DataSynthesizer vs MOSTLY AI
DataSynthesizer
github.comMOSTLY AI
mostly.aiSide by side
| Figure | DataSynthesizer | MOSTLY AI |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Linux | — | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · AI Synthetic Data Generators | 7.8 (#12) | 8.1 (#9) |
| Support | The repository exposes GitHub Issues for project questions and reports. | Users can contact support by emailing [email protected]. |
Plans
In their own words
DataSynthesizer
DataSynthesizer generates synthetic data that imitates a supplied dataset while applying differential privacy techniques, according to the project. Its demonstrations cover random, independent-attribute, and correlated-attribute modes. The documented input must be a table in first normal form, and the project states that relational and unstructured data are outside its scope. Installation uses the Python command `pip install DataSynthesizer`. Users can run example notebooks with Jupyter Notebook or start the included Django project locally and open its web interface at `/synthesizer`. The project is MIT-licensed and offers GitHub Issues for questions and reports. It is intended to help data scientists work with owners of sensitive data. DataSynthesizer is free and open source, with self-hosted and web platforms listed. Its documented integrations are Python pip, Jupyter Notebook, and the companion Django interface. There is no free trial because the project lists only a free plan.
MOSTLY AI
MOSTLY AI is a data intelligence platform for accessing production data, generating synthetic, mock and simulated data, and analyzing data. Its assistant lets users request Python code in natural language and run it for analysis. The open-source Synthetic Data SDK supports local generation and uses the Apache v2 license. The platform handles numerical, categorical, date-time, text and geolocation data; it can synthesize relational tables while preserving their relationships. Other controls include Data Insights Reports, data rebalancing, missing-value imputation and generation temperature settings. Listed database connectors include MySQL, PostgreSQL, MariaDB, Oracle, Microsoft SQL Server, Snowflake, Databricks, BigQuery and Apache Hive; cloud bucket connectors include Azure, GCP and AWS. An API and Python client support integration into applications and workflows. Deployment options include Kubernetes, OpenShift and Minikube, as well as on-premises, private-cloud and air-gapped installations. The maker says the platform is SOC 2 and ISO 27001 certified. A single-node Kubernetes deployment requires at least 24 CPU cores, 48 GB RAM and 256 GB storage. The free plan is free, with no further limits stated. It is available via API, Linux, self-hosting and web.