Deequ alternatives

3 tools to consider instead of Deequ, shown against it.

Deequ Great Expectations Soda DQOps
Vendor Amazon Web Services (AWS Labs) GX (Great Expectations) Soda Data NV DQOps
Pricing model Open source + paid options Free tier + paid plans Free tier + paid plans Subscription
Free tier Yes Yes Yes
Deployment Self-hosted Cloud, Self-hosted Cloud, Self-hosted Cloud, Self-hosted
Open source Yes (Apache-2.0) Yes (Apache-2.0) No (Elastic-2.0) No (BSL-1.1)
Best for Spark-based data engineering teams on AWS wanting code-native quality checks at scale. Data and ML engineering teams wanting code-first, version-controlled data quality tests. Teams wanting a check-as-code data quality tool with an optional collaborative SaaS layer. Data teams wanting an out-of-the-box checks library with predictable per-table pricing.
Pricing

Free, open-source library with no vendor pricing or hosted product.

Pricing has not been verified yet — see the vendor's site.

Open-source library is free; GX Cloud has a free Developer plan plus quote-based Team and Enterprise plans.

Developer Free
Team Custom
Enterprise Custom

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Free plan available; Team is a flat monthly fee plus pay-as-you-go usage; Enterprise is custom.

Free $0/month
Team $750/month
Enterprise Custom

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Personal and Team tiers have published monthly prices (billed annually); Enterprise is custom.

Personal $600/month (billed annually)
Team $2,000/month (billed annually)
Enterprise Custom

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features
  • Declarative data quality 'unit tests'
  • Automated constraint suggestion
  • Anomaly detection on quality metrics over time
  • Incremental metric computation on Spark
  • PyDeequ Python API
  • AWS Glue and EMR integration
  • Data profiling
  • Declarative 'expectations' for data validation
  • Auto-generated data documentation
  • Automated profiling and test suite generation
  • Airflow, dbt and Dagster integration
  • Python and SQL-based validation
  • GX Cloud hosted UI
  • Validation results store
  • Alerting on failed checks (Cloud)
  • SodaCL declarative check language
  • Data quality monitoring and alerting
  • Data contracts
  • Catalog integrations
  • Anomaly detection
  • CI/CD pipeline testing
  • No-code check builder
  • RBAC and SSO (paid)
  • 150+ built-in data quality checks
  • YAML or UI-based configuration
  • Daily automated monitoring and dashboards
  • Anomaly detection
  • Data quality KPI scoring
  • Incident and issue tracking
  • Source-available self-hosting
  • Custom Python/SQL checks

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