Bigeye vs dbt-expectations.
Bigeye and dbt-expectations both anchor in quality & testing — 12 dimensions differ, 1 hold. Below: posture, coverage diff, and capability matrix.
What each is betting on.
Strategic repositioning in 2025–2026 from pure data observability to an 'Enterprise AI Trust Platform.' Founder Kyle Kirwan transitioned from CEO to CPO. New launches include AI Guardian (runtime data-access policy enforcement for AI applications) and expanded sensitive-data classification (PII/PHI/PCI). USAA invested USD 5M as a strategic customer round.
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 The original repo (calogica/dbt-expectations) was marked no longer maintained on 2024-12-18; active development forked to metaplane/dbt-expectations and the dbt Package Hub listing now publishes under the `metaplane` namespace (latest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.
Each tool's current strategic narrative, verbatim from its profile.
How each tool describes the other.
Bigeye's page doesn't directly mention dbt-expectations. See the Bigeye detail page.
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.
Each quote is pulled from the named tool's own "Where it fits" write-up.
Spec sheet diff.
| Bigeye | dbt-expectations | |
|---|---|---|
| Vendor | Bigeye | Metaplane (Datadog) |
| Deployment | SaaS · Self-hosted | Self-hosted only |
| License | Proprietary | Open source |
| Pricing | Contact sales | OSS · paid tiers |
| Free tier | No | Yes |
| OSS self-host | No | Yes |
| dbt integration | Metadata sync | Native |
| Founded | 2019 | 2020 |
| Authoring style | Code-first + GUI | YAML |
| Test paradigm | Assertion + anomaly | Assertion-based |
Both share Primary cluster: Quality & testing · OpenLineage: None · Status: ● active
Each tool's center of gravity.
| Cluster | Bigeye | dbt-expectations |
|---|---|---|
| Lineage & metadata | 2/3 | 0/3 |
| 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.
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 | Bigeye | dbt-expectations |
|---|---|---|
| Assertion-Based Testing Quality & testing | ||
| dbt-Native Testing Quality & testing | ||
| ML Anomaly Detection Quality & testing | ||
| Warehouse-Native Monitoring Quality & testing | ||
| PII Auto-Classification Catalog & discovery | ||
| Column-Level Lineage Lineage & metadata | ||
| Table-Level Lineage Lineage & metadata | ||
| Schema Change Detection Quality & testing |
Where they disagree.
Quality & testing
7 of 13 differ| Bigeye | dbt-expectations | |
|---|---|---|
| dbt-native | ||
| ML anomaly detection | ||
| Pre-merge diffing | ||
| Circuit breaker | ||
| Incident management | ||
| Root-cause UI | ||
| Column profiling |
When to pick each.
Mid-market and enterprise data teams who want a polished, sales-supported data observability product with strong ML-based anomaly detection (Autometrics) and an explicit governance and sensitive-data story. Bigeye's 2025–2026 pivot toward AI Trust — including AI Guardian, the runtime data-access policy gate for AI applications — makes it a fit for organisations actively deploying agentic AI on internal data and worried about what those agents can read. The customer list (Cisco, Zoom, USAA, Burberry, Centene) skews to large regulated enterprises, and the column-level lineage product is real, not a token feature.
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.
What each does best.
Bigeye stands out for
- Autometrics / Autothresholds — Bigeye's ML-based anomaly detection — has a strong reviewer reputation for low false-positive rates relative to peers in the cluster
- First-class column-level lineage from query-log parsing, including BI dashboard tracing — one of the better lineage products in a quality-led tool
- AI Guardian (2026) is among the few production-ready runtime AI data-access policy products in the data-observability landscape — runtime enforcement, not just classification
- Strong enterprise governance posture — PII/PHI/PCI auto-classification, certification workflows, semantic-layer creation
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
Tools both also compete with.
A note on this comparison.
Every capability value above traces to Bigeye or dbt-expectations's own structured spec, which links back to its source — nothing here is averaged or smoothed across the two.
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