01
dbt-expectations
cross-shopped OSS Self-host
3/3 shared
Open-source dbt package adding 50+ Great Expectations-style assertions as native dbt tests that run in your own warehouse.
Compare Great Expectations vs dbt-expectations →§ Alternatives · Quality & testing
Great Expectations alternatives.
9 same-cluster tools a team evaluating Great Expectations would realistically shortlist, ranked by capability overlap. 2 open source.
01
What counts as an alternative
Same job, different shape.
Every tool below shares Great Expectations's primary cluster (Quality & testing) and overlaps on the kind of team that buys it. Ranking favours shared key capabilities and cross-shop signals from the catalog data, with no paid placement. For the head-to-head detail, open the comparison on any row.
02
9 ranked substitutes
Alternatives to Great Expectations.
02
Soda
cross-shopped SaaS / Self-host
3/3 shared
YAML-first data contracts and observability — SodaCL plus Soda Cloud, with anomaly detection and a self-hosted Kubernetes runner.
Compare Great Expectations vs Soda →03
Anomalo
cross-shopped SaaS / Self-host
2/3 shared
GUI-first ML anomaly detection at petabyte scale — pivoting in 2026 around agentic AI and unstructured-data monitoring.
Compare Great Expectations vs Anomalo →04
Datafold
cross-shopped SaaS / Self-host
2/3 shared
Pre-merge data diffing and column-level lineage — the tool that shifts data quality left into the pull request.
Compare Great Expectations vs Datafold →05
Elementary
cross-shopped OSS SaaS / Self-host
2/3 shared
The dbt-native observability layer — tests, anomaly detection, and lineage that live inside your dbt project.
Compare Great Expectations vs Elementary →06
Acceldata
Hybrid
3/3 shared
Enterprise data observability with ML data quality, reconciliation, and a built-in catalog — strong on hybrid and on-prem estates.
Compare Great Expectations vs Acceldata →07
Monte Carlo
SaaS
3/3 shared
Warehouse-side data observability for teams whose problems are upstream of dbt — ingestion, streaming, and across the full pipeline.
Compare Great Expectations vs Monte Carlo →08
Bigeye
SaaS / Self-host
1/3 shared
Enterprise data observability with Autometrics ML thresholds — repositioning in 2026 as an AI Trust Platform with runtime governance.
Compare Great Expectations vs Bigeye →09
Sifflet
SaaS / Self-host
1/3 shared
EU-built full-stack data observability pairing ML-driven monitoring with an embedded catalog and field-level lineage.
Compare Great Expectations vs Sifflet →How this ranking is built.
The list is mechanical — same primary cluster and overlapping buyer profile, then scored on shared key capabilities, cross-shop signals, and cluster-strength proximity from the catalog data. See the methodology.