dbt-expectations vs Sifflet.
dbt-expectations and Sifflet both anchor in quality & testing — 11 dimensions differ, 2 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 The original repo (calogica/dbt-expectations) was marked no longer maintained on 2024-12-18; active development forked to metaplane/dbt-expectations and the dbt Package Hub listing now publishes under the `metaplane` namespace (latest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.
Independent and active. Privately held, Paris-based; raised ~USD 2.3M pre-seed, a ~USD 12.8M Series A (March 2023, led by EQT Ventures), and USD 18M in June 2025. ISO 27001, SOC 2 Type 2, GDPR; EU origin and a self-host option differentiate it for European and regulated buyers.
Each tool's current strategic narrative, verbatim from its profile.
Spec sheet diff.
| dbt-expectations | Sifflet | |
|---|---|---|
| Vendor | Metaplane (Datadog) | Sifflet |
| Deployment | Self-hosted only | SaaS · Self-hosted |
| License | Open source | Proprietary |
| Pricing | OSS · paid tiers | Contact sales |
| Free tier | Yes | No |
| OSS self-host | Yes | No |
| Founded | 2020 | 2021 |
| HQ | — | Paris, France |
| Authoring style | YAML | Code-first + GUI |
| Test paradigm | Assertion-based | Assertion + anomaly |
Both share Primary cluster: Quality & testing · dbt integration: Native · OpenLineage: None · Status: ● active
Each tool's center of gravity.
| Cluster | dbt-expectations | Sifflet |
|---|---|---|
| Catalog & discovery | 0/3 | 2/3 |
| Lineage & metadata | 0/3 | 3/3 |
| Quality & testing | 3/3primary | 3/3primary |
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.
8 of 9 declared features differ — listed first.
These are each tool's self-declared key_features; a blank dot means
undeclared, not impossible.
| Feature | dbt-expectations | Sifflet |
|---|---|---|
| Assertion-Based Testing Quality & testing | ||
| Circuit Breaker Quality & testing | ||
| dbt-Native Testing Quality & testing | ||
| ML Anomaly Detection Quality & testing | ||
| Schema Change Detection Quality & testing | ||
| Business Glossary Catalog & discovery | ||
| Column-Level Lineage Lineage & metadata | ||
| Reverse Impact Analysis Lineage & metadata | ||
| Warehouse-Native Monitoring Quality & testing |
Where they disagree.
Quality & testing
5 of 13 differ| dbt-expectations | Sifflet | |
|---|---|---|
| ML anomaly detection | ||
| Circuit breaker | ||
| 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.
Mid-market and enterprise data teams — especially in Europe — that want one platform spanning quality monitoring, an embedded catalog, and column-level lineage rather than stitching point tools together, with strong compliance posture (ISO 27001, SOC 2 Type 2, GDPR, single-tenant isolation, and a self-host option). The combination of assertion rules, ML/dynamic anomaly detection, automated root cause, and a Flow Stopper circuit breaker makes it a credible single-vendor observability suite.
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
Sifflet stands out for
- Spans all three observability clusters in one product — monitoring, an embedded catalog, and field-level lineage
- Both assertion-based rules and ML/dynamic anomaly detection (dynamic freshness/volume, distribution change, proprietary time-series thresholds) to cut alert fatigue
- Automatic field-level (column-level) lineage via SQL query-log parsing across Snowflake, BigQuery, Redshift, and Databricks, plus BI tools
- Flow Stopper circuit breaker and Monitors-as-Code (CLI, YAML, Terraform provider, public API) fit engineering workflows
Tools both also compete with.
A note on this comparison.
Every capability value above traces to dbt-expectations or Sifflet's own structured spec, which links back to its source — nothing here is averaged or smoothed across the two.
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