9 Synthetic Data Platforms for AI Training in 2026

TL;DR: Synthetic-data platforms now fall into three practical groups: privacy-preserving tabular data, simulated images and sensor scenes for computer vision, and generated text for LLM training. Choose by modality, privacy guarantees, deployment model, quality reports, integrations, and access to a free local option.

2026 update: The market has consolidated and specialized. NVIDIA acquired Gretel in 2025; Syntho acquired the MOSTLY AI brand assets in June 2026; SAS acquired Hazy’s principal software assets and integrated the technology into SAS Data Maker; and Synthesis AI and Globant announced a strategic partnership. Datagen has closed and is no longer counted. The active list below therefore contains nine companies and platforms. Synthetic data usually supplements—rather than eliminates—carefully governed real data.

Synthetic data is generated to mimic real-world data. It’s created using algorithms, simulations, or predefined rules. Today, it is primarily used in:

Below are nine active companies and platforms you can use to generate synthetic data:

Gretel, acquired by NVIDIA in 2025, generates tabular, text, and time-series data and connects synthetic-data jobs to databases, warehouses, and cloud storage. It suits enterprise ML, analytics, privacy, and LLM-data teams that need pipelines plus quality and privacy reports. Free access: public SDKs, documentation, blueprints, and sample datasets; production platform access depends on NVIDIA’s current offering.

MOSTLY AI, powered by Syntho, focuses on high-fidelity, privacy-aware single-table, multi-table, language, and time-series generation while preserving relationships between tables. Syntho acquired the MOSTLY AI brand assets in June 2026, and the brand now continues within the Syntho ecosystem. It remains a strong fit for regulated enterprises and teams that need conditional sampling, imputation, fairness controls, and local deployment. Free access: the Apache-2.0 Synthetic Data SDK runs locally, and the hosted platform has a limited free tier.

Tonic.ai now spans Tonic Structural for structured test-data management, Tonic Textual for unstructured and LLM data, and Tonic Fabricate for generating data from scratch. It is designed for software, data engineering, and AI teams that need realistic development environments, de-identification, subsetting, and generated text or documents. Free access: self-service and evaluation options vary by product; enterprise deployments are sales-led.

SAS Data Maker is now the relevant active product: SAS acquired Hazy’s principal software assets in November 2024 and integrated Hazy technology into Data Maker. The platform focuses on low-code/no-code generation and augmentation of privacy-sensitive enterprise data, with quality evaluation and privacy-enhancing capabilities for regulated use cases. Hazy should no longer be treated as an independent vendor. Free access: access is sales-led; no permanent public free tier is confirmed.

Betterdata provides privacy-preserving synthetic tabular and relational data for enterprise AI/ML, analytics, system testing, and secure data sharing. Its platform supports differential privacy and cloud, on-premises, and air-gapped deployment, making it most relevant to organizations working with regulated or sensitive records. Free access: access is by request; no permanent public free tier is confirmed.

DataCebo develops the Synthetic Data Vault ecosystem for single-table, relational, time-series, geospatial, and semi-structured data. It is especially useful when developers need a downloadable SDK, detailed evaluation metrics, business-rule constraints, and on-premise operation. Free access: SDV Community is freely available; SDV Enterprise is paid.

In September 2025, Synthesis AI and Globant announced a strategic partnership to bring Synthesis AI’s technology and capabilities into Globant’s Digital Twins Studio. The offering remains relevant for simulated human and visual data used in biometrics, driver monitoring, pedestrian detection, AR/VR, security, and virtual try-on, but buyers should treat it as a Globant-linked offering rather than an unqualified standalone-vendor listing. Free access: public datasets and resources may be available; commercial access is arranged with the providers.

Parallel Domain builds deterministic simulation and synthetic sensor data for autonomous vehicles, drones, and robotics. Its differentiator is reproducible camera, lidar, and radar scenarios that connect to autonomy stacks and CI pipelines for training and validation. Free access: enterprise access is demo-led; no permanent public free tier is confirmed.

Rendered AI provides cloud tooling for domain-specific synthetic computer-vision datasets, especially where real imagery is scarce, expensive, dangerous, or difficult to label. Its platform supports custom simulation channels, repeatable dataset-generation workflows, and multiple sensor types for training and testing vision systems. Free access: the company offers a trial; confirm current limits with the vendor.

This category increasingly overlaps with the tabular platforms above. NVIDIA/Gretel can generate instruction, safety, and domain-specific records; Tonic Textual synthesizes and transforms unstructured documents; and MOSTLY AI, now powered by Syntho, supports language-data generation. For LLM work, compare provenance controls, deduplication, filtering, licensing, evaluator quality, and whether human review is built into the pipeline—not just raw generation volume.

Synthetic data is artificially generated information designed to reproduce useful patterns, relationships, formats, or edge cases found in real data without copying individual records. It can be created with statistical models, generative AI, simulations, rules, or combinations of these methods.

OpenAI does not publish the complete composition of every ChatGPT training dataset. Model-generated data are used in parts of modern AI post-training, evaluation, and safety work, but it would be inaccurate to say that ChatGPT is trained only, or primarily, on synthetic data without model-specific disclosure.

Financial services, healthcare, insurance, retail, telecommunications, automotive, robotics, defense, manufacturing, and software companies use synthetic data for privacy-safe analytics, model training, rare-event simulation, test-data management, and computer-vision development.

Typical users include data science teams that lack enough labeled examples, software engineers who need realistic test databases, privacy teams sharing sensitive datasets, and perception teams training autonomous systems. Adoption ranges from startups to banks, insurers, manufacturers, and large technology companies.

It can reduce privacy and collection risks, but it is not automatically safe or unbiased. Teams should test privacy leakage, fidelity, downstream utility, class coverage, provenance, licensing, and the synthetic-to-real gap before relying on it in production.

To further explore the topic, use our article: What is synthetic data and how to generate it

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