Koalify vs Zingg

Koalify is a web-based, HubSpot-native tool for identifying and merging duplicate contacts, companies, deals, tickets, and custom objects. Its matching rules can combine HubSpot properties such as email, phone number, domain, name similarity, and custom fields. For bulk cleanup, it can merge thousands of duplicates automatically through HubSpot workflow actions. Users can also view duplicate suggestions and merge records manually on HubSpot contact, company, deal, and ticket pages. Koalify properties can be used in HubSpot lists, reports, and alerts, including notifications to record owners. The service says it syncs only subscribed record types and properties used in duplicate, primary, or merge rules; temporary full-property backups may be made during merges unless backup is disabled. The free plan is for sandbox or demo accounts, or portals below 10,000 records. Pro costs $25 per month when billed annually, with pricing based on the selected objects’ total records rather than scans or merges.

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.