Verato Universal MPI vs Zingg
Verato Universal MPI
verato.comZingg
zingg.aiSide by side
| Figure | Verato Universal MPI | Zingg |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Web | Yes | — |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 4 | 6 |
| Spec score · Identity Resolution Software | 6.7 (#22) | 8.1 (#4) |
| Headquarters | McLean, Virginia, United States | Lewes, Delaware, United States |
| Matching | Verato says its Referential Matching technology provides independently verified identity matching accuracy. | It combines machine learning matching with rule based deterministic matching, which can use identifiers such as email, SSN, and passport ID. |
| Purpose | Verato UMPI and Auto-Steward provide patient identity matching and help create a more complete view of patient information. | Zingg is open source, warehouse native master data management software for resolving entities and building unified views from siloed data. |
| Support | The Verato Support Center offers case submission, support contact, a status page, developer experience resources, and a knowledge base. | The comparison page lists open source support for Community and enterprise grade support for both Enterprise editions. |
Plans
Verato Universal MPI
- EnterprisePriced on request
- Enterprise PlusPriced on request
- StandardPriced on request
- StarterPriced on request
Zingg
- CommunityFree
- Community (Open Source)Free
- EnterprisePriced on request
- Enterprise LitePriced on request
- Zingg EnterprisePriced on request
- Zingg Enterprise LitePriced on request
In their own words
Verato Universal MPI
Verato Universal MPI has no write-up on Specifiction yet; its figures are above.
Zingg
Zingg is open-source, warehouse-native master data management software for resolving entities across data silos and building unified views. It is intended for analytics engineers and data scientists integrating sources at scale, and runs on Linux in a self-hosted environment. Users train the system by labeling 40–50 matching pairs from their own data. Matching combines machine learning with deterministic rules that can use identifiers such as email, SSN, and passport ID. Listed language matching covers Chinese, Japanese, Thai, Hindi, German, French, and Greek without language-specific setup. The Community, Enterprise Lite, and Enterprise editions support millions of records. Enterprise editions can run incrementally against previously matched data, and Enterprise also supports real-time architecture. ZINGG_ID is a persistent identifier assigned to new entities that remains stable across model and configuration changes. Zingg can also run within Snowflake as a Snowflake Container using Snowpark. Community costs nothing and includes batch processing, probabilistic matching, and open-source support.