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SPECIFICTION
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CleanSmart vs Zingg

CleanSmart

cleansmartlabs.com

Zingg

zingg.ai

Side by side

FigureCleanSmartZingg
Price from$59/moFree
Free plan—Yes
Free trialYes—
Open source—Yes
WebYes—
macOS—Yes
Linux—Yes
API—Yes
Self-hosted—Yes
Plans published36
Spec score · Customer Data Quality Software8.2 (#6)8.1 (#13)
HeadquartersUnited StatesLewes, Delaware, United States
IntegrationsThe pricing page lists Salesforce, HubSpot, Shopify, Mailchimp, and Klaviyo as native connectors on Pro and Business; Starter supports CSV upload and export only.Documented connectors include Databricks, Snowflake, Cassandra, S3, Azure, Elastic, Exasol, relational databases and Spark-supported sources.
Intended usersCleanSmart describes its intended users as marketing, sales, and revenue operations staff at small and mid-sized companies.The maker describes Zingg as a tool for analytics engineers and data scientists to integrate data silos and build unified views at scale.
SupportThe help center says the team aims to reply within one business day and monitors in-app chat during business hours for urgent active-run issues.The comparison page lists open source support for Community and enterprise grade support for both Enterprise editions.

Plans

CleanSmart

  • Starter$59 a month
  • Pro$179 a month
  • Business$399 a month

CleanSmart 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

CleanSmart

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

Visit cleansmartlabs.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