Buying snapshot
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| Decision point | What Soda states |
|---|---|
Installation | Open-source Soda Core v4 packages on public PyPI, one per data source (soda-{data source}, for example soda-postgres); needs Python 3.9, 3.10, 3.11 or 3.12 |
How it runs | CLI and Python API, run locally, embedded in pipelines (Airflow, Dagster, Prefect, etc.), or remotely when connected to Soda Cloud |
Data sources and checks | PostgreSQL, Snowflake, BigQuery, Databricks, DuckDB and more; 50+ built-in data quality checks |
Where data stays | Soda says data stays in your cloud; failed records are stored in your data warehouse of choice (Diagnostics Warehouse) |
Cloud evaluation | Free Soda Cloud account on request, with access for up to three datasets |
Our take
Soda makes sense if your engineers want quality checks as versioned YAML in their own pipelines and you expect business users to review those same contracts later. If you only need checks in code, the open-source engine covers that without a paid plan.
Evaluate Soda
Popular comparisons
See all 19 Soda comparisons
Where it stands out
One contract, two views. Engineers work in Git and business users work in the UI. Soda versions every change and shows proposals and diffs in either view, so a contract change does not need a second tool to review.
Runs inside your environment. The Soda Runner (formerly the Soda Agent) is configured in your environment and verifies contracts that are defined in Soda Cloud. This keeps execution close to the data.
Uses past data from day one. Soda says built-in backfilling and backtesting analyze historical data, so monitoring can start with a view of earlier patterns instead of an empty baseline.
Product screenshot
Trade-offs to weigh
Access controls need a sales talk. SSO, RBAC, audit logs and private deployment appear only in the Enterprise column, which has custom pricing. A team with those requirements cannot budget from the price list alone.
Version 4 renamed the packages. Legacy v3 packages use the form soda-core-{data source}, while v4 uses soda-{data source}. Teams on v3 can pin 3.5.x, but moving to v4 means changing package names.
AI Remediation is still coming. Soda marks AI Remediation, which gives AI-generated recommendations to fix bad records in source systems, as "Soon". Do not count on that feature when you plan a purchase.
Pricing and plan conditions
The Team price is a starting point, not a cap: what you add on top depends on how many Soda Processing Units (SPUs) you use beyond the included ones and which add-ons you take. Annual billing and volume discounts are listed with Enterprise.
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| Plan | Published price | What it adds |
|---|---|---|
Free | $0 per month | Free SPUs, pipeline testing, metrics observability, alerting and ticketing integrations; no credit card required. "Great for small projects" |
Team | $750 per month | All Free features, plus unlimited users, pay as you go for additional SPUs, catalog integrations; add-ons available. For data engineering teams |
Enterprise | Custom pricing | All Team features, plus collaborative data contracts, no-code interface, advanced AI-powered data quality features, audit logs, custom roles, RBAC, private deployment, SSO and premium support. For business collaboration |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
123 GitHub commits 90d2.5k GitHub stars0 vulnerabilities across 1 package
See all signals from 4 sources
Three-month score against stable baseline terms—not search volume or adoption.
PyPI · soda-core@4.30.0
Alternatives
Soda itself frames the choice against Monte Carlo, Anomalo, Great Expectations and building an in-house solution, with a comparison page for each. Use those as a shortlist, and test each against your own data sources and who needs to edit the checks.
Alternatives to Soda
The reviewed substitutes for Soda among the data validation frameworks, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Great Expectations
- Great Expectations and Soda both give data teams a way to define checks on their data and run them to catch quality problems before they spread. Great Expectations is an open-source Python framework built around codified expectations, while Soda writes checks in its own check language and adds an automated platform for finding and fixing issues.Applies to: Choosing between two products of the same kind for one job.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Datafold
- Soda and Datafold stop data incidents before they hit production. Soda runs data quality checks from table to record level and adds AI automation, while Datafold shows the effect of each code change by diffing data in pull requests.Applies to: For teams that want data checks to run in pull requests before a change reaches production.
- Metaplane
- Soda and Metaplane both alert a team the moment data quality slips. Soda is built around checks written for each dataset and run as part of the pipeline, while Metaplane watches the whole data stack continuously and gives metadata for debugging. Teams choose Soda for explicit checks in code and Metaplane for monitoring that needs few rules.Applies to: Deciding how data quality is enforced: automatic monitoring or checks written in the pipeline.
- Elementary
- Soda and Elementary both enforce data quality, and a dbt-centred team picks one as its primary tool. Elementary is dbt-native and stores test history in the warehouse, while Soda uses its own check language and is built for shared ownership across roles.Applies to: Choosing the primary data quality tool for a dbt-centred stack.
- Anomalo
- Soda and Anomalo each catch bad data before it reaches production users. Soda started from data quality checks that engineers define and run in the pipeline, while Anomalo relies on AI to detect and root-cause issues across tables, semi-structured and unstructured data without writing rules first. How much the team wants to define by hand decides between them.Applies to: Deciding how data quality is enforced: automatic monitoring or checks written in the pipeline.
- Bigeye
- Soda and Bigeye both detect data quality issues and help teams fix them before they spread. Soda runs checks at pipeline steps and has an open-source core, while Bigeye is an enterprise platform with automatic anomaly detection, end-to-end lineage and AI governance. The shape of the data stack, pipeline checks or platform-wide monitoring, decides the pick.Applies to: Deciding how the stack is shaped, where both products can be part of the answer.
- DataBuck
- Soda is a validation framework whose checks the data engineering team authors and runs in the pipeline; DataBuck is an observability platform whose context-aware AI discovers the rules itself. A buyer chooses between the two when the open question is whether to author quality checks or have the platform find them.Applies to: Enterprise data-quality work across large, cross-platform estates where the team wants validation rules discovered and maintained by the platform rather than authored as code and run in the pipeline.
- Acceldata
- Soda and Acceldata both find data quality issues and help teams resolve them. Soda centers on checks that run in the pipeline, from table to record level, with AI to automate them, while Acceldata monitors data, pipelines and governance across the enterprise with AI agents. Teams that enforce quality at pipeline steps lean to Soda, and those wanting estate-wide monitoring lean to Acceldata.Applies to: Deciding how data quality is enforced: automatic monitoring or checks written in the pipeline.
- Monte Carlo
- Monte Carlo and Soda both detect data quality issues and alert the team. Monte Carlo learns normal patterns and flags anomalies across the whole data estate without manual rules, while Soda grew from checks that engineers write and run in their pipelines. Teams that want hands-off monitoring lean to Monte Carlo, and teams that want checks in code lean to Soda.Applies to: Deciding how data quality is enforced: automatic monitoring or checks written in the pipeline.
- Validio
- Soda and Validio both find and explain data quality issues as soon as they appear. Soda grew from checks that teams write and run in the pipeline, while Validio monitors data and business metrics automatically across the enterprise. The pick depends on whether the stack enforces quality at pipeline steps or through one monitoring platform.Applies to: Deciding how the stack is shaped, where both products can be part of the answer.
Related technologies
Normally used together rather than chosen between, so these are not alternatives.
- Collibra
- A validation framework defines and runs checks; a catalog stores and displays the results beside lineage, ownership and glossary. Catalogs integrate the check tools rather than replacing them, so the pair is deployed together and the reader's question is which job each one does.Applies to: Whether a data catalog removes the need for a separate checks tool, or reports what it found.
- DataHub
- Soda defines and runs data quality checks; DataHub catalogues assets and carries quality status alongside lineage and ownership. The catalog displays results produced elsewhere, so the two are deployed together rather than chosen between.Applies to: Whether a data catalog replaces a checks tool, or shows what it found.