MOSTLY AI

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Introduction

MOSTLY AI’s Synthetic Data SDK is a Python toolkit for high-fidelity, privacy-safe synthetic data, according to its README. LOCAL mode trains and generates on your own compute (the default), and CLIENT mode connects to a remote MOSTLY AI platform endpoint.

The SDK manages generators trained on tabular or language data, synthetic datasets created from them, and connectors to databases and cloud storage. Its documentation covers options such as differential privacy and fairness settings, and each generator comes with a quality assurance report comparing synthetic and original data. The README highlights a fully permissive open-source licence (Apache-2.0).

Release 6.2.1 shipped in October 2026. The company also offers a hosted platform.

In the quality loop it supports privacy-safe test and analytics data.

Features

1. Local training: Runs on your compute.
2. Client mode: Use the hosted platform.
3. Pre-trained generators: Import and probe generators.
4. Privacy options: Differential privacy settings in the docs.
5. QA reports: Compare synthetic and real data.

Pros

1. Permissive licence: Apache-2.0 SDK.

Cons

1. Compute needs: Training can be heavy.
2. Needs source data: Generators learn from real data.

Usage and Links (Example)

from mostlyai.sdk import MostlyAI
mostly = MostlyAI()                      # LOCAL mode by default
g = mostly.train(name="orders", data=df) # train a generator
g.reports(display=True)                  # quality assurance report
sd = mostly.generate(g)                  # synthetic dataset
synthetic_df = sd.data()

Resources:

• MOSTLY AI website
• GitHub repository

Pricing

SDK free (Apache-2.0); hosted platform plans. See site.

Recommended for?

1. Teams needing privacy-safe synthetic data with an open SDK.
2. Not a fit for simple fake values; use Faker.

Conclusion

MOSTLY AI’s open SDK makes high-quality synthetic data available locally.

On CheckOps, MOSTLY AI is filed under the Integration testing stage. Related tools listed here include Synthetic Data Vault (SDV), Synthea, Tonic Structural, GenRocket, K2view Test Data Management and Faker (Python). Before choosing, compare them on licence, supported languages and platforms, hosting model, and how well each fits your existing CI pipeline. Facts on this page were checked against the official website, documentation and repository on 11 November 2026; statements the vendor makes about its own product are marked as vendor claims.