Mostly AI
Synthetic data platform for privacy-safe AI analytics and data access
Strong buy for enterprises already on Databricks or AWS needing privacy-safe synthetic data at scale. The open-source SDK and agentic automation are genuine differentiators. Opaque pricing and Kubernetes dependency limit appeal for smaller teams.
- Data teams needing high-fidelity synthetic data for ML training with privacy guarantees
- Enterprises using Databricks or AWS to accelerate AI innovation
- Organizations requiring multi-table and time-series synthetic data with referential integrity
- Analysts wanting natural-language data insights and automated orchestration
- Teams looking for a free or transparently-priced synthetic data tool
- Non-technical users needing a fully no-code platform with managed cloud hosting
- Small organizations lacking infrastructure for Kubernetes or OpenShift
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Skip MOSTLY AI if you need a simple, low-cost synthetic data tool with transparent pricing and no Kubernetes requirement.
Contact-only pricing means you must negotiate a contract; no self-serve entry point exists, so even a small team faces sales friction.
MOSTLY AI's enterprise pricing is opaque, making it suited for larger budgets. For SMBs, alternatives like Tonic.ai offer transparent per-row pricing. The open-source SDK is free, but the platform's full value requires a paid contract.
In short
Mostly AI — Synthetic data platform for privacy-safe AI analytics and data access. Best for Data teams needing high-fidelity synthetic data for ML training with privacy guarantees, Enterprises using Databricks or AWS to accelerate AI innovation, Organizations requiring multi-table and time-series synthetic data with referential integrity. Contact Sales pricing.
What's new in Mostly AI
Checked 11 days agoAcross the latest 6 updates: 2 feature updates and 4 news mentions.
A Comparison of Synthetic Data Vault and MOSTLY AI, Part 3.2: Foreign Key Experiment, Revisited
Revisits foreign key experiment comparing SDV and MOSTLY AI libraries for synthetic data generation.
Turning a Complex Synthetic Data Workflow into a Simple Dialogue
Simplifies synthetic data workflows via LLM-powered conversational interface.
Sonnet 4.5's Analytical Instincts
Compares Sonnet 4.5 model's autonomous data analysis against other models across three domains.
Mock Data That Acts Like Reality
Generates realistic mock data that structurally mimics production data without exposing sensitive info.
Data for Everyone: A Manifesto
Advocates for breaking barriers to data access, safety, and usability to democratize data intelligence.
The 0.4% Opportunity
Analyzes OpenAI's ChatGPT usage report, identifying billion-dollar opportunities in verticalized AI startups.
What independent users actually report about Mostly AI
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
77 mentions across 5 sources (Hacker News, Bluesky, Stack Overflow, GitHub, Lemmy).
- +Open-source Synthetic Data SDK under Apache v2 license.
- +Multi-table synthesis with referential integrity and time-series support.
- +Differential privacy with temperature control for privacy preservation.
- +Agentic data science automation reduces manual training effort.
- +Natural language AI Assistant executes Python on live data.
- −Almost no community feedback on Reddit, Hacker News, or Product Hunt.
- −Contact-only pricing creates uncertainty for budget planning.
- −Limited independent reviews or third-party benchmarks.
- −No clear support channels or response times documented.
- −No published uptime or reliability guarantees.
- • Infrastructure costs for self-hosting on Kubernetes or OpenShift
- • Potential overage fees for data connectors or API usage
Viability Score
How likely is Mostly AI to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- High-fidelity synthetic data generation via TabularARGN
- Agentic data science automation for training and sampling
- Natural language AI Assistant executing Python code
- Mock data generation for safe experimentation and testing
- Simulated data for edge-case and what-if modeling
- Multi-table synthesis with referential integrity
- Time-series support and data rebalancing
- Differential privacy with temperature control
- Deployment on Kubernetes or Red Hat OpenShift
- Open-source Synthetic Data SDK under Apache v2 license
- Data connectors for Databricks, AWS, Snowflake, BigQuery
- Real-time data access from production systems (e.g., Databricks)
- Star schema and nested sequences support
- REST API and Python Client for programmatic access
- Conditional simulation and seeded generation
About Mostly AI
MOSTLY AI, powered by Syntho, is a data intelligence platform that enables organizations to securely access, generate, and analyze data while preserving privacy. It serves data teams, analysts, and enterprises looking to accelerate AI innovation with high-fidelity synthetic data, mock data for testing, and simulated data for edge-case and what-if modeling. The platform features an agentic data science layer that automates training and sampling, plus a natural-language AI Assistant that executes Python code on live production data. Core capabilities include multi-table synthesis with referential integrity, time-series support, and differential privacy with temperature control. The open-source Synthetic Data SDK (Apache v2) enables local generation using the TabularARGN model, and the platform deploys on Kubernetes or OpenShift. Integrations span Databricks, BigQuery, Snowflake, and AWS, with REST API and Python Client for programmatic access. Recent blog posts highlight comparisons with SDV for foreign key generation and LLM-powered conversational workflows. Compared to Gretel or Tonic, MOSTLY AI differentiates with its open-source SDK, deeper Databricks/AWS integration, and a privacy-first agentic layer.
