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Zingg

zingg.ai

Free plan Open source macOSLinuxAPISelf-hosted
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Spec score 8.1 #4 of 25 in Identity Resolution Software

What it is

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.

Spec sheet

What it does
Zingg uses machine learning and Spark to link duplicate or related records into unified entity identities for master-data and identity-resolution workflows.Read 15 Oct 2026 · zingg.ai
Purpose
Zingg is open source, warehouse native master data management software for resolving entities and building unified views from siloed data.Read 3 Oct 2026 · zingg.ai
Intended users
The maker describes Zingg as a tool for analytics engineers and data scientists to integrate data silos and build unified views at scale.Read 3 Oct 2026 · docs.zingg.ai
Platforms
The maker lists Databricks, Snowflake, BigQuery, Redshift, AWS Glue, GCP, and Microsoft Fabric as cloud platforms for Zingg.Read 3 Oct 2026 · zingg.ai
Integrations
Documented connectors include Databricks, Snowflake, Cassandra, S3, Azure, Elastic, Exasol, relational databases and Spark-supported sources.Read 15 Oct 2026 · docs.zingg.ai
Privacy
The Snowflake page says Zingg can run directly within Snowflake as a Snowflake Container running a Snowpark application, maintaining control over data.Read 3 Oct 2026 · zingg.ai
Support
The comparison page lists open source support for Community and enterprise grade support for both Enterprise editions.Read 3 Oct 2026 · zingg.ai
Headquarters
Lewes, Delaware, United StatesRead 28 Sep 2026 · zingg.ai
AI matching
Its machine-learning matcher learns from labeled examples to find matches that fixed rules can miss.Read 15 Oct 2026 · zingg.ai
CLI
The command-line interface runs labeling, training, matching and Python programs through zingg.sh.Read 15 Oct 2026 · docs.zingg.ai
Community praise
A Provenance engineering case study reports an active Slack community, regular releases and very few operational issues in production use.Read 15 Oct 2026 · zingg.ai
Contact response
The comparison page says the team will reach out within 1–2 business days after an inquiry.Read 3 Oct 2026 · zingg.ai
Deterministic matching
Enterprise editions combine probabilistic and deterministic matching in one flow and support identifiers such as email, SSN and passport ID.Read 15 Oct 2026 · zingg.ai
Docker install
The documented Docker workflow pulls zingg/zingg:0.5.0 and starts it with an interactive shell.Read 15 Oct 2026 · docs.zingg.ai
Documentation depth
Documentation covers installation, command-line use, Python APIs, connectors, cloud platforms, security, FAQs and step-by-step examples.Read 15 Oct 2026 · docs.zingg.ai
Entity coverage
The product is positioned for customers, suppliers, products, locations and other entity types.Read 15 Oct 2026 · zingg.ai
File formats
The connector layer supports formats including Parquet, Avro, JSON, XLSX, CSV and TSV.Read 15 Oct 2026 · docs.zingg.ai
Funding information
The opened Crunchbase page exposed performance metrics and placeholder recent-event entries but no usable funding amount or named funding round.Read 15 Oct 2026 · crunchbase.com
GitHub activity
The public repository showed about 1.3k stars, 178 forks, 91 issues and 23 pull requests when opened.Read 15 Oct 2026 · github.com
Incremental processing
Enterprise editions support incremental runs against previously matched data, and Enterprise also supports real time architecture.Read 3 Oct 2026 · zingg.ai
Independent company profile
LinkedIn describes Zingg.AI as a privately held software-development company headquartered in San Francisco with 2–10 employees and founded in 2021.Read 15 Oct 2026 · linkedin.com
Independent comparison
Data Stack Hub lists Zingg as an open-source machine-learning entity-resolution tool for record linkage, matching and deduplication, with no G2 rating displayed.Read 15 Oct 2026 · datastackhub.com
Independent integration evidence
AWS published a 23 May 2024 tutorial showing Zingg used inside AWS Glue notebooks and ETL jobs for entity resolution.Read 15 Oct 2026 · aws.amazon.com
Independent limit: engineering burden
MatchLogic says Zingg requires engineering ownership of operations, review, survivorship and access control and may require statistical or machine-learning expertise.Read 15 Oct 2026 · matchlogic.io
Independent praise: control
MatchLogic describes Zingg as free open-source software giving engineering teams control over the resolution model and the ability to scale on Spark and warehouses.Read 15 Oct 2026 · matchlogic.io
Interactive labeling
Zingg provides an interactive labeling workflow so users can train matching models with selected record pairs.Read 15 Oct 2026 · zingg.ai
Language matching
The product page lists matching for Chinese, Japanese, Thai, Hindi, German, French, and Greek without language specific setup.Read 3 Oct 2026 · zingg.ai
Licence
The public Zingg repository is licensed under GNU Affero General Public License version 3.0.Read 15 Oct 2026 · github.com
Limit: pre-1.0 maturity
The Provenance case study characterizes Zingg as pre-1.0, although the user says it was viable for production in that deployment.Read 15 Oct 2026 · zingg.ai
Matching
It combines machine learning matching with rule based deterministic matching, which can use identifiers such as email, SSN, and passport ID.Read 3 Oct 2026 · zingg.ai
Matching features
The feature set includes nickname mappings, company-name dictionaries, multilingual matching, stop words and categorical standardization.Read 15 Oct 2026 · zingg.ai
Notable news: 0.7.0
A September 2026 Reddit release announcement says Zingg 0.7.0 improved Fabric notebooks, updated Spark and GraphFrames versions and improved error handling.Read 15 Oct 2026 · reddit.com
Persistent identifier
ZINGG_ID is described as a persistent identifier that is assigned to new entities and remains persistent across model and configuration changes.Read 3 Oct 2026 · zingg.ai
Persistent identity
Zingg Enterprise assigns persistent ZINGG_ID values and maintains an identity graph as new or updated records arrive.Read 15 Oct 2026 · zingg.ai
Python API
Zingg publishes a Python package installable with pip and exposes client, argument, field-definition and pipe classes.Read 15 Oct 2026 · docs.zingg.ai
Rating on G2
No Zingg rating is displayed in the opened Data Stack Hub comparison, and that page does not state a review count.Read 15 Oct 2026 · datastackhub.com
Record scaling
The editions comparison lists support for millions of records in Community, Enterprise Lite, and Enterprise.Read 3 Oct 2026 · zingg.ai
Scale
The comparison page lists support for millions of records across Community, Enterprise Lite and Enterprise editions.Read 15 Oct 2026 · zingg.ai
Security claim
Zingg says models run inside the customer's network and input and output data do not leave the deployment.Read 15 Oct 2026 · docs.zingg.ai
Self-hosting
The documentation describes local Spark, Spark-cluster, Docker and source-build deployments, with data processing on the user's infrastructure.Read 15 Oct 2026 · docs.zingg.ai
System requirements
The release installation guide specifies Java JDK 11.0.23 and Apache Spark 3.5.0 as the example prerequisites.Read 15 Oct 2026 · docs.zingg.ai
Telemetry
The security page says usage metrics such as source type, field count, record count, execution time, version and environment details are sent to Google Analytics, with a collectMetrics opt-out.Read 15 Oct 2026 · docs.zingg.ai
Training
The homepage says users train Zingg on their own data by labeling 40–50 matching pairs.Read 3 Oct 2026 · zingg.ai

