Mostly AI
Generate privacy-safe synthetic data with MOSTLY AI's Apache v2 SDK and TabularARGN engine.
If your bottleneck is training or testing on data you're contractually barred from touching, MOSTLY AI pairs a genuinely usable open-source SDK with enterprise deployment controls, and the November 2025 dialogue interface plus October 2025 mock-data coherence work both lower the skill floor. We'd point teams running Databricks, Snowflake, or BigQuery here before a masking tool. Where it bites: the platform half assumes Kubernetes or OpenShift, so infra-light teams are stuck with the SDK, and
Verified 1h ago · liveness 74/100 · cite: rightaichoice.com/tools/mostly-ai
- Enterprises on Databricks or AWS needing high-fidelity synthetic data for ML training without exposing records
- Data scientists who want to evaluate synthesis quality locally with the Apache v2 SDK before buying
- Teams that must share datasets with partners or run cross-industry analysis under privacy constraints
- Organizations needing multi-table and time-series synthesis that preserves referential integrity
- Small teams without Kubernetes or OpenShift infrastructure to host an enterprise deployment
- Projects that only need column masking or a static anonymized copy of one flat table
- Use cases whose real requirement is direct real-time access to production data, not a synthetic layer
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Skip MOSTLY AI if you only need to mask columns in a single flat table, or if your team has no Kubernetes or OpenShift capacity to run the enterprise platform.
The platform meters usage in credits, with a dedicated 'What are credits?' page in the docs, so heavy training and sampling will consume an allowance you should model before rollout.
The Apache v2 Synthetic Data SDK is free and open source, so evaluation and local generation cost nothing beyond your own compute. Paid positioning is enterprise-grade: this is a platform you deploy on Kubernetes or OpenShift, which places it alongside data-privacy and governance suites rather than self-serve analytics tools. If you only need masking or flat-table anonymization, a lightweight masking tool will cost less in both license and infrastructure.
In short
Mostly AI — Generate privacy-safe synthetic data with MOSTLY AI's Apache v2 SDK and TabularARGN engine. Best for Enterprises on Databricks or AWS needing high-fidelity synthetic data for ML training without exposing records, Data scientists who want to evaluate synthesis quality locally with the Apache v2 SDK before buying, Teams that must share datasets with partners or run cross-industry analysis under privacy constraints. Contact Sales pricing.
What's new in Mostly AI
Checked 8 days agoAcross the latest 3 updates: 2 feature updates and 1 news mention.
Turning a Complex Synthetic Data Workflow into a Simple Dialogue
MOSTLY AI introduced a dialogue-based interface that breaks synthetic data generation into conversational steps, aimed at users who are not synthetic data specialists.
Mock Data That Acts Like Reality
Mock data generation gained relational coherence, so generated test data stays consistent across linked tables rather than drifting apart in staging and demo environments.
Data for Everyone: A Manifesto
The company published a manifesto describing its vision for democratizing data access with safety, fairness, and intelligence, framing the four-mode data platform strategy.
What people actually say about Mostly AI — is it worth it?
