The biggest difference is where matching runs. Hosted or self-hosted, every option that loads your records into its own store adds pipelines to build, a second copy to secure, and sync to keep running. Zingg matches your tables where they already are.
| Zingg | Build It Yourself | MDM HubsInformatica, Reltio, Tamr, CluedIn | Entity Resolution EnginesSenzing, Tilores, Quantexa | Customer Data PlatformsAmperity | |
|---|---|---|---|---|---|
| Where your data lives | Stays in your warehouse or lakehouse | In your platform | Copied into the hub, in the vendor's cloud or yours | Loaded into the engine's own store, as SaaS or self-hosted | Ingested into the CDP, with some zero-copy options |
| Cost of moving data | None. No pipelines out, no second copy | None | ETL into the hub, a second copy, sync back | Pipelines into the engine and a second copy to keep in sync, even when self-hosted | Ingestion pipelines and a second copy of customer data |
| Matching approach | Learned from 40–50 labelled pairs, plus deterministic rules in Enterprise | Hand-written rules that break | Configured match rules, with AI assistance | Pre-tuned engines or configured rules | Customer identity rules plus scoring |
| Blocking and scale | Learned from your labelled pairs, so there are no blocking rules to design or retune | You design blocking and retune it as data grows, or comparisons explode | Varies; some, like Tamr, ask you to define and tune blocking terms | Varies by engine | Handled inside the product |
| Entities | Any entity | Whatever you build for | Several master data domains | Mostly people and organisations | Customers and households |
| Time to first results | Days. Label 40–50 pairs | Months of rules and tuning | Long implementation projects | Quick to start, then integrate into your apps | Set up around marketing sources and activation |
| Openness | Open source core, inspectable | Yours to maintain | Proprietary | Proprietary | Proprietary |