Best Synthetic Data Tools in 2026 Compared

The best synthetic data tools in 2026 are Gretel for developer-friendly generative pipelines, MOSTLY AI for privacy-safe tabular data at enterprise scale, Tonic.ai for realistic test data, and SDV if you want a free open-source library. Hazy, K2view and YData cover regulated-industry and data-fabric use cases, while NVIDIA Omniverse Replicator stands alone for synthetic images and computer vision. Below we compare all eight by data type, privacy guarantees, licensing model and pricing model.

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Synthetic data tools compared (2026)

Here is the short version. Pricing models can change — vendors update plans often, so verify current pricing on the official sites before committing.

What synthetic data tools actually do

Synthetic data tools train a generative model — GANs, variational autoencoders, transformers or agent-based simulators — on a real dataset, then generate artificial records that preserve the statistical patterns of the original without containing any real individual's information. Teams use them for three main jobs:

• Privacy-safe analytics and sharing: give data scientists, partners or offshore teams a dataset that behaves like production data but is not personal data.
• Test data for software development: realistic, referentially intact databases for staging and CI environments, without copying production.
• Training data for machine learning: augmenting scarce classes, balancing datasets, or generating labeled images for computer vision when real capture is expensive or dangerous.

Synthetic data complements — rather than replaces — the curated corpora we cover in our Data for AI hub. You still need good real data to train the generator.

1. Gretel — best for developers and ML teams

Gretel (acquired by NVIDIA in 2025) is the most developer-centric platform on this list. You interact with it through APIs, SDKs and a console: point it at tabular data, free text or time series, pick a model (LSTM, ACTGAN, or its Navigator LLM-based generator), and get synthetic output plus a quality-and-privacy report scoring statistical fidelity and re-identification risk.

Strengths: excellent docs, differential privacy options, text generation, and a genuinely usable free tier for prototyping. Weak points: usage-based pricing gets hard to predict at scale, and the NVIDIA integration means the roadmap is tilting toward LLM training data.

Pricing: free developer tier with monthly credits, then usage-based pricing by credit, plus team plans. Verify at gretel.ai.

2. MOSTLY AI — best for privacy-safe tabular data

Vienna-based MOSTLY AI focuses on one thing and does it extremely well: high-fidelity synthetic versions of tabular and multi-table relational data, with privacy mechanisms designed to satisfy European regulators. Its generators retain correlations across linked tables (customers → accounts → transactions), which is where most competitors degrade.

Strengths: best-in-class relational fidelity, strong GDPR positioning, automatic privacy checks (no exact matches, rare-category protection), and an open-source Synthetic Data SDK in Python. Weak points: tabular only — no images, no free-text generation beyond simple columns.

Pricing: free tier for limited daily rows; enterprise plans are quote-based. Verify at mostly.ai.

3. Tonic.ai — best for realistic test data

Tonic.ai approaches synthetic data from the software-engineering side. Tonic Structural connects to your production databases (PostgreSQL, MySQL, SQL Server, Mongo, Snowflake…), de-identifies or synthesizes columns, and keeps referential integrity intact so your staging environment actually works. Tonic Textual does the same for unstructured text, detecting and replacing PII in documents.

Strengths: unmatched database coverage, subsetting (shrink a 2 TB database to a coherent 20 GB slice), and developer workflow integration. Weak points: it is test-data tooling first; if your goal is ML training data with formal privacy guarantees, Gretel or MOSTLY AI fit better.

Pricing: Tonic Structural and Textual are sold on quote-based enterprise plans; the newer Tonic Fabricate product also offers free and paid self-serve tiers. Free trial available. Verify at tonic.ai.

4. SDV — best open-source option

The Synthetic Data Vault (SDV), created at MIT and maintained by DataCebo, is the de facto standard open-source ecosystem for synthetic data in Python. It bundles single-table models (GaussianCopula, CTGAN, TVAE), multi-table synthesis (HMA), sequential models for time series, and SDMetrics for evaluating quality — all installable with pip install sdv.

Strengths: free, transparent, scriptable, huge community, and you keep everything on your own infrastructure. Weak points: you own the whole pipeline — tuning, privacy validation and scaling are on you, and deep-learning models like CTGAN can be slow on wide tables. Note the license has moved to a source-available model for some components, so check terms for commercial embedding.

Pricing: free; DataCebo sells enterprise add-ons and support. Verify at sdv.dev.

5. Hazy — best for banking and regulated industries

Hazy, a UK spin-out acquired by SAS in late 2024, built its reputation generating synthetic financial data for banks (Nationwide, Wells Fargo pilots). It emphasizes differential privacy budgets, sequential/transactional data, and deployment inside the customer's own perimeter — the generator trains where the real data lives, and only synthetic data leaves.

Strengths: strong formal privacy story, financial-services pedigree, on-premises deployment, and now SAS's enterprise muscle behind it. Weak points: no self-serve option; procurement-grade sales cycles and pricing to match.

Pricing: quote-based enterprise licensing, sold through SAS's procurement process. Verify with SAS.

6. K2view — best inside a data-fabric stack

K2view sells synthetic data generation as part of a broader entity-based data management platform. Its differentiator is the "business entity" approach: it assembles all data about a customer/device/order across systems into a Micro-Database, then masks, subsets or synthesizes at the entity level — which keeps cross-system consistency that column-level tools miss.

