AI Frameworks · mostly-ai

mostlyai

MostlyAI is a Python SDK for generating high-fidelity synthetic tabular and language data locally or via remote endpoints. It offers differential privacy, quality metrics, and flexible sampling for privacy-safe data generation without leaving your infrastructure.

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Source: GitHub — github.com/mostly-ai/mostlyai

782

GitHub stars

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	mostly-ai/mostlyai
Owner	mostly-ai
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	782
Forks	68
Open issues	1
Latest release	6.1.1 (2026-05-08)
Last updated	2026-05-08
Source	https://github.com/mostly-ai/mostlyai

What mostlyai is

Open-source Python toolkit using TabularARGN for efficient tabular synthesis, LSTM or Hugging Face model fine-tuning for language data, and optional differential privacy. Supports LOCAL (on-device) and CLIENT (remote endpoint) modes with GPU/CPU compute and connectors to external data sources.

Quickstart

Get the mostlyai source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/mostly-ai/mostlyai.git2cd mostlyai3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Privacy-Compliant Data Sharing

Generate synthetic datasets for sharing with third parties or testing, eliminating re-identification risk while preserving statistical properties of original data. Useful for GDPR/CCPA compliance, testing environments, and data monetization.

Machine Learning Model Development on Sensitive Data

Train and validate ML models on synthetic data before deploying on real data, reducing data exposure during development. Supports multi-table and time-series scenarios with conditional sampling.

Data Augmentation for Underrepresented Segments

Rebalance imbalanced datasets, impute missing values contextually, and generate additional samples for minority classes or rare cohorts without synthetic bias.

Implementation considerations

LOCAL mode requires sufficient local compute (CPU is supported but GPU recommended for LLM fine-tuning). Plan resource budgeting early, especially for large datasets or language models.
Differential privacy must be explicitly enabled and tuned per use case; default configurations do not guarantee DP. Understand the privacy-utility trade-off before production use.
Quality metrics are provided in HTML reports; integrate these into your data validation pipeline to prevent low-fidelity synthetic data from entering production.
Multi-table and time-series synthesis require careful schema definition and relationship configuration. Start with single-table scenarios to validate workflow.
Generator serialization supports import/export (`.zip` files). Plan for versioning, reproducibility, and change management of trained generators.

When to avoid it — and what to weigh

Real-time, Streaming Data Synthesis — SDK is batch-oriented; not designed for continuous, low-latency synthetic data generation pipelines. Training is compute-intensive and offline.
Unstructured or Highly Complex Data — While it supports text via LSTM/Hugging Face, the primary focus is tabular and semi-structured data. Images, audio, video, or highly irregular schemas require external adaptation.
Regulatory Certainty Demands (Pre-Deployment) — Differential privacy is supported but requires explicit configuration and tuning. No formal third-party security audit or compliance certification is provided in the README. Verify with legal/compliance before handling regulated data.
Minimal Dependency and Isolation Requirements — SDK requires Python 3.11+, PyTorch, and multiple ML libraries. GPU setups add system complexity. Not suitable for lightweight, hermetic deployments.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution under the same license terms. No proprietary restrictions on synthetic data output.

Apache-2.0 is a permissive OSI license and clearly permits commercial use, including proprietary derivative works. The license does not restrict commercial use of generated synthetic data. However, verify your organization's legal counsel on data ownership, liability, and regulatory compliance (e.g., GDPR, CCPA, industry-specific rules) before processing regulated datasets in production.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

No formal third-party security audit or penetration test is documented. Differential privacy is supported but not enabled by default and requires explicit tuning. LOCAL mode keeps data on-device (reduces exfiltration risk). CLIENT mode transmits data to remote endpoint (verify endpoint security posture, TLS, authentication, and compliance with your data governance). No mention of input sanitization, injection attack mitigation, or secrets management hardening in README. Conduct threat modeling for regulated data use cases before production deployment.

Alternatives to consider

Synthetic Data Vault (SDV) by DataCebo

Similar focus on tabular and time-series synthesis. Open-source with permissive license. Mature ecosystem but different modeling approach (Gaussian Copula, CTGAN). Consider if you prefer model variety or have SDV integrations already.

Gretel (Gretel.ai)

Commercial platform with built-in compliance (PII detection, regulatory templates). Offers managed service model with formal support and audit trails. Better for enterprises requiring SLAs and hands-off data governance but higher cost.

YData (formerly Synthflow)

Enterprise synthetic data platform with stronger ML Ops integration, data quality assurance, and compliance certifications. Targets Fortune 500 workflows. More expensive than open-source MostlyAI but includes managed hosting and dedicated support.

Software development agency

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Install MostlyAI today and start training synthetic data generators on your own infrastructure. No vendor lock-in, full control, Apache 2.0 open-source.

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mostlyai FAQ

Can I use MostlyAI to generate synthetic data without sending data to an external server?

Yes. LOCAL mode (default) trains and generates data locally on your own compute. No data leaves your infrastructure unless you explicitly use CLIENT mode or connectors to external data sources.

Does MostlyAI guarantee differential privacy by default?

No. Differential privacy is an optional feature that must be explicitly enabled and configured. Default configurations do not enforce DP. Review the technical white paper and quality reports to validate privacy levels for your use case.

What are the system requirements?

Python 3.11+, pip/uv, and PyTorch. LOCAL mode works on CPU but GPU is strongly recommended for language model fine-tuning. Memory and storage scale with dataset size and model complexity.

Can I use MostlyAI to generate synthetic data for commercial products?

Yes. Apache-2.0 license allows commercial use. However, ensure your organization complies with data protection regulations (GDPR, CCPA, industry standards) and consult legal counsel regarding liability, ownership, and regulatory classification of synthetic data outputs.

Software development & web development with DEV.co

Adopting mostlyai is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate ai frameworks software in production.

Generate Privacy-Safe Synthetic Data Locally

Install MostlyAI today and start training synthetic data generators on your own infrastructure. No vendor lock-in, full control, Apache 2.0 open-source.

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