Behind the Verdict
MOSTLY AI is a solid pick if you're a mid-to-large enterprise running on Databricks or AWS and need synthetic data that passes privacy muster. The agentic layer is a real time-saver—automating the train-sample loop and letting you query production data in natural language. The open-source SDK under Apache v2 is a standout: you can prototype locally with TabularARGN, then scale to the full platform. Recent benchmarks (December 2025) show it handling foreign key generation well against SDV. Where it falls short: pricing is opaque (contact sales only), and the platform requires Kubernetes or OpenShift—so smaller teams without that infrastructure will struggle. The AI Assistant is nifty but still Python-based; less technical users may find it a stretch. If your stack is Snowflake-heavy, Gretel might integrate more smoothly. For Databricks loyalists, MOSTLY AI feels purpose-built. We'd reach for this when you need high-fidelity multi-table synthesis with privacy controls and can commit to the container orchestration overhead.
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Real-world workflow fit
Concrete scenarios for the personas Mostly AI actually fits — and what changes day-one when you adopt it.
You need to train an ML model on sensitive customer data but cannot move data out of the secure environment.
Outcome: Connect to Databricks via the connector, generate high-fidelity synthetic data with differential privacy, and train the model on the synthetic copy—reducing privacy risk while maintaining accuracy.
You want to explore production data for trends but lack direct access due to compliance rules.
Outcome: Use the AI Assistant to query live production data with natural language, get Python-generated insights, and share synthetic derivatives with the team without exposing sensitive information.
Use Cases
- Generate privacy-safe synthetic datasets for sharing analysis results with external partners.
- Create high-fidelity training data for machine learning models when real data is scarce or sensitive.
- Produce realistic mock data for application testing and development environments.
- Simulate edge cases and future scenarios to stress-test business strategies and algorithms.
- Enable data access for non-privileged users by providing synthetic copies of production data.
- Analyze live production data via natural language AI Assistant without exposing sensitive information.
- Collaborate across teams by sharing synthetic data generated from secure environments.
- Accelerate cloud migration by unlocking data silos with synthetic data.
Models Under the Hood
as of 2026-07-17
Limitations
- The platform focuses on structured, text, and geolocation data; unstructured data like images or audio is not supported.
- Pricing is contact-only with no public tiers, making it inaccessible for individual or small-team exploration.
- The platform is designed for deployment on Kubernetes or OpenShift, which may be excessive for simple use cases.
as of 2026-06-28
Where the pricing makes sense
The company stage and team size where Mostly AI's pricing actually pencils out — and where peers do it cheaper.
MOSTLY AI's enterprise pricing is opaque, making it suited for larger budgets. For SMBs, alternatives like Tonic.ai offer transparent per-row pricing. The open-source SDK is free, but the platform's full value requires a paid contract.
Setup time & first value
How long it actually takes to get something useful out of Mostly AI — broken out by persona, not the marketing-page minute.
Data scientists can install the open-source SDK and generate synthetic data locally within minutes. Full platform deployment on Kubernetes/OpenShift may take a few hours to days depending on cluster readiness. Analysts using the AI Assistant can get insights immediately after connecting to a data source.
Switching to or from Mostly AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Gretel: Export synthetic data models as CSV/Parquet and import into MOSTLY AI; retrain using the SDK or platform.
- →From Tonic.ai: Migrate data connectors and workflows; similar conceptual model but may require redefining table relationships.
- ↗To Gretel: Export generators via API and re-train in Gretel's environment; note that Gretel's pricing is usage-based.
- ↗To Tonic.ai: Tonic supports similar tabular synthetic data and may offer a simpler deployment model.
Integrations
Resources & Guides
- Resourcemostly.ai
A Comparison of Synthetic Data Vault and MOSTLY AI, Part 2: Sequential Scenario
If you’d like to recreate this experiment yourself, follow along in the companion notebook.
- Resourcemostly.ai
A Comparison of Synthetic Data Vault and MOSTLY AI, Part 3.2: Foreign Key Experiment, Revisited
If you’d like to recreate this experiment yourself, follow along in the companion notebook.
- Resourcemostly.ai
Turning a Complex Synthetic Data Workflow into a Simple Dialogue
Synthetic data workflows have traditionally required a mix of engineering effort, platform knowledge, and careful coordination across tools. You upload a
- Resourcemostly.ai
Mock Data That Acts Like Reality
I wanted to generate realistic mock data: structured, relational, and coherent enough to actually build with.
- Resourcemostly.ai
Enabling Cross-Industry Intelligence with AWS Clean Rooms and Open-Source Synthetic Data
Tl;dr: Use AWS Clean Rooms to securely merge sensitive data from multiple parties. Then generate a unified, granular synthetic dataset that anyone can safely
Official links
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Common stack mates teams adopt alongside Mostly AI, with the specific reason each pairing earns its keep.
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