We scanned public community sources for Mostly AI on Oct 14, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 2 of the posts we fetched could be positively tied to Mostly AI. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Mostly AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Privacy-safe synthetic tabular and textual data generation via TabularARGN
- Apache v2 open-source Synthetic Data SDK, installed with pip install mostlyai
- Multi-table synthesis preserving referential integrity across linked tables
- Time-series and events data synthesis for relational and sequential datasets
- Built-in differential privacy with temperature control for utility tuning
- AI Assistant writes and runs Python code from natural-language prompts
- Dialogue-based interface turning synthesis workflows into conversational steps (Nov 2025)
- Mock data generation with relational coherence across linked tables (Oct 2025)
- Simulated data for edge cases, what-if scenarios, and algorithm stress testing
- Real-World Data mode surfaces insights from live systems like Databricks
- 100x faster generator training with automated training and sampling
- Quality reports on trained generators plus conditional and seeded generation
- Generate up to one million synthetic samples locally from a trained generator
- Export trained generators to a file and upload to the platform for sharing
- REST API and Python client for programmatic platform access
About Mostly AI
MOSTLY AI is a synthetic data platform built for teams that can't train models, test software, or share records against live production data. The platform runs four data modes: Real-World Data pulls insights from live systems like Databricks without moving raw records, Mock Data produces realistic structured and text records for staging and demos, Synthetic Data generates high-fidelity privacy-safe datasets for training and partner sharing, and Simulated Data models edge cases and what-if conditions for stress testing. Every mode sits under one roof, so a data team can prototype on mocks and ship on synthetic without switching vendors. The engine underneath is TabularARGN, the vendor's tabular model architecture with built-in differential privacy and 100x faster generator training. Multi-table synthesis preserves referential integrity across linked tables, time-series support keeps sequential datasets realistic, and quality reports let you audit a generator before you use it. An AI Assistant writes and runs Python against your data from natural-language prompts, which is how analysts without notebook habits get to production queries. The November 2025 dialogue interface turned the multi-step synthesis workflow into a conversation for non-specialists, and the October 2025 release gave mock data relational coherence so linked test tables stop drifting apart. The Synthetic Data SDK is the piece most buyers underrate. It's Apache v2, installed with pip install mostlyai, and lets you train a generator, inspect its reports, and probe up to a million samples locally with your data never leaving your Python environment. Generators export to a file and upload to the platform for sharing. Enterprise deployment runs on Kubernetes or OpenShift with connectors for Databricks, Snowflake, BigQuery, and AWS. Positioning-wise, masking vendors rewrite your existing records and clean rooms restrict access to them. MOSTLY AI generates new records instead, and you can produce those
Behind the Verdict
Synthetic data vendors love to talk about privacy; MOSTLY AI's actual differentiator is that you can evaluate the product before you pay for it. The Synthetic Data SDK is Apache v2, it's a pip install, and it lets you train a generator, read its quality reports, and generate a million samples locally with your data never leaving your environment. That's a rare thing in an enterprise data category. The natural workflow: prototype in Python, confirm the fidelity is real, then upload generators to the platform when you need team sharing or deployment controls. Where the platform earns its keep is multi-table work. Referential integrity across linked tables is the failure mode that kills naive synthesis projects, and the October 2025 mock data upgrade extended that coherence into staging environments. If your test suites or demos depend on a customer table, an orders table, and an events table staying consistent with each other, that release matters more than any benchmark number. Pick this when your organization is on Databricks, Snowflake, BigQuery, AWS, or Azure and the blocker is either data access restrictions or model training on records you can't ship. The four-mode structure (real-world, mock, synthetic, simulated) is genuinely useful for teams that don't want three separate vendors for prototyping, training, and stress testing. Erste Group's Databricks team is a fair poster child: they explicitly can't test on production data, which is exactly the gap synthetic fill. Pass if your need is column masking on a single flat table. Masking tools are cheaper, simpler, and solve a different problem. Also pass if you have no Kubernetes or OpenShift footprint and no appetite to build one: the SDK is yours for free, but you'll be reading generator files and uploading them
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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.
Install the SDK with pip install mostlyai, train a generator on a sensitive customer table inside your own Python environment, inspect quality reports, and probe up to a million synthetic samples — all without data leaving the environment.
Outcome: You get a quality-verified generator and synthetic samples you can use for model development, and you can export the generator to a file for later upload if the organization buys the platform.
Open the platform, use the AI Assistant to describe the analysis in natural language, and let it create and run Python against the connected Databricks system; then move to the dialogue-based interface to walk through generating a synthetic dataset.
Outcome: You produce privacy-safe datasets and insights on production data without hand-writing synthesis code, using the conversational flow shipped in November 2025.
Use Mock Data with relational coherence to generate realistic structured and text-based records that stay consistent across linked tables, and load them into staging, demo, or pre-production environments.
Outcome: Test environments get realistic data that holds together across relationships, replacing the fragile hand-built fixtures teams maintain today.
Use Cases
- Generate privacy-safe synthetic datasets to share analysis with external partners.
