Great Expectations vs Sifflet.
Great Expectations and Sifflet both anchor in quality & testing — 11 dimensions differ, 3 hold. Below: posture, coverage diff, and capability matrix.
What each is betting on.
Acquired May 2026 (acquirer not publicly named in the May 6 community update). GX Cloud announced as discontinued June 1, 2026 — the team is being absorbed into the acquirer's platform. GX Core (Apache-2.0) continues under new stewardship; the OSS path is the only continuing option pending the new stewards' roadmap.
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.
How each tool describes the other.
Great Expectations's page doesn't directly mention Sifflet. See the Great Expectations detail page.
Sifflet competes most directly with monte-carlo and bigeye as a full-stack, ML-driven observability suite, but leans harder into catalog and field-level lineage, giving it overlap with atlan and datahub on discovery. Against soda or great-expectations it is a managed, broader platform rather than a code-first testing framework; against datafold it does impact analysis in CI but not value-level data diffing. Its EU origin, GDPR posture, and self-host option are the clearest differentiators for European and regulated buyers.
Each quote is pulled from the named tool's own "Where it fits" write-up.
Spec sheet diff.
| Great Expectations | Sifflet | |
|---|---|---|
| Vendor | Great Expectations | Sifflet |
| License | Open source | Proprietary |
| Pricing | OSS · free | Contact sales |
| Free tier | Yes | No |
| OSS self-host | Yes | No |
| dbt integration | None | Native |
| Founded | 2017 | 2021 |
| HQ | — | Paris, France |
| Status | ○ acquired | ● active |
| Authoring style | Python | Code-first + GUI |
| Test paradigm | Assertion-based | Assertion + anomaly |
Both share Primary cluster: Quality & testing · Deployment: SaaS · Self-hosted · OpenLineage: None
Each tool's center of gravity.
| Cluster | Great 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.
7 of 8 declared features differ — listed first.
These are each tool's self-declared key_features; a blank dot means
undeclared, not impossible.
| Feature | Great Expectations | Sifflet |
|---|---|---|
| Assertion-Based Testing Quality & testing | ||
| Circuit Breaker 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
6 of 13 differ| Great Expectations | Sifflet | |
|---|---|---|
| dbt-native | ||
| ML anomaly detection | ||
| Pre-merge diffing | ||
| Freshness | ||
| Incident management | ||
| Root-cause UI |
When to pick each.
Python-first data engineering teams who treat data quality as a software engineering problem and want their tests to live in the same repository, version control, and CI as their pipeline code. GX Core remains the most mature OSS data-validation framework — Apache-2.0, deeply embedded in Airflow, Dagster, and Prefect operators, and supported by roughly 300 built-in Expectations covering schema, value distribution, statistical, and multi-column relationships. Particularly well-suited to healthcare, financial-services, and other regulated buyers who need pure-OSS, on-prem deployment with no SaaS dependency, since the project is permissive Apache-2.0 with no copyleft or relicensing risk.
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.
Great Expectations stands out for
- Largest open-source data-validation community by stars and contributors, with deep first-party Airflow, Dagster, and Prefect operator support
- Apache-2.0 license with permissive reuse — no source-available games, no rug-pull risk on the OSS path
- Roughly 300 built-in Expectations cover schema, distribution, statistical, and multi-column relationships — the broadest assertion library in the cluster
- Data Docs auto-generate human-readable validation results that non-engineering stakeholders can actually read
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 Great Expectations or Sifflet's own structured spec, which links back to its source — nothing here is averaged or smoothed across the two.
Notice something inaccurate? Send a correction.