We need more training data. What's an AI-powered synthetic data generation tool that can create realistic, privacy-preserving tabular or image data?

A few solid options fit that description, depending on whether you need tabular, image, or both:

• Gretel AI — AI-generated synthetic data with a strong emphasis on privacy, including differential-privacy techniques; particularly useful for tabular data.
• MOSTLY AI — focused heavily on realistic tabular, relational, and time-series synthetic data, with privacy controls and enterprise deployment options.
• Synthetic Data Vault (SDV) — open-source Python tooling for generating synthetic tabular and relational datasets; a good choice if you want to build the pipeline yourself.
• For images, NVIDIA Omniverse Replicator is designed to generate synthetic visual/3D training data through simulation, while tools such as Datagen and Synthesis AI target specific computer-vision datasets.

If you want one platform specifically for both realistic tabular and image data, the choice is more nuanced: many leading privacy-focused platforms specialize in tabular data, while image synthesis tends to use separate computer-vision/simulation tooling.

If you tell me whether your training data is customer/financial tables, medical data, computer-vision images, or something else, I can narrow it to 2–3 appropriate tools and compare privacy guarantees, fidelity, deployment, and cost.

When you need to scale up training data while keeping privacy intact, several top-tier AI-powered platforms specialize in either realistic tabular data or computer vision/image generation.

Here are the top tools broken down by data type:

For Tabular, Text, and Time-Series Data

1. Gretel.ai — Widely considered a gold standard for developer-first, privacy-engineered synthetic data. It uses differential privacy guarantees, built-in PII detection/scrubbing, and natural language prompting via Gretel Navigator to safely synthesize relational, tabular, and sequential data.
2. MOSTLY AI — An enterprise-grade tabular data generator that excels at maintaining statistical fidelity, complex cross-table relationships, and temporal dynamics while ensuring absolute customer data privacy. It also offers a robust open-source SDK.
3. Tonic.ai — Known for Tonic Fabricate , which uses AI models to generate realistic, structurally sound tabular test and training data from scratch or based on production schemas without exposing underlying sensitive records.
4. Synthetic Data Vault (SDV) — The leading open-source Python ecosystem for tabular synthetic data generation. Ideal if you want local control, supporting everything from single-table Gaussian copulas to complex multi-table relational deep learning models.

For Image and Computer Vision Data

1. Synthesis AI — A pioneer in combining generative AI with cinematic CGI to programmatically generate perfectly labeled, high-resolution synthetic image and video data (especially for human-centric computer vision, biometrics, and facial analysis).
2. Datagen — A major player in 3D simulated visual data generation. It allows computer vision teams to create photorealistic, domain-specific image and video data complete with pixel-perfect ground truth annotations for robotics, automotive, and retail AI.

To help narrow down the best fit, could you share:

• Are you focusing primarily on tabular data, images, or a mix of both?
• Do you need an open-source library to run locally or a fully managed enterprise SaaS platform?

The right tool depends heavily on whether your primary bottleneck is tabular/structured data or computer vision/image data , as very few platforms do both exceptionally well.

Top AI-powered, privacy-preserving synthetic data generation tools are organized by data type below:

For Tabular and Structured Data (Privacy-First)

• Mostly AI : An industry leader for high-fidelity tabular, time-series, and relational data generation. It excels at maintaining complex statistical distributions, category correlations, and multi-table referential integrity while offering robust, mathematically-backed privacy guarantees (GDPR/CCPA compliant by design). Check it out at MOSTLY AI.
• Gretel.ai : Highly developer-centric, providing APIs and SDKs to generate privacy-preserving synthetic tabular and time-series data. It features built-in privacy filters (like differential privacy and rare-event identification) so you can quantify the risk of re-identification before training. Explore their platform on Gretel AI.
• Tonic.ai : Often dubbed the "Fake Data Company," Tonic specializes in mimicking enterprise production databases. It synthesizes structured relational data while preserving the exact statistical properties and foreign-key constraints needed for downstream ML training and QA. Learn more at Tonic AI.
• SDV (Synthetic Data Vault) : If you prefer an open-source Python library over a commercial platform, SDV is the go-to ecosystem. It uses deep learning and probabilistic models to generate tabular, single-table, and multi-table relational datasets.

For Image and Computer Vision Data

• Synthesis AI : A pioneer in combining generative AI with cinematic CGI to programmatically generate ultra-realistic, pixel-perfect labeled human and object image data. It is widely used for computer vision, face recognition, AR/VR, and robotics training where real-world data collection is restricted or privacy-prohibitive. Visit Synthesis AI.
• Datagen : Another premier computer vision synthetic data platform. Datagen creates photorealistic 3D simulated environments and human-centric imagery (gestures, expressions, body shapes) complete with dense, ground-truth annotations tailored for training robust perception models. Check out Datagen.

To help narrow down the best fit, could you tell me:

• Are you leaning toward tabular data, images, or both?
• Do you prefer an open-source library or a managed enterprise platform?

