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Made in AT Austria AI for Data Science

Mostly AI — AI for Data Science & Advanced Predictive Analytics

Generate high-fidelity, privacy-safe synthetic data to accelerate model training and securely share insights across your organization.

Leverage an open-source SDK or a full enterprise platform to work with production-like data without the compliance risks.

Best forAI for Data Science
DifferentiatorDual offering of a free open-source SDK and a full enterprise platform with built-in differential privacy.
ProofSwiss Post increased usable data access from 11% to 100%.
Try Mostly AI
Free Plan Pricing on request
Free plan verified · free AI tools for Data Science
8.5 Zekai
Privacy-Safe Data Synthesis
AI for Data Science & Advanced Predictive Analytics
Ease of Use
8.0
Accuracy
8.8
Value
9.0
Time Saving
8.2
Synthetic DataOpen Source SDKDifferential PrivacyData SimulationEnterprise Security
📊 Independently reviewed and scored. How we score →
✍️ Reviewed by Zekai Editorial Team · Updated August 7, 2026
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Zekai Verdict

What is it?
Mostly AI provides a data intelligence platform for Data Scientists to generate high-quality synthetic data.
Best for
Best for data science teams needing to generate high-quality, privacy-safe synthetic data for model development…
Not ideal for
Pricing for the full Data Intelligence Platform is not public and requires a demo.
Price
Free plan
Zekai Score
8.5/10
Visit official site ↗Compare alternatives →Why this score →
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Hand-scored by Zekai

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⚡ Quick answer

For Data Science and Advanced Predictive Analytics, Mostly AI is a leading tool for generating high-fidelity, privacy-safe synthetic data. It allows teams to accelerate model training and testing by providing production-like data without compliance risks, as proven by customers like Swiss Post who unlocked 89% more data for analysis.

CategorySynthetic Data Generation
Best ForData science teams needing production-like data without privacy risks.
Price FromFree (SDK)
FreeOpen Source SDK
DifferentiatorDual offering of a free open-source SDK and a full enterprise platform with built-in differential privacy.
ProofSwiss Post increased usable data access from 11% to 100%.
Rating8.5/10
📖 About Mostly AI
Watch

See Mostly AI in action

How It Works

Your workflow, automated

1
Install & Connect
Install the open-source SDK via pip (`pip install -U mostlyai`) and connect to your source data locally within your Python environment.
2
Train & Inspect
Train a synthetic data generator on your data using `mostly.train()` and inspect the quality reports to ensure statistical accuracy.
3
Generate & Share
Use `mostly.probe()` to generate millions of new privacy-safe samples or export the generator model to share with your team.
Ready to automate your workflow with Mostly AI?
Try Mostly AI →
Real Impact

Before & After

❌ Before

Data access is blocked or severely limited by privacy and compliance rules, stalling projects.

Only 11% of data is usable
✅ After

Full data access is enabled by generating a statistically identical, yet fully private, synthetic dataset.

100% of data is usable
Prompt Templates

Try it with these prompts

Copy any prompt and paste it directly into the tool.

Analyze production data for insights

Access live production data from [Data Source, e.g., Databricks] and analyze patterns, monitor performance, and track trends. Use natural language to generate Python code for analysis and gain AI-powered insights into re…

Generate synthetic data for testing

Create high-fidelity, privacy-safe synthetic datasets that mimic real production data. Use this mock data for safe experimentation, prototyping, and testing in staging environments where production data access is limited…

Simulate future scenarios

Model edge cases and future 'what-if' conditions using controlled data simulation based on real or synthetic data. This is ideal for stress testing strategies and validating algorithm performance in a safe, simulated env…

AI Prompts

Prompts for Data Science

Prompt 01 Comprehensive Data Profile
Act as a data quality analyst. I am providing you with a pandas DataFrame named `df`. Its schema is as follows: `[[PASTE SCHEMA OR HEAD() OUTPUT HERE]]`. Your task is to perform a comprehensive data profiling. For each column, provide: 1. D…
Prompt 02 Missing Value Imputation Plan
has missing values. Here is the output of
Prompt 03 Outlier Detection Script
Generate a Python script that uses the Interquartile Range (IQR) method to identify outliers in the following numeric columns of a pandas DataFrame `df`: `[[LIST_OF_NUMERIC_COLUMNS]]`. The script should: 1. Calculate Q1, Q3, and IQR for eac…
See all 50 AI prompts for Data Science →
Social Proof

Trusted by professionals

Ease of Use
8.0
Accuracy
8.8
Value
9.0
Time Saving
8.2

"This is the solution we've needed for years. We went from using a small, restricted sample of our customer data to being able to analyze patterns across the entire base with a 100% private synthetic version. Game-changing."

