, Trino, DuckDB, Dremio, Spark, Fabric, SQL Server, Vertica |
| Anomaly detection | Anomaly detection as dbt tests in OSS; ML-powered monitors in Cloud |
| Freshness and volume monitoring | Yes, as dbt tests |
| Schema checks | Yes, schema change tests |
| Validation rules | Uses dbt tests and Elementary tests defined in YAML |
| Lineage and root cause | dbt lineage in the OSS report; column-level lineage in Cloud |
| Alerting | Slack and Microsoft Teams |
| Deployment | Self-hosted dbt package and CLI, or SaaS |
| Licensing | Apache 2.0 OSS; Cloud by quote with 30-day trial |
Elementary is the natural first step for dbt teams: install the package, add anomaly tests in YAML next to your models and get a report and alerts at no cost. Move to Elementary Cloud when you need several dbt projects, monitoring outside dbt, ML-based monitors or incident management, and expect a quote, since no Cloud prices are published.
Overview
Elementary OSS has two parts: a dbt package that you add to your project, which stores dbt run results and test results in an Elementary schema in your warehouse and provides anomaly detection as native dbt tests (freshness, volume, column metrics and schema changes), and a Python CLI that builds a static observability report and sends alerts to Slack or Microsoft Teams. The OSS documentation lists adapters for Snowflake, BigQuery, Redshift, Databricks, Postgres, Athena, ClickHouse, Trino, DuckDB, Dremio, Spark, Fabric, SQL Server and Vertica.
Elementary Cloud is the commercial service built on the same package. According to the documentation, OSS covers a single dbt project, while Cloud covers several projects, connects to warehouses for monitoring that is not defined in dbt, and adds ML-powered anomaly detection, column-level lineage, a built-in catalog, incident management and AI agents. The vendor states Cloud reads the schema created by the dbt package rather than needing broader access to your data.
Key features
- Anomaly detection tests (volume, freshness, column metrics) written as native dbt tests in YAML
- Schema change tests and dbt test result collection in a warehouse schema
- Self-hosted HTML observability report with lineage and test history (OSS)
- Alerts to Slack and Microsoft Teams from the CLI
- dbt artifacts uploader and model performance tracking
- Elementary Cloud: multi-project coverage, ML monitors, column-level lineage, catalog and incident management
Integrations and ecosystem
Pros and cons
Pros
- Free, Apache 2.0 open source core that lives inside your dbt project
- Tests are configured in the same YAML as your dbt models
- Supports a long list of dbt adapters
- Clear upgrade path from OSS to Cloud, and back to OSS if you cancel
Cons
- OSS covers one dbt project and only data that dbt manages
- Self-hosting the report and alerting is your responsibility in OSS
- No published prices for Elementary Cloud; the trial text references a plan name not listed
Pricing
Elementary OSS, the dbt package and CLI, is free under the Apache 2.0 licence. Elementary Cloud has Scale, Enterprise and Unlimited plans priced on seats and environments, with extra monitored tables charged per 1,000 and an AI Layer add-on billed in credits; every plan says "Talk to us" and no amounts are shown. A 30-day free trial is offered; the pricing page says it includes all features of an "Essentials" plan, a name that no longer appears among the listed plans. If you cancel, the vendor points you to the self-hosted community version.
Pricing checked on the vendor's official page on 7 November 2026. Prices change; confirm before buying.
Who it is best for
Analytics engineering teams that build in dbt and want observability defined in code, starting free with the open source package.
Typical use cases
- Add volume and freshness anomaly tests to critical dbt models in Snowflake or BigQuery
- Send a Slack alert with failed dbt tests after each production run
- Share a data health report with stakeholders after each run
- Track slow or failing dbt models over time
Who should look elsewhere
- Organisations without dbt that want warehouse-wide automated monitoring: see Monte Carlo or Metaplane.
Verdict
Elementary is a practical, low-cost way to add observability to a dbt project, with its free Apache 2.0 package doing real work rather than acting as a teaser. Its scope follows dbt, so teams that need monitoring outside dbt or across many projects will be looking at the paid Cloud tier, priced by quote.
Frequently asked questions
Is Elementary open source?
Yes. Elementary OSS, the dbt package and Python CLI, is licensed under Apache 2.0. Elementary Cloud is a separate paid SaaS.
What is the difference between Elementary OSS and Elementary Cloud?
OSS covers a single dbt project with dbt-native anomaly tests, a self-hosted report and Slack or Teams alerts. Cloud adds multiple projects, monitoring of warehouses outside dbt, ML-powered anomaly detection, column-level lineage, a catalog and incident management.
Does Elementary work without dbt?
The open source version depends on dbt. Elementary Cloud can connect to data warehouses without dbt, according to its documentation.
How much does Elementary Cloud cost?
Plans (Scale, Enterprise, Unlimited) are priced on seats and environments with no published amounts. There is a 30-day free trial.
Sources
- Elementary GitHub repository (README and licence)
- Elementary releases
- Elementary OSS introduction (documentation)
- Elementary pricing
Checked November 2026.
Listed in: Data Quality & Observability.
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