Zingg
The facts below are what the maker publishes about Zingg, checked and dated. Our editors have not written about it yet.
Plans and prices
From Zingg's own pricing page, checked 15 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| Community | Free | Open-source edition · batch architecture · probabilistic matching · millions of records |
| Community (Open Source) | Free | Batch · probabilistic matching · millions of records · open source support |
| Enterprise | Priced on request | Batch and real-time architecture · probabilistic and deterministic matching · native Snowflake run · enterprise support |
| Enterprise Lite | Priced on request | Batch architecture · probabilistic and deterministic matching · incremental runs · enterprise support |
| Zingg Enterprise | Priced on request | Batch and real time · probabilistic and deterministic matching · enterprise grade support |
| Zingg Enterprise Lite | Priced on request | Batch · probabilistic and deterministic matching · enterprise grade support |
The facts
Each one read from Zingg's own pages, with the date it was checked.
What it does
- AI matching
- Its machine-learning matcher learns from labeled examples to find matches that fixed rules can miss.checked 15 Oct 2026
- CLI
- The command-line interface runs labeling, training, matching and Python programs through zingg.sh.checked 15 Oct 2026
- Community praise
- A Provenance engineering case study reports an active Slack community, regular releases and very few operational issues in production use.checked 15 Oct 2026
- Contact response
- The comparison page says the team will reach out within 1–2 business days after an inquiry.checked 3 Oct 2026
- Deterministic matching
- Enterprise editions combine probabilistic and deterministic matching in one flow and support identifiers such as email, SSN and passport ID.checked 15 Oct 2026
- Docker install
- The documented Docker workflow pulls zingg/zingg:0.5.0 and starts it with an interactive shell.checked 15 Oct 2026
- Documentation depth
- Documentation covers installation, command-line use, Python APIs, connectors, cloud platforms, security, FAQs and step-by-step examples.checked 15 Oct 2026
- Entity coverage
- The product is positioned for customers, suppliers, products, locations and other entity types.checked 15 Oct 2026
- File formats
- The connector layer supports formats including Parquet, Avro, JSON, XLSX, CSV and TSV.checked 15 Oct 2026
- Funding information
- The opened Crunchbase page exposed performance metrics and placeholder recent-event entries but no usable funding amount or named funding round.checked 15 Oct 2026
- GitHub activity
- The public repository showed about 1.3k stars, 178 forks, 91 issues and 23 pull requests when opened.checked 15 Oct 2026
- Incremental processing
- Enterprise editions support incremental runs against previously matched data, and Enterprise also supports real time architecture.checked 3 Oct 2026
- 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.checked 15 Oct 2026
- Independent integration evidence
- AWS published a 23 May 2024 tutorial showing Zingg used inside AWS Glue notebooks and ETL jobs for entity resolution.checked 15 Oct 2026
- 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.checked 15 Oct 2026
- 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.checked 15 Oct 2026
- Integrations
- Documented connectors include Databricks, Snowflake, Cassandra, S3, Azure, Elastic, Exasol, relational databases and Spark-supported sources.checked 15 Oct 2026
- 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.checked 3 Oct 2026
- Interactive labeling
- Zingg provides an interactive labeling workflow so users can train matching models with selected record pairs.checked 15 Oct 2026
- Language matching
- The product page lists matching for Chinese, Japanese, Thai, Hindi, German, French, and Greek without language specific setup.checked 3 Oct 2026
- Licence
- The public Zingg repository is licensed under GNU Affero General Public License version 3.0.checked 15 Oct 2026
- 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.checked 15 Oct 2026
The company
- Headquarters
- Lewes, Delaware, United Stateschecked 28 Sep 2026
- 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.checked 15 Oct 2026
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