Sanzio M., Data Scientist · June 2026

"The ability to generate a synthetic asset that mimics a valuable dataset is critical for data sharing, especially in Clean Room settings. Mostly AI makes this process seamless and secure for enterprise collaboration."

Nikhil G., Product Lead · May 2026

"The open-source SDK is fantastic and incredibly powerful for local development. However, figuring out the full enterprise platform requires a sales call, and the pricing is a black box until you get a quote."

Devon R., ML Engineer · April 2026

"We use synthetic data in every environment except production. Access to data in UAT is very limited, and this tool allows our teams to test effectively without ever touching sensitive production data."

Vukola M., Databricks Solution Manager · March 2026
Start Free Today →
Connects With

Mostly AI integrations

Databricks AWS Kubernetes OpenShift Python
Setup complexity: Advanced
Mostly AI provides a data intelligence platform for Data Scientists to generate high-quality synthetic data. It enables you to create privacy-safe datasets that mimic real-world data, accelerating AI development, streamlining testing workflows, and enabling secure collaboration without exposing sensitive information.
The decision

Is Mostly AI worth it?

Built for
Data Scientists, ML Engineers, Data Analysts, and Data Platform Managers.
Effort to adopt
Advanced
Compliance
The platform is designed to be 'privacy-safe' and offers 'built-in differential privacy'. However, specific compliance certifications like GDPR, HIPAA, or SOC 2 are not publicly documented — verify with vendor.
Who It's For

Why Data Science & Advanced Predictive Analytics choose this tool

🎯
Built for
Best for data science teams needing to generate high-quality, privacy-safe synthetic data for model development, testing, and secure collaboration without using sensitive production data.
In-Depth Overview
For data science and advanced analytics, secure access to high-quality data is the primary bottleneck. Mostly AI directly addresses this by enabling you to generate statistically representative synthetic data that is safe to use for model training, testing, and analysis. Its core strength lies in its dual offering: a fully permissive, open-source Python SDK (Apache v2) for local development and a full Data Intelligence Platform for enterprise-scale collaboration. Powered by the TabularARGN model, it supports complex tabular and textual datasets and includes built-in differential privacy for an added layer of security. This means you can stop waiting for permission to access sensitive production data. Instead, you can generate an unlimited supply of safe, realistic data to train models, test edge cases, and validate algorithms. The proof is in customer results; Swiss Post used the SDK to increase their usable customer data from 11% to 100%, creating a fully private synthetic dataset that mirrored their entire customer base.

Key Use Cases

🔬
Accelerate Model Training Cycles
Data Scientist
Generate vast amounts of privacy-safe data to train and validate machine learning models without waiting for access to restricted production environments.
Faster time-to-production for models
🛡️
Enable Secure Data Sharing
Data Privacy Officer
Create privacy-safe synthetic datasets that can be shared freely across teams, with partners, or in cloud environments like Databricks Clean Rooms.
100% private, shareable data
🧪
Improve Software Testing Robustness
QA Engineer
Use realistic mock and synthetic data for UAT and staging environments where using real production data is prohibited, ensuring more thorough testing.
Reduced bugs in production
📈
Unlock Siloed Data for Insights
Data Analyst
Analyze patterns across an entire user base by creating a synthetic version, overcoming privacy restrictions that limit access to a small fraction of data.
89% more data made accessible
✓ Pros
Free, open-source Python SDK allows for local development and testing.
Creates statistically representative data, preserving patterns from the original source.
Enables safe data sharing and collaboration without exposing PII.
Supports advanced use cases like simulating 'what-if' scenarios and edge cases.
Integrates with enterprise data platforms like Databricks.
· Cons
Pricing for the full Data Intelligence Platform is not public and requires a demo.
Requires Python and data science expertise to use the SDK effectively.
The quality of synthetic data is dependent on the quality of the source data.
Full capabilities of the enterprise platform vs. the SDK are not exhaustively detailed.
⚡ Editorial Verdict

Mostly AI is a powerful, specialized tool for overcoming data access and privacy hurdles. The free open-source SDK is a significant asset for individual developers and smaller teams. The main trade-off is the lack of public pricing for the full enterprise platform, which makes evaluating its total cost of ownership difficult without a sales demo.

