Metasyn vs MOSTLY AI

Metasyn generates synthetic tabular data to help make sensitive datasets more accessible, reproducible, and reusable. Users fit a MetaFrame to a dataframe, save the model and settings as auditable JSON, then generate new rows. It handles varied data types, missing values, categories, unique key columns, and structured strings such as postcodes or identifiers. Metasyn supports pandas and Polars dataframes and uses Faker for localized names, emails, phone numbers, and other data. By default, its model excludes multivariate information to reduce disclosure risks. A disclosure-control plugin is available, but synthetic output still needs human review and is not guaranteed to be free of sensitive information. The plugin system allows users and organizations to add extensions. Generated dataframes can be exported to CSV, Parquet, Excel, and other formats. The free, MIT-licensed Python package is self-hosted and can be installed with pip from PyPI or directly from GitHub. The project also points users to versioned Docker containers and a command-line interface. Its intended users include public organizations, research groups, and individual researchers with sensitive datasets.

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.