Data observability · Elementary Data, Inc.

Elementary

dbt-native, open-source-core data observability tool that turns dbt test results and metadata into monitoring and alerts.

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Elementary is built as a dbt package: it reads the tests, models and run metadata already produced by a dbt project and turns them into anomaly detection, data health scores and column-level lineage without requiring a separate agent or connector layer. The open-source core can be self-hosted for free under an Apache-2.0 license, generating a local observability report from dbt artifacts. Elementary Cloud, the commercial hosted product, adds incident management, AI agents that suggest root causes, Slack/Teams/Jira alerting integrations and multi-project dashboards, sold across Scale, Enterprise and Unlimited tiers with pricing available only on request. It is the natural fit for analytics engineering teams that already live inside dbt and want observability layered on their existing workflow rather than a separate platform to maintain.

At a glance

Vendor Elementary Data, Inc.
Pricing model Quote only
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (Apache-2.0)
Best for dbt-centric analytics engineering teams wanting observability without leaving their existing workflow.

Pricing

Open-source dbt package is free to self-host; Elementary Cloud tiers (Scale/Enterprise/Unlimited) require a sales conversation.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

Features

  • dbt-native anomaly detection
  • Automated tests derived from dbt metadata
  • Column-level lineage
  • Incident management
  • Slack/Teams/Jira alerting
  • AI agents for root-cause suggestions
  • Data health scores
  • Self-hosted open-source core

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Metaplane Lightweight data observability tool with a genuine free tier and per-table usage pricing, including Snowflake credit billing. Monte Carlo Data observability platform using ML-based anomaly detection to catch broken pipelines before they reach dashboards. Great Expectations Open-source Python framework for defining, running and documenting data quality tests ("expectations") in pipelines.

Elementary in the index now

Terms to know

Data observability Continuous automated monitoring of pipelines and datasets to catch freshness, volume and quality problems early. dbt (data build tool) An open-source tool from dbt Labs that lets analytics teams write data transformations as version-controlled, tested SQL. Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. Analytics engineer A role that builds and maintains the transformed, tested data models analysts and dashboards rely on.

Related guides

How to choose a data observability tool Pick a data observability tool by how it generates coverage, where it needs to run relative to your data, and whether you're already living inside dbt.