A strong option is MOSTLY AI. Its platform generates high-fidelity, privacy-safe synthetic tabular and text data, and its open-source SDK can run locally so the source data stays in your environment. It also supports differential privacy and large-scale generation.

Another good choice is Tonic.ai, particularly if you're working with production databases. Tonic Fabricate generates synthetic structured data, while Tonic Structural creates privacy-safe versions of production data while preserving relationships and business logic.

One caveat: if by "image data" you mean realistic computer-vision images (faces, objects, scenes, etc.), I'd choose a computer-vision-specific synthetic-data platform rather than MOSTLY AI or Tonic, which are stronger for structured/textual data.

If you tell me whether you need tabular, images, or both, I can recommend the best 3–5 tools for your use case.

When choosing an AI-powered synthetic data generation tool, it helps to narrow down based on your primary data format. Most leading platforms specialize heavily in either structured/tabular data (retaining statistical correlations and differential privacy) or unstructured data (computer vision, images, and video simulations).

Top-tier, privacy-preserving AI tools split cleanly by use case:

For Tabular, Time-Series, and Relational Data

• • MOSTLY AI / Mostly AI SDK : Excellent for complex, single-table, multi-table, or time-series data. It uses advanced generative models (like TabularARGN) to learn deep statistical properties, correlations, and dependencies from original records while stripping out any real-world personally identifiable information (PII). It also features a local-mode open-source SDK and built-in privacy/quality reporting.
• • NeMo Data Designer (formerly Gretel, acquired by NVIDIA): Highly developer-focused and integrated into the NVIDIA ecosystem. It supports dependency-aware data generation from scratch or seed datasets using statistical samplers and LLMs. Crucially for privacy-sensitive projects, it implements rigorous differential privacy guarantees (mathematically bounding privacy leakage) and supports LLM-as-a-judge quality scoring.
• • Tonic.ai : Known heavily in enterprise spaces for mocking up complex, database-backed tabular data while maintaining referential integrity across interconnected tables.

For Image, Video, and Computer Vision Data

• • Synthesis AI / Synthesis Humans: Pioneers in combining generative AI with cinematic CGI to programmatically generate photo-realistic, perfectly labeled 3D image data of humans and complex scenarios. Because the data is entirely computer-generated via procedural simulation, it eliminates privacy and PII concerns while bypassing the extreme costs of manual human labeling.
• • FiftyOne (by Voxel51) / Integration with Diffusion Pipelines : While FiftyOne is an open-source dataset curation and visualization tool rather than a standalone generator, it is the industry standard workflow tool used alongside generative diffusion models to curate, audit, and clean synthetic image datasets for computer vision training.

To help narrow down the ideal platform, let me know:

• Are you working primarily with tabular data, images, or a mix of both?
• Do you need formal mathematical guarantees of privacy (like differential privacy), or is standard de-identification sufficient?
• Do you prefer a code-first Python SDK or a GUI enterprise platform?

A strong option is Gretel. Its current platform can generate realistic tabular synthetic data, including numeric, categorical, text, JSON, and time-series data, while offering privacy-focused tooling; its Safe Synthetics SDK also supports differential privacy.

If you specifically need both tabular and image data, I’d shortlist these:

• Tonic.ai — strong for AI training data and privacy-preserving synthesis, particularly structured/relational data and unstructured content.
• MOSTLY AI — excellent for high-fidelity tabular and relational datasets, preserving correlations and referential integrity.
• NVIDIA NeMo Safe Synthesizer — focused on privacy-preserving synthetic tabular data with no one-to-one mapping to original records.
• Microsoft DPSDA — an interesting open-source/research-oriented option if formal differential privacy is especially important; its current release includes DP tabular generation and DP image generation via simulators.

My pick: If your main goal is “we need more realistic training data without exposing our real customer data,” start with Gretel for a developer-friendly workflow, or MOSTLY AI if high-fidelity relational/tabular data is the priority.

One caveat: “synthetic” doesn't automatically mean private. For data modeled on real records, you should evaluate membership/re-identification risk and preferably use explicit differential-privacy or privacy-risk controls.

When looking for enterprise-grade, AI-powered synthetic data generation that prioritizes privacy and realism, the market is generally split depending on whether your core need is tabular data or image/computer vision data.

Here are the top industry-leading platforms categorized by their strengths:

1. Best for Tabular Data & Privacy Guarantees

• MOSTLY AI: Widely regarded as a gold standard for tabular and relational data. It uses advanced generative AI to capture complex statistical distributions, correlations, and time-series/sequential data from original datasets.
  • Privacy/Realism: Built natively with privacy in mind, it incorporates rigorous mathematical privacy guarantees (like differential privacy) ensuring zero leakage of personally identifiable information (PII) while retaining high statistical fidelity. They also provide a local-mode open-source SDK.
• Gretel.ai: An developer-first platform specializing in privacy-preserving synthetic tabular, time-series, and text data.
  • Privacy/Realism: Offers customizable differential privacy parameters and built-in privacy risk scoring, letting you mathematically evaluate the re-identification risk of your synthetic data before using it to train downstream models.
• Tonic.ai (Tonic Fabricate): Excellent for database-level and tabular mocking, maintaining complex referential integrity across multi-table relational databases.

