Datafold
Dataops platforms
Data observability software
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$799 per month
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What is Datafold
Datafold is a data observability product focused on preventing and diagnosing data quality issues in analytics pipelines. It is used by data engineering and analytics teams to monitor changes in data, validate transformations, and troubleshoot incidents across warehouses and transformation workflows. A key differentiator is its emphasis on data diffing and change-aware testing/monitoring to detect unexpected changes between datasets and across deployments.
Data diffing for change detection
Datafold centers on comparing datasets to identify unexpected changes in values, schema, and row-level results. This approach helps teams validate transformations and detect regressions after code or upstream data changes. It is particularly useful for debugging because it highlights what changed, not only that something failed.
Works with modern ELT workflows
The product is designed to fit common warehouse-centric stacks and transformation workflows used by analytics engineering teams. It supports use cases such as validating model changes and monitoring downstream impact from upstream modifications. This alignment reduces the need to build custom validation tooling around routine pipeline changes.
Faster incident triage context
Datafold provides context to investigate data incidents by surfacing where changes occur and which datasets are affected. This can shorten time spent isolating the source of a data issue compared with relying only on generic alerts. Teams can use this information to prioritize fixes based on impacted tables and transformations.
Not a full DataOps suite
Datafold focuses on observability and validation rather than end-to-end orchestration, ingestion, and activation. Organizations still need separate tools for scheduling, workflow management, and broader pipeline operations. Buyers looking for a single platform to cover the entire data lifecycle may find gaps.
Best fit for warehouse analytics
The strongest fit is for SQL/warehouse-based analytics pipelines and transformation-centric workflows. Teams with heavy streaming, event-time processing, or non-tabular data patterns may need additional observability approaches. Coverage and value can vary depending on how much of the stack is accessible for comparison and monitoring.
Requires setup and governance
To get consistent results, teams must define what to compare, set thresholds, and maintain monitors as schemas and pipelines evolve. Without ownership and operational processes, alerts can become noisy or incomplete. Adoption typically requires coordination between data engineering and analytics stakeholders.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Free | $0 | For small teams using a Modern Data Stack (cloud DWH + dbt). Includes column-level lineage and automated testing with Data Diff (announced by Datafold). |
| Cloud | $799 per month (annual) — starting price | Full Datafold Cloud platform. Datafold states pricing "starts at $799 / month when billed annually" and that actual pricing grows with usage and the complexity of the data monitored; pricing is customized based on number of users and tables monitored/tested. Deployment options include multi-tenant SaaS, dedicated VPC, and customer-hosted VPC; some features (one-time migration conversion/validation, column-level lineage) can be purchased separately. |
| Enterprise | Custom pricing | Enterprise entitlements (in-VPC / on-premise deployment, custom SLAs, dedicated support, bespoke contracts). Contact sales for a quote. |
Seller details
Datafold, Inc.
San Francisco, CA, USA
2020
Private
https://www.datafold.com/
https://x.com/datafoldhq
https://www.linkedin.com/company/datafold/
