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Epsilon COREid vs Zingg

Epsilon COREid

epsilon.com

Zingg

zingg.ai

Side by side

FigureEpsilon COREidZingg
Price from—Free
Free plan—Yes
Free trial——
Open source—Yes
macOS—Yes
Linux—Yes
API—Yes
Self-hosted—Yes
Plans published06
Spec score · Identity Resolution Software7.4 (#12)8.1 (#4)
IntegrationsEpsilon says COREid powers its customer data platform, which can activate audiences through owned channels such as email and SMS and paid channels such as digital media, CTV and online video.Documented connectors include Databricks, Snowflake, Cassandra, S3, Azure, Elastic, Exasol, relational databases and Spark-supported sources.
PrivacyEpsilon says its solutions have been built with Privacy by Design principles for more than 10 years.The Snowflake page says Zingg can run directly within Snowflake as a Snowflake Container running a Snowpark application, maintaining control over data.
PurposeCOREid services provide identity resolution to recognize and reach more than 200 million U.S. consumers in a privacy-safe way.Zingg is open source, warehouse native master data management software for resolving entities and building unified views from siloed data.
ScaleEpsilon’s identity capabilities page says COREid provides insights on more than 255 million unique U.S. individuals.The comparison page lists support for millions of records across Community, Enterprise Lite and Enterprise editions.

Plans

Epsilon COREid

No plans published.

Epsilon COREid pricing →

Zingg

  • CommunityFree
  • Community (Open Source)Free
  • EnterprisePriced on request
  • Enterprise LitePriced on request
  • Zingg EnterprisePriced on request
  • Zingg Enterprise LitePriced on request

Zingg pricing →

In their own words

Epsilon COREid

Epsilon COREid has no write-up on Specifiction yet; its figures are above.

Visit epsilon.com

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.

Visit zingg.ai