Strengths: consistency across dozens of source systems, combined rule-based + generative pipelines, good for test-data management at telco/bank scale. Weak points: it only makes sense if you buy into the wider platform; heavy for a team that just needs one synthetic dataset.

Pricing: enterprise custom quotes. Verify at k2view.com.

7. YData — best SDK-first hybrid

Portugal-based YData pairs an open-source profiling library (ydata-profiling, formerly pandas-profiling, with millions of downloads) with a synthetic-data SDK and the YData Fabric platform. The workflow is data-scientist-native: profile your dataset, spot quality issues, then generate synthetic tabular or time-series data to fix imbalance or enable sharing.

Strengths: excellent data-quality tooling around the generator, smooth path from free SDK to managed platform, good time-series support. Weak points: smaller vendor than the leaders; enterprise governance features are younger.

Pricing: open-source SDK free (Community plan); Fabric platform on pay-as-you-go, with a custom Enterprise tier. Verify at ydata.ai.

8. NVIDIA Omniverse Replicator — best for synthetic images and vision

Everything above generates rows and text. Omniverse Replicator generates pixels: physically accurate, ray-traced 3D scenes rendered into unlimited labeled images — bounding boxes, segmentation masks, depth maps included — for training computer-vision models. Randomize lighting, textures, poses and camera angles, and you get training sets for scenarios too rare or dangerous to capture (factory defects, autonomous-driving edge cases, warehouse robotics).

Strengths: perfect ground-truth labels for free, domain randomization at scale, integration with Isaac Sim for robotics. Weak points: you need 3D assets and RTX GPUs, and the learning curve is real — this is a simulation SDK, not a button.

Pricing: the SDK is free within the Omniverse ecosystem; production support comes via NVIDIA AI Enterprise licensing. Verify at developer.nvidia.com.

Tabular, image or text: match the tool to the data type

Tabular and relational data is the mature segment: MOSTLY AI, Gretel, SDV, Hazy, K2view and YData all compete here, and quality is measured on statistical fidelity plus downstream ML utility (train-on-synthetic, test-on-real accuracy).

Images and 3D vision data is a different discipline entirely — simulation and rendering rather than learned generative models over records. Omniverse Replicator dominates; alternatives are custom pipelines over Unity/Unreal.

Text splits in two: de-identifying real documents (Tonic Textual, Gretel Transform) versus generating new synthetic text with LLMs (Gretel Navigator). If you are building retrieval systems on top of that text, pair your generator with one of the options from our best vector databases comparison — synthetic corpora are a safe way to load-test embeddings pipelines before real data arrives.

Privacy, GDPR and differential privacy

The core legal question: properly generated synthetic data is generally treated as anonymous data, which falls outside the GDPR — Recital 26 excludes data that no longer relates to an identifiable person. But two caveats matter:

• Generation itself is processing. Training the generator on personal data is a processing activity that needs a lawful basis, and regulators (including the EDPS and national DPAs) expect a residual re-identification risk assessment — synthetic data is not automatically anonymous just because a vendor says so.
• Overfitted generators leak. A model that memorizes rare records can reproduce them. This is why serious vendors ship protections: differential privacy (Gretel, Hazy) adds calibrated noise during training with a measurable privacy budget (ε), while MOSTLY AI enforces no-exact-match and rare-category suppression checks on output.

Practical rule: for regulated data (health, finance), prefer tools with differential privacy or documented privacy reports, keep the generator inside your perimeter, and document your risk assessment.

Open source vs commercial: how to decide

Choose open source (SDV, YData SDK, MOSTLY AI's SDK) when you have data-science capacity in-house, data cannot leave your infrastructure, and you can own quality/privacy validation yourself. Total cost is engineering time, not licenses.

Choose commercial (Gretel, MOSTLY AI platform, Tonic, Hazy, K2view) when you need connectors to production databases, audit-ready privacy reports, support SLAs, or you are provisioning test data for many teams. The license fee usually beats the cost of maintaining a bespoke pipeline once more than one or two teams depend on it.

A common pattern in 2026: prototype with SDV, then graduate to a commercial platform when governance requirements arrive. Whichever route you take, benchmark synthetic output against real holdout data — and against public reference sets like the ones in our Data for AI archive — before trusting it for model training.

FAQ: synthetic data tools

What is synthetic data?

Synthetic data is artificially generated data that mimics the statistical properties of a real dataset without containing any actual records from it. It is produced by generative models (GANs, VAEs, transformers) trained on real data, or by simulation engines that render labeled scenes for computer vision.

Is synthetic data legal under GDPR?

Generally yes: if the synthetic output no longer relates to identifiable individuals, it is anonymous data and outside the GDPR's scope. However, generating it from personal data is itself regulated processing, and you must verify residual re-identification risk — tools with differential privacy or automated privacy checks make that demonstrably easier.

Can synthetic data fully replace real data?

No. Synthetic data inherits the biases and gaps of the data the generator was trained on, and quality degrades for rare events the model never saw. It excels at augmentation, privacy-safe sharing and test environments, but production models should still be validated — and usually fine-tuned — on real data.

Which synthetic data tool is best for free?

SDV is the strongest fully free option for tabular and relational data, YData's SDK is a close alternative with better profiling, and NVIDIA Omniverse Replicator is free for synthetic images if you have RTX hardware. Gretel and MOSTLY AI both offer usable free tiers if you prefer a managed service.