Metaplane vs Sifflet.
Metaplane and Sifflet both anchor in quality & testing — 7 dimensions differ, 4 hold. Below: posture, coverage diff, and capability matrix.
What each is betting on.
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.
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.
| Metaplane | Sifflet | |
|---|---|---|
| Vendor | Metaplane (Datadog) | Sifflet |
| Deployment | SaaS only | SaaS · Self-hosted |
| Pricing | Published | Contact sales |
| Free tier | Yes | No |
| Founded | 2019 | 2021 |
| HQ | Boston, MA | Paris, France |
| Status | ○ acquired | ● active |
| Authoring style | GUI | Code-first + GUI |
Both share Primary cluster: Quality & testing · License: Proprietary · OSS self-host: No · dbt integration: Native · OpenLineage: None · Test paradigm: Assertion + anomaly
Each tool's center of gravity.
| Cluster | Metaplane | Sifflet |
|---|---|---|
| Catalog & discovery | 0/3 | 2/3 |
| Lineage & metadata | 2/3 | 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.
5 of 8 declared features differ — listed first.
These are each tool's self-declared key_features; a blank dot means
undeclared, not impossible.
| Feature | Metaplane | Sifflet |
|---|---|---|
| Circuit Breaker Quality & testing | ||
| dbt-Native Testing Quality & testing | ||
| Schema Change Detection Quality & testing | ||
| Business Glossary Catalog & discovery | ||
| Reverse Impact Analysis Lineage & metadata | ||
| ML Anomaly Detection Quality & testing | ||
| Warehouse-Native Monitoring Quality & testing | ||
| Column-Level Lineage Lineage & metadata |
Where they disagree.
Quality & testing
1 of 13 differ| Metaplane | Sifflet | |
|---|---|---|
| Circuit breaker |
Lineage & metadata
1 of 7 differ| Metaplane | Sifflet | |
|---|---|---|
| Lineage API |
When to pick each.
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.
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.
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
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 Metaplane 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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