- Create high-fidelity training data for machine learning when real data is scarce or sensitive.
- Produce realistic mock data with relational coherence for application testing and staging.
- Simulate edge cases and future scenarios to stress-test business strategies and algorithms.
- Give non-privileged users access to data through synthetic copies of production systems.
- Analyze live production data via the natural-language AI Assistant without exposing records.
- Share synthetic datasets across teams generated inside a secure on-premise environment.
- Accelerate cloud migration by unlocking data silos before moving workloads.
Models Under the Hood
as of 2026-10-18
Limitations
- MOSTLY AI generates privacy-safe structured data — numerical, categorical, date-time, text, and geolocation types — so it is not an unstructured-media synthesis tool.
- Enterprise deployment runs on Kubernetes or OpenShift, which the vendor's own installation docs treat as a platform project with a deployment checklist, Helm chart, compute configuration, ingress controllers, TLS certificates, and an optional external PostgreSQL database.
- The platform is accessed through a web UI, a REST API, and a Python SDK, which means the local SDK path requires Python competency even though the dialogue interface lowers that bar for platform users.
- Quality depends on your source data: the docs devote dedicated pages to preparing data, CSV requirements, encoding types, _RARE_ values, and privacy best practices for original data, so poor inputs will produce poor generators.
as of 2026-09-30
Verification history
We have re-verified Mostly AI 108 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
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Showing the 6 most recent of 108 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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.
The Apache v2 Synthetic Data SDK is free and open source, so evaluation and local generation cost nothing beyond your own compute. Paid positioning is enterprise-grade: this is a platform you deploy on Kubernetes or OpenShift, which places it alongside data-privacy and governance suites rather than self-serve analytics tools. If you only need masking or flat-table anonymization, a lightweight masking tool will cost less in both license and infrastructure.
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 evaluating locally: minutes to a working SDK install (pip install mostlyai) and a first trained generator on a small dataset. Analysts using the platform UI and AI Assistant: same-day once a data connection exists. Full enterprise deployment: days to weeks, since the installation path covers Kubernetes or OpenShift with Helm, compute configuration, ingress, TLS, and optionally an
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 manual column masking or static anonymized copies: move to generator-based synthesis so records are newly created rather than transformed, which supports multi-table integrity that masking cannot.
- →From hand-built test fixtures: replace static fixtures with Mock Data that maintains relational coherence across linked tables.
- →From a clean room or data-sharing enclave: generate a synthetic copy of the dataset so partners can analyze it without the enclave's access constraints, as Databricks describes for cross-industry sharing.
- ↗To a masking-only tool: if your requirement narrows to column-level obfuscation on flat tables, a masking product removes the Kubernetes footprint entirely.
- ↗To a clean room platform: if the requirement shifts from generating new records to restricting analysis of the original records inside a governed environment.
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
Tutorials & Learning
YouTube returned 6 videos for “Mostly AI”, and we withheld 6: 6 could not be judged, because “Mostly AI” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Mostly AI.
Official links
Tools that pair well with Mostly AI
Common stack mates teams adopt alongside Mostly AI, with the specific reason each pairing earns its keep.
SWE Smith
SWE-smith generates training data for software engineering agents from any GitHub repository.
Securiti
Securiti unifies DSPM, privacy automation, and AI security for enterprises on its DataAI Command Platform.
Toloka
Agent training data, RL environments, and safety red-teaming built by Toloka for LLM and agent teams
Featured Head-to-Head Comparisons
Attention Insight vs Mostly Ai
These tools serve completely different purposes. Choose Mostly AI if you need high-fidelity synthetic data for ML training or privacy-safe analytics; choose Attention Insight if you're a designer or marketer aiming to predict visual attention on designs. They are not competitors—your use case dictates the pick.
Amplitude vs Mostly Ai
Choose Mostly AI if your priority is generating privacy-safe, high-fidelity synthetic data for ML training and you have the infrastructure to deploy on Kubernetes. Choose Amplitude if you need a comprehensive product analytics platform with AI-driven insights, session replays, and experimentation features for understanding and optimizing user behavior.
