MostlyAI
MostlyAI generates privacy-safe synthetic data with automated QA and a free plan for 100K rows daily.
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What is MostlyAI?
MostlyAI is a Data Intelligence Platform that enables organizations to unlock, share, and simulate data through AI-generated synthetic data. MostlyAI provides secure access to production data, generates high-quality privacy-safe synthetic data, and enables seamless analysis and sharing across teams, with agentic data science at its core.
The platform supports Real-World Data, Mock Data, Synthetic Data, and Simulated Data, with an AI Assistant for natural-language data insights, the Synthetic Data SDK for programmatic generator training, connectors for external sources, and built-in QA reports with fidelity and privacy metrics. MostlyAI serves data scientists, developers, and enterprises across finance, healthcare, insurance, retail, and the public sector, supports tabular, textual, time-series, and multi-table data, and offers enterprise deployment on Kubernetes or OpenShift plus a free plan for up to 100,000 rows daily.
MostlyAI pricing
Pricing model: Freemium
MOSTLY AI offers three pricing tiers: Free plan at $0/Forever with up to 100K rows of synthetic data daily (not a trial, free access forever with up to 5 credits per day); Team plan at $3/credit or $3/per month with 1 credit for every 1 million data points, handling over 1 billion data points monthly; Enterprise plan at $5/credit or $5/per month with same credit structure plus specialized customer assistance. The platform uses a credit-based system where teams and enterprises get 1 credit per 1 million data points.
MostlyAI pros
- Generates high-fidelity privacy-safe synthetic data
- 100x faster training with TabularARGN model architecture
- Built-in differential privacy for data protection
- Works locally - data never leaves your Python environment
- Supports mixed-type data: categorical, numerical, geospatial, text
- Handles single-table, multi-table, and time-series datasets
- AI Assistant enables natural language data insights without coding
- Free plan available forever with up to 100K rows daily
- Open Source Synthetic Data SDK under Apache v2 license
- Advanced sampling: up-sample, conditional generation, rebalancing
- Context-aware data imputation for missing values
- Statistical fairness controls to mitigate bias
- Quality assurance reports with fidelity and privacy metrics
- Seamless integration with databases and cloud storages
- Enterprise deployment on Kubernetes or OpenShift
- Export generators and share synthetic data globally
- TSTR validation: Train Synthetic Test Real methodology
- Supports Hugging Face language models fine-tuning
- GPU and CPU support for training
- Progress monitoring during generator training
MostlyAI cons
- Synthetic data may not capture all real-world complexities
- Models trained on synthetic data can overfit to synthetic characteristics
- Synthetic users don't correspond to real individuals
- Requires extra step to transfer insights back to real-world data
- Bias from original dataset is retained in synthetic dataset
- Free tier limited to 100K rows per day
- Credit-based pricing can become expensive at scale
- Learning curve for advanced sampling configuration
Frequently asked questions about MostlyAI
What is synthetic data?
Synthetic data is artificially generated data that mimics the structure and statistical properties of real-world data, but is not derived from actual events or individuals. Today, synthetic data typically refers to AI-generated synthetic data created using advanced generative models that learn from real datasets to reproduce realistic, high-dimensional data that preserves statistical patterns and relationships without containing any personally identifiable information (PII).
How is synthetic data generated?
Synthetic data is generated using various techniques including Statistical or Rule-Based Generation, Machine Learning and Generative Models like GANs and VAEs, Agent-based Modeling, and Simulation-based Approaches. MOSTLY AI uses advanced generative models based on the TabularARGN architecture that learn from real datasets to reproduce realistic data.
What are the main use cases for synthetic data?
The three most common use cases are: 1) AI and ML for training and validating machine learning models when real data is scarce or sensitive, 2) Data Sharing and Collaboration for privacy-compliant data sharing in regulated industries like healthcare and finance, and 3) Software Development and Testing for applications, databases, and data pipelines under realistic conditions without exposing real user data.
Is synthetic data privacy-safe?
When generated correctly with proper measures, synthetic data is privacy-safe because it contains no real personal information and eliminates privacy breach risk. However, synthetic data is not automatically private - techniques like GANs or VAEs must be designed to avoid overfitting, outliers must be handled properly, and validation through re-identification tests is essential.
What industries use synthetic data?
Key industries include Finance and Banking for fraud detection and credit scoring, Insurance for risk modeling and claims prediction, Healthcare for disease research and clinical model training while preserving patient confidentiality, Retail and E-commerce, and Public Sector for simulating populations and safe inter-agency data sharing.
How do I validate synthetic data quality?
Validation involves comparing statistical properties of synthetic data with real data and using Quality Assurance reports. The real benchmark is TSTR (Train Synthetic Test Real) - testing model performance trained on synthetic data using a validation set of real data. MOSTLY AI provides built-in HTML reports with fidelity and privacy metrics for visual analysis.
What data types does MOSTLY AI support?
MOSTLY AI supports mixed-type data including categorical, numerical, geospatial, and text. It handles single-table, multi-table, and time-series datasets. The platform can train generators on both tabular and textual data, including columns with text data, and maintains referential integrity between tables in multi-table scenarios.
What is the MOSTLY AI Assistant?
The MOSTLY AI Assistant provides the simplest interface for data interaction using natural language. Users can get rich data insights instantly and use all MOSTLY AI features from a single place without writing code. The Assistant can build generators and connectors, explore datasets, create artifacts, generate and execute Python code, and enrich or transform data using natural language.
How does the Synthetic Data SDK work?
The Synthetic Data SDK is an Open Source Python toolkit under Apache v2 license that allows programmatic creation, browsing, and management of Generators, Synthetic Datasets, and Connectors. It works in Local mode using your machine's compute resources or Client mode connecting to a remote MOSTLY AI Platform. Users can train generators, inspect quality with reports, generate samples, and export generators.
Can I deploy MOSTLY AI in my enterprise environment?
Yes, MOSTLY AI offers enterprise-ready scalable and secure deployment on Kubernetes or OpenShift. Officially supported distributions include Amazon EKS, Google GKE, Azure AKS, and Red Hat OpenShift. The platform runs in your secure environment with your compute, ensuring your data never leaves your environment while enabling synthetic data generation and sharing.