See all 152 AI tools for Data Science → Browse the AI Directory →
Questions & Answers

Frequently asked questions

What is the best tool for creating synthetic data for machine learning models?

+
Mostly AI is a top contender, offering a powerful platform and an open-source Python SDK to generate high-fidelity, privacy-safe synthetic data that mimics real-world datasets for robust model training.

How can I generate privacy-safe test data that mimics my production database?

+
You can use Mostly AI's SDK or platform to connect to your production data and generate a synthetic version. This new dataset will retain the statistical properties of the original but contain no sensitive information, making it safe for testing.

Which synthetic data tools have an open-source Python SDK?

+
Mostly AI provides a fully permissive open-source Python SDK under an Apache v2 license, allowing data scientists to generate synthetic data locally within their own environment.

Can I use synthetic data to overcome data access restrictions for model training?

+
Yes, this is a primary use case for Mostly AI. By generating a synthetic dataset, you can bypass restrictions on production data access, enabling faster and more flexible model development, as demonstrated by Swiss Post unlocking 89% more data.

What are the top platforms for enterprise-grade synthetic data generation?

+
The MOSTLY AI Data Intelligence Platform is designed for enterprise use, offering scalable deployment on Kubernetes or OpenShift, team collaboration features, and secure data sharing capabilities.

Does Mostly AI support data simulation?

+
Yes, the platform allows you to model edge cases, future scenarios, and 'what-if' conditions using controlled data simulation based on either real or synthetic data.

Last reviewed:

Plans & Pricing

Mostly AI pricing and plans

Free
$0
Get Started Free
Pro
Pricing on request
The MOSTLY AI Data Intelligence Platform requires a demo for pricing details. A free, open-source SDK is also available.
Start Pro Plan
Enterprise
Pricing on request
Tailored for teams and large organisations.
Contact Sales

Prices and features are updated regularly but can change at any time — always confirm on the official website. Some links on this page are affiliate links.

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Free guide

Take it with you

Getting Started with Synthetic Data
  • **What is Synthetic Data?** Understand how statistically-representative data can accelerate your workflows.
  • **Key Use Cases:** Learn when to use synthetic data for model training, testing, and analysis.
  • **Local vs. Platform:** Decide whether the free SDK or the enterprise platform is right for you.
  • **Understanding Differential Privacy:** A quick primer on the privacy guarantees built into Mostly AI.
  • **Inspecting Quality:** Learn how to read the generator reports to validate your synthetic data.
+3 more steps inside the guide
Send me the full guide

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About Mostly AI

Full Description

Mostly AI provides a data intelligence platform for Data Scientists to generate high-quality synthetic data. It enables you to create privacy-safe datasets that mimic real-world data, accelerating AI development, streamlining testing workflows, and enabling secure collaboration without exposing sensitive information.

Editorial Verdict

Mostly AI is a powerful, specialized tool for overcoming data access and privacy hurdles. The free open-source SDK is a significant asset for individual developers and smaller teams. The main trade-off is the lack of public pricing for the full enterprise platform, which makes evaluating its total cost of ownership difficult without a sales demo.

Last reviewed:
Disclaimer
Zekai is an independent AI tools directory. We are not affiliated with, endorsed by, or officially connected to Mostly AI unless clearly stated. All product names, logos, and brands are the property of their respective owners and are used for identification purposes only. The information on this page — including pricing, features, and availability — is general information, may have changed since our last review, and is not professional advice. Zekai Scores and verdicts are our editorial opinion. Some outbound links are affiliate links that may earn us a commission at no extra cost to you. Spotted outdated or incorrect information? Request a correction →
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