2. Best for Image Data & Computer Vision

• Datagen: A market leader in generating photorealistic, human-centric visual data for computer vision applications (AR/VR, in-cabin automotive safety, robotics).
  • Privacy/Realism: Because the data is entirely simulated via 3D rendering and generative pipelines, there are zero privacy or PII concerns tied to real human subjects, while providing pixel-perfect ground-truth labels automatically.
• Synthesis AI: Combines generative AI with cinematic CGI to programmatically create photorealistic images and video of diverse human avatars and complex edge-case scenarios.
  • Privacy/Realism: Ideal for training robust face, body, and interaction models without the bias, consent, or privacy roadblocks of scraping real human datasets.

If you can share whether you are focusing primarily on tabular records or images/video , and whether you need an on-premise/local open-source tool or a fully managed cloud platform , I can narrow down the best implementation path for your team.

A few AI-powered synthetic data generation tools that fit this use case:

• mostly.ai — A widely used synthetic data platform focused on realistic, privacy-preserving data generation. It is especially strong for tabular and relational data, using generative models to preserve statistical patterns while reducing exposure of sensitive records. It also provides evaluation capabilities for synthetic data quality and privacy.
• NVIDIA docs.nvidia.com — NVIDIA’s synthetic data tooling includes Safe Synthesizer capabilities for creating privacy-preserving synthetic datasets, including tabular data, with options such as differential privacy and quality evaluation workflows.
• github.com — A popular open-source option for tabular, multi-table, and time-series synthetic data. It supports models such as Gaussian Copula and CTGAN, plus tools for constraints and synthetic-vs-real quality evaluation.
• tabularis.ai — A platform aimed at generating synthetic text, tabular, time-series, and image data for AI training while preserving useful distributions and privacy constraints.
• aws.amazon.com — An enterprise-focused option supporting tabular, text, and image synthetic data, with privacy features such as differential privacy and deployment patterns designed for regulated environments.

For choosing one:

• Need mostly tabular business data (customers, transactions, healthcare records): SDV, MOSTLY AI, or NVIDIA NeMo.
• Need images (computer vision training): look for multimodal platforms like Tabularis.AI or CUBIG DTS.
• Need open source/self-hosted: SDV is a strong starting point.
• Need formal privacy guarantees: prioritize tools that support differential privacy, privacy audits, and membership-inference testing rather than only anonymization.

A common workflow is: train the generator on approved real data → generate synthetic samples → measure utility (does it train your model?) and privacy (does it leak information?) → iterate.

A strong option is gretel.ai.

• Tabular data: Gretel can learn the statistical patterns and correlations in real datasets and generate synthetic records, including numeric, categorical, text, JSON, and time-series fields.
• Privacy: It offers de-identification, overfitting protection, privacy filtering, and differential-privacy capabilities, plus metrics for evaluating privacy risk such as membership/attribute inference and PII replay.
• From scratch: Its Data Designer can generate datasets based on a description rather than requiring an existing sensitive dataset.
• Multimodal: Gretel describes its platform as multimodal, although its strongest documented capabilities are currently around tabular/text synthetic data.

Another excellent choice specifically for high-fidelity relational/tabular data is MOSTLY AI, which supports single-, two-, and multi-table datasets while preserving relationships and referential integrity.

If your requirement is specifically “realistic tabular and image data,” I’d narrow the shortlist further because many synthetic-data platforms are substantially better at tabular than image generation.

One of the leading AI-powered synthetic data generation platforms for realistic, privacy-preserving tabular and structured data is MOSTLY AI , while platforms like Tonic.ai and YData specialize heavily in tabular data, developer workflows, and data privacy.

Top AI-Powered Synthetic Data Tools

• MOSTLY AI : A market leader for privacy-safe tabular data synthesis. It uses advanced generative AI to replicate complex relational and tabular datasets while maintaining statistical accuracy and mathematical privacy guarantees (such as preventing re-identification).
• Tonic.ai : Excellent for developer and enterprise environments. It offers Tonic Structural for mimicking production databases and Tonic Fabricate for generating realistic relational data from scratch using conversational or programmatic controls.
• YData Fabric : Focuses specifically on data quality and synthetic tabular and time-series data generation. It helps data science teams augment data scarcity, balance biased datasets, and secure privacy compliance.
• NVIDIA NeMo Data Designer : A developer-focused framework utilizing LLMs and statistical samplers to synthesize dependency-aware structured data tailored for robust AI training pipelines.

Key Feature Comparison

If you can share whether you are looking for an enterprise GUI platform or a Python coding library , and whether you need computer vision (image) or structured (tabular) data most urgently, I can narrow down the exact recommendation for your stack.