dbt-expectations vs Metaplane.
dbt-expectations and Metaplane both anchor in quality & testing — 8 dimensions differ, 4 hold. Below: posture, coverage diff, and capability matrix.
What each is betting on.
Apache-2.0 dbt package, not a company. The original repo (calogica/dbt-expectations) was marked no longer maintained on 2024-12-18; active development forked to metaplane/dbt-expectations and the dbt Package Hub listing now publishes under the `metaplane` namespace (latest 0.10.x, dbt Fusion-compatible). Metaplane was itself acquired by Datadog (announced 2025-04-23). The package remains free and Apache-2.0 — it was never sold or made proprietary.
Acquired by Datadog (NASDAQ: DDOG), announced 2025-04-23. As of mid-2026 it continues as a standalone product branded 'Metaplane by Datadog' with features and support uninterrupted; Datadog has said it will work toward folding Metaplane's capabilities into the Datadog platform over time, so long-term roadmap independence is a known unknown. Acquisition price was not disclosed.
Each tool's current strategic narrative, verbatim from its profile.
How each tool describes the other.
The original package, by Calogica, was marked "no longer actively supported" in December 2024. Active development continues on a fork by Metaplane, which republished the canonical dbt Package Hub listing under its own namespace and keeps it current and dbt Fusion-compatible. Metaplane was acquired by Datadog in April 2025, so the practical maintainership chain today is Calogica (dormant) → Metaplane fork → a Datadog company. The licence never changed: it is Apache-2.0 and free.
Metaplane's page doesn't directly mention dbt-expectations. See the Metaplane detail page.
Each quote is pulled from the named tool's own "Where it fits" write-up.
Spec sheet diff.
| dbt-expectations | Metaplane | |
|---|---|---|
| Deployment | Self-hosted only | SaaS only |
| License | Open source | Proprietary |
| Pricing | OSS · paid tiers | Published |
| OSS self-host | Yes | No |
| Founded | 2020 | 2019 |
| HQ | — | Boston, MA |
| Status | ● active | ○ acquired |
| Authoring style | YAML | GUI |
| Test paradigm | Assertion-based | Assertion + anomaly |
Both share Vendor: Metaplane (Datadog) · Primary cluster: Quality & testing · Free tier: Yes · dbt integration: Native · OpenLineage: None
Each tool's center of gravity.
| Cluster | dbt-expectations | Metaplane |
|---|---|---|
| Lineage & metadata | 0/3 | 2/3 |
| Quality & testing | 3/3primary | 3/3primary |
| Catalog & discovery | 0/3 | 0/3 |
Scored 0–3 per cluster on the same rubric across all tools. A 0 means the cluster isn't the tool's focus, not that the feature is absent. See the methodology.
Where they cover different ground.
The declared feature set.
3 of 6 declared features differ — listed first.
These are each tool's self-declared key_features; a blank dot means
undeclared, not impossible.
| Feature | dbt-expectations | Metaplane |
|---|---|---|
| Assertion-Based Testing Quality & testing | ||
| ML Anomaly Detection Quality & testing | ||
| Column-Level Lineage Lineage & metadata | ||
| dbt-Native Testing Quality & testing | ||
| Schema Change Detection Quality & testing | ||
| Warehouse-Native Monitoring Quality & testing |
Where they disagree.
Quality & testing
4 of 13 differ| dbt-expectations | Metaplane | |
|---|---|---|
| ML anomaly detection | ||
| Incident management | ||
| Root-cause UI | ||
| Column profiling |
When to pick each.
dbt-centric analytics-engineering teams that already run dbt test in CI and want a broad library of declarative, in-warehouse assertions — value ranges, regex and pattern matching, schema shape, and distributional bounds (mean, median, stdev, quantiles) — with zero added cost or infrastructure. It is the natural first step up from dbt's four built-in tests (unique, not_null, accepted_values, relationships) for a team that wants richer checks without leaving the dbt workflow.
Startups and scaleups on a Snowflake, BigQuery, Redshift, or Databricks plus dbt stack that want fast, low-effort ML-based monitoring — roughly fifteen-minute setup, useful alerts within days — and want to pay only for the tables they actually monitor. Strong for analytics-engineering teams that want anomaly detection, automatic column-level lineage, and PR-time Data CI/CD checks without standing up a heavyweight enterprise platform.
What each does best.
dbt-expectations stands out for
- Free and Apache-2.0 with no paid tier, no SaaS, and no lock-in — the only cost is your own warehouse compute
- A library of 50+ assertions far beyond dbt's four built-ins (value ranges, regex, schema shape, distributional bounds)
- Fully native to dbt — declared in YAML, run by dbt test / dbt build, inheriting dbt severity levels, CI, and run artifacts; the current fork release is dbt Fusion-compatible
- Push-down execution across Postgres, Snowflake, BigQuery, DuckDB, Spark, and Trino
Metaplane stands out for
- ML anomaly detection that accounts for seasonality and trend, with very fast time-to-value (about fifteen-minute setup, alerts within days)
- Automatic end-to-end column-level lineage across warehouse, dbt, ingestion (Fivetran/Airbyte) and BI tools, with no manual instrumentation
- A genuine free-forever tier (10 monitored tables) and usage-based "pay only for monitored tables" pricing, payable with Snowflake credits via the Snowflake-native app
- Data CI/CD — regression and impact tests on GitHub/GitLab pull requests for dbt Core and Cloud, shifting checks left
Tools both also compete with.
A note on this comparison.
Every capability value above traces to dbt-expectations or Metaplane's own structured spec, which links back to its source — nothing here is averaged or smoothed across the two.
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