01
Elementary
OSS SaaS / Self-host
The dbt-native observability layer — tests, anomaly detection, and lineage that live inside your dbt project.
Compare Datafold vs Elementary →§ Alternatives · Quality & testing
Datafold alternatives.
10 same-cluster tools a team evaluating Datafold would realistically shortlist, ranked by capability overlap. 3 open source.
01
What counts as an alternative
Same job, different shape.
Every tool below shares Datafold'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
10 ranked substitutes
Alternatives to Datafold.
02
dbt-expectations
OSS Self-host
Open-source dbt package adding 50+ Great Expectations-style assertions as native dbt tests that run in your own warehouse.
Compare Datafold vs dbt-expectations →03
Metaplane
SaaS acquired
ML-powered, no-code data observability for the dbt and warehouse stack with automatic column-level lineage — now Metaplane by Datadog.
Compare Datafold vs Metaplane →04
Monte Carlo
SaaS
Warehouse-side data observability for teams whose problems are upstream of dbt — ingestion, streaming, and across the full pipeline.
Compare Datafold vs Monte Carlo →05
Great Expectations
OSS SaaS / Self-host acquired
Python-native data validation framework — the OSS standard, now in stewardship transition after the May 2026 acquisition.
Compare Datafold vs Great Expectations →06
Acceldata
Hybrid
Enterprise data observability with ML data quality, reconciliation, and a built-in catalog — strong on hybrid and on-prem estates.
Compare Datafold vs Acceldata →07
Bigeye
SaaS / Self-host
Enterprise data observability with Autometrics ML thresholds — repositioning in 2026 as an AI Trust Platform with runtime governance.
Compare Datafold vs Bigeye →08
Soda
SaaS / Self-host
YAML-first data contracts and observability — SodaCL plus Soda Cloud, with anomaly detection and a self-hosted Kubernetes runner.
Compare Datafold vs Soda →09
Anomalo
SaaS / Self-host
GUI-first ML anomaly detection at petabyte scale — pivoting in 2026 around agentic AI and unstructured-data monitoring.
Compare Datafold vs Anomalo →10
Sifflet
SaaS / Self-host
EU-built full-stack data observability pairing ML-driven monitoring with an embedded catalog and field-level lineage.
Compare Datafold 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.