Plans and prices

Community Free Open-source edition · batch architecture · probabilistic matching · millions of records Read 15 Oct 2026 · zingg.ai
Community (Open Source) Free Batch · probabilistic matching · millions of records · open source support Read 3 Oct 2026 · zingg.ai
Enterprise Priced on request Batch and real-time architecture · probabilistic and deterministic matching · native Snowflake run · enterprise support Read 15 Oct 2026 · zingg.ai
Enterprise Lite Priced on request Batch architecture · probabilistic and deterministic matching · incremental runs · enterprise support Read 15 Oct 2026 · zingg.ai
Zingg Enterprise Priced on request Batch and real time · probabilistic and deterministic matching · enterprise grade support Read 3 Oct 2026 · zingg.ai
Zingg Enterprise Lite Priced on request Batch · probabilistic and deterministic matching · enterprise grade support Read 3 Oct 2026 · zingg.ai

Every Zingg plan and price →

Strong points and limits

Strong points

  • Community supports millions of records.
  • Training uses labeled pairs from the user's data.
  • Matching combines machine learning and deterministic rules.
  • Listed matching covers seven languages.
  • Community costs nothing.

Limits

  • The training workflow requires labeling 40–50 matching pairs.
  • Real-time architecture is listed for Enterprise only.

Who it suits

Zingg suits analytics engineers and data scientists who need to integrate siloed data and build unified views. Its self-hosted Linux setup and record-matching approach are relevant to teams managing entity resolution.

Verdict

Zingg combines machine learning and deterministic matching for entity resolution, with a free Community edition for batch processing.

Where Specifiction places it

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