Agent Vault vs Mostly Ai
Mostly AI and Agent Vault solve completely different problems. Pick Mostly AI if your priority is generating high-fidelity synthetic data for ML training or analytics under privacy constraints. Pick Agent Vault if you run AI coding agents and need a simple, self-hosted way to stop credential leaks from prompt injection. They complement each other rather than compete.
Agentic Soc Platform vs Mostly Ai
Mostly AI and Agentic SOC Platform serve completely different domains: synthetic data generation versus security operations. Unless your need is exactly synthetic data for analytics, choose Agentic SOC Platform—it's free, open-source, and offers powerful AI-driven investigation workflows. Mostly AI is enterprise-focused, contact-priced, and requires infrastructure investment, making it only suitable for dedicated data teams with privacy mandates.
Formula Bot vs Mostly Ai
Choose Mostly AI if you need high-fidelity synthetic data with differential privacy for ML training or testing, and you have the infrastructure (Kubernetes) to support it. Choose Formula Bot if you want a no-code, freemium tool for natural language querying, dashboards, and data transformations—especially for smaller datasets or quick business insights.
Mostly Ai vs Socialprofiler
Mostly AI and Socialprofiler serve completely different needs. Choose Mostly AI if you need to generate high-fidelity synthetic data with privacy guarantees for ML training and analytics, especially in enterprise environments with Databricks or Snowflake. Choose Socialprofiler for instant, AI-driven social media background checks on individuals for personal safety, HR vetting, or legal research. There is no overlap in use cases.
Mostly Ai vs Sust Global
Choose Mostly AI if you need to generate high-fidelity synthetic data for ML or testing with strong privacy guarantees and multi-table support. Choose Sust Global if you're an institutional investor or asset manager requiring geospatial climate risk analytics for large portfolios, especially after its ISS Stoxx acquisition. The tools serve completely different purposes—synthetic data vs. climate risk—so your decision hinges on your domain.
Airgap vs Mostly Ai
Pick Mostly AI if you need to generate synthetic versions of large datasets for ML or analytics, especially in cloud ecosystems like Databricks or AWS. Choose Airgap if your top priority is keeping confidential documents entirely on-device for chat and summarization—no cloud involvement. They solve different problems; decide based on whether your data is tabular and shareable (Mostly AI) or document-based and strictly private (Airgap).
Mostly Ai vs Versatile
Versatile and Mostly AI serve entirely different needs. If you're a steel erector needing real-time crane data with zero workflow changes, Versatile is your only choice. If you're a data team needing high-fidelity synthetic data for ML training with privacy guarantees, Mostly AI is the clear winner. They don't compete — pick by your domain.
Mostly Ai vs Sprig Feedback
Choose Mostly AI if you need to generate realistic, privacy-safe synthetic datasets for ML training and analytics, especially in regulated enterprises with existing data infrastructure. Choose Sprig Feedback if you want to continuously capture in-context user feedback via in-product surveys with AI-driven analysis and session replays. They solve fundamentally different problems, so your use case—data generation vs. user research—will dictate the choice.
Fundamental Ava vs Mostly Ai
If your priority is generating high-fidelity synthetic data at scale with privacy guarantees and deep cloud integrations (Databricks, AWS, Snowflake), choose Mostly AI. If you need an autonomous agent that can analyze complex spreadsheets, run parallel what-if simulations, and show every reasoning step, Fundamental-Ava is your tool. Both require contacting sales, so pick the one that matches your core task: data synthesis vs. spreadsheet intelligence.
Mostly Ai vs Pendo
Mostly AI and Pendo serve entirely different domains: one generates synthetic data for ML and privacy, the other analyzes user behavior and drives adoption. Choose Mostly AI if your team needs high-fidelity synthetic data for model training or testing with privacy guarantees and you have the infrastructure for Kubernetes. Choose Pendo if you're a product manager or IT leader looking to understand usage, improve onboarding, and measure AI agent adoption—its freemium model lets you start small.
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