Overview
Soda is a data quality platform for testing and monitoring production datasets. It helps data engineering and analytics teams define quality checks, monitor metrics, receive alerts, integrate with ticketing systems, and scale data quality practices across the organization.
The platform is useful for teams that need a practical bridge between pipeline testing and enterprise-wide data quality governance.
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Key Features
- Dataset monitoring - Tests and monitors production datasets.
- Pipeline testing - Helps catch data issues earlier in data workflows.
- Metrics observability - Tracks changes and quality signals over time.
- Alerting and ticketing - Sends quality issues into operational workflows.
- Data contracts - Enterprise features support collaborative data contracts.
- No-code and AI features - Enterprise plan includes no-code and advanced AI-powered data quality features.
Pricing & Plans
Soda has public pricing.
| Plan | Price | Best fit |
|---|---|---|
| Free | $0 | Up to 3 production datasets |
| Team | $8 per dataset/month billed annually | Data engineering teams scaling monitoring |
| Enterprise | Custom | Organizations needing no-code, contracts, SSO, RBAC, and private deployment |
Check the current pricing page for fair use rules, dataset definitions, integrations, and annual billing terms.
Best For
Soda is best for data engineering and analytics teams that need scalable dataset testing, monitoring, and alerting.
FAQ
What is Soda?
Soda is a data quality platform for testing, monitoring, and alerting on datasets.
Is Soda free?
Yes. Soda offers a free plan for up to 3 production datasets.
How much does Soda Team cost?
Soda Team is listed at $8 per dataset per month, billed annually.
Who should use Soda?
It is best for teams that need production data quality monitoring across pipelines and datasets.
How much does Soda cost?
Pricing can change by plan, billing term, region, and usage limits. Check the official website for the current plan details before purchase.
Does Soda replace human review?
No. It can speed up the workflow, but important outputs should still be checked for accuracy, quality, and fit before production use.
