dbt-expectations vs Soda.
dbt-expectations and Soda both anchor in quality & testing — 8 dimensions differ, 3 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 The original repo (calogica/dbt-expectations) was marked no longer maintained on 2024-12-18; active development forked to metaplane/dbt-expectations and the dbt Package Hub listing now publishes under the `metaplane` namespace (latest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.
Repositioned through 2025–2026 as an 'AI-native, fully automated data quality platform' — heavy product investment in Soda AI (anomaly detection), Collaborative Data Contracts, and Soda Cleanse (automated remediation). Soda Core is licensed under Elastic License 2.0 (source-available), not Apache, which OSS-purist evaluators should factor into the decision.
Each tool's current strategic narrative, verbatim from its profile.
How each tool describes the other.
It extends dbt's four built-in tests for teams that want richer assertions without leaving the dbt project — a lighter, code-only alternative to soda or the full Great Expectations framework. Against elementary it adds no anomaly detection or reporting UI; against bigeye, monte-carlo, or anomalo it has no ML monitoring, lineage, or incident management. In practice it pairs with those tools rather than competing: several observability vendors document running dbt-expectations as the in-warehouse assertion layer beneath their platform.
Soda's page doesn't directly mention dbt-expectations. See the Soda detail page.
Each quote is pulled from the named tool's own "Where it fits" write-up.
Spec sheet diff.
| dbt-expectations | Soda | |
|---|---|---|
| Vendor | Metaplane (Datadog) | Soda Data |
| Deployment | Self-hosted only | SaaS · Self-hosted |
| License | Open source | Source available |
| Pricing | OSS · paid tiers | From $750 |
| OSS self-host | Yes | No |
| dbt integration | Native | Metadata sync |
| Founded | 2020 | 2019 |
| HQ | — | Brussels, Belgium |
| Authoring style | YAML | Code-first + GUI |
| Test paradigm | Assertion-based | Assertion + anomaly |
Both share Primary cluster: Quality & testing · Free tier: Yes · OpenLineage: None · Status: ● active
Each tool's center of gravity.
| Cluster | dbt-expectations | Soda |
|---|---|---|
| Quality & testing | 3/3primary | 3/3primary |
| Catalog & discovery | 0/3 | 0/3 |
| Lineage & metadata | 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 | Soda |
|---|---|---|
| Data Contracts Quality & testing | ||
| dbt-Native Testing Quality & testing | ||
| ML Anomaly Detection Quality & testing | ||
| Assertion-Based Testing Quality & testing | ||
| Schema Change Detection Quality & testing | ||
| Warehouse-Native Monitoring Quality & testing |
Where they disagree.
Quality & testing
8 of 13 differ| dbt-expectations | Soda | |
|---|---|---|
| dbt-native | ||
| ML anomaly detection | ||
| Pre-merge diffing | ||
| Circuit breaker | ||
| Data contracts | ||
| 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.
Data engineering teams who want a clean, declarative DSL — SodaCL — for data quality checks that version-control in Git and run equally well in CI, in Airflow, or against a managed agent. Soda's sweet spot is teams that need both deterministic assertion-based checks and ML-based anomaly detection in one product, plus a real data-contract surface that engineers and business users can both work in. The European headquarters and self-hosted Kubernetes runner option make Soda one of the better fits for EU enterprises with data-residency constraints, and the published pricing at USD 750/month for the Team plan removes the always-talk-to-sales tax that several competitors impose.
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
Soda stands out for
- SodaCL is one of the cleaner data-quality DSLs — readable, version-controllable, and expressive enough for both simple assertions and ML thresholds
- Collaborative Data Contracts is a real enforcement primitive, not a doc page — Git workflow for engineers, UI for business users, breaking-change detection on contract violations
- Soda AI / anomaly detection is integrated, not bolted on — the same checks engine handles deterministic and ML thresholds
- Self-hosted Kubernetes runner is a genuine deployment option for EU and regulated buyers with data-residency requirements
Tools both also compete with.
A note on this comparison.
Every capability value above traces to dbt-expectations or Soda's own structured spec, which links back to its source — nothing here is averaged or smoothed across the two.
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