Data quality testing · Soda Data NV

Soda

Data quality testing platform combining a check-as-code language (SodaCL) with a SaaS UI for monitoring and collaboration.

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Soda lets teams define data quality checks in SodaCL, a concise YAML-based language, and run them from the command line or CI/CD pipelines against warehouses, lakes and streaming sources. Checks cover missing values, duplicates, freshness, schema and custom SQL conditions, and can be tied to data contracts between producers and consumers. Soda Cloud, the paid layer, adds a collaborative web UI, historical trend dashboards, anomaly detection, catalog integrations and alerting on top of the open check engine. Soda Core was relicensed from Apache-2.0 to the source-available Elastic License 2.0 in January 2026, so it remains free to use and self-host but is no longer an OSI-approved open-source license; teams embedding it in commercial software should review the new terms. A genuine free Soda Cloud tier exists alongside paid Team and Enterprise plans.

At a glance

Vendor Soda Data NV
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source No (Elastic-2.0)
Best for Teams wanting a check-as-code data quality tool with an optional collaborative SaaS layer.

Pricing

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

Plan Price Notes
Free $0/month Soda Processing Units included, pipeline testing, metrics observability, basic alerting
Team $750/month unlimited users, pay-as-you-go for additional SPUs, catalog integrations, SSO, RBAC, annual billing/volume discounts available
Enterprise Custom contact sales

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

Features

  • 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)

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Great Expectations Open-source Python framework for defining, running and documenting data quality tests ("expectations") in pipelines. Deequ Open-source Scala/Spark library from AWS for defining unit tests that measure data quality on large datasets. DQOps Data quality and observability platform with 150+ built-in checks, configurable via UI or YAML, with daily automated monitoring.

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Terms to know

Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. Role-based access control (RBAC) Granting data or system permissions based on a user's assigned role rather than configuring access for each person individually.

Related guides

How to choose a data quality testing tool Pick by how checks get authored — code library or check-as-code platform — and whether you need scheduling, dashboards and alerting, or just a test suite.