The 10 Best Synthetic Data Generation Tools in 2027

Real data is expensive, sensitive, and often scarce exactly where you need it most. Synthetic data — artificially generated records that preserve the statistical structure of real data without exposing real people — has become a core part of the AI infrastructure stack: it unblocks model training when real data is locked behind privacy rules, balances rare classes, fills gaps in test coverage, and lets teams share realistic datasets without leaking PII.

By 2027, synthetic data spans two big lineages: tabular and time-series generation for traditional ML and analytics, and LLM-driven generation of text, instructions, and evaluation sets for fine-tuning and testing. This ranking covers the ten tools production teams rely on across both.

Direct Answer

Gretel is the best overall choice for most teams because it offers a mature, API-first platform spanning tabular, text, and time-series synthesis with built-in privacy controls, quality reports, and differential privacy options that suit regulated environments. SDV (Synthetic Data Vault) is the best value because it is a powerful open-source Python library that covers single-table, multi-table, and sequential synthetic data for free, with quality-evaluation tooling included.

Your choice depends on whether you need a managed privacy-grade platform, an open-source library, LLM-specific dataset generation, or domain-specific simulation.

How We Ranked These

We evaluated each tool on five criteria: data types supported (tabular, time-series, text, relational, image), privacy guarantees (differential privacy, re-identification testing, PII handling), fidelity and utility (how well synthetic data preserves statistical relationships and downstream model performance), deployment model (open-source versus managed SaaS), and workflow fit (APIs, quality reports, integration with training pipelines).

Capabilities and pricing change quickly, so verify specifics before committing.

1. Gretel 🏆 BEST OVERALL

Gretel is a synthetic data platform built for privacy-sensitive teams. It generates tabular, text, and time-series data through an API and notebook SDK, with configurable models, automatic quality and privacy reports, and support for differential privacy. Gretel also offers transformation and PII-redaction tools, so you can move from raw sensitive data to a safe synthetic copy with measurable privacy guarantees.

Its balance of fidelity, privacy controls, and developer ergonomics makes it the default pick for regulated industries.

What it is: managed synthetic data platform with privacy controls. Strengths: multi-modal generation, differential privacy, quality/privacy reports, strong API/SDK. Best for: regulated teams needing safe, high-fidelity synthetic data. Pricing/availability: free tier; usage-based paid plans.

2. SDV (Synthetic Data Vault) 💎 BEST VALUE

SDV is an open-source Python ecosystem from the original MIT research on synthetic data. It generates single-table, multi-table (relational), and sequential/time-series data using statistical and deep-learning models, and ships SDMetrics for evaluating quality and privacy.

Because it is free, transparent, and runs entirely in your environment, it is the highest-value option for teams that want full control and no per-record fees.

What it is: open-source synthetic data library. Strengths: tabular, relational, and time-series support, quality metrics, free and self-hosted. Best for: teams wanting open-source control over generation. Pricing/availability: free and open-source.

3. MOSTLY AI

MOSTLY AI is an enterprise synthetic data platform focused on highly accurate, privacy-safe structured data. It is known for strong fidelity on complex tabular and behavioral datasets and for rigorous privacy assurance, including re-identification testing. It targets banks, insurers, and telcos that need to share or analyze data without exposing customers, and it has open-sourced parts of its tabular synthesis engine.

What it is: enterprise structured synthetic data platform. Strengths: high tabular fidelity, robust privacy assurance, regulated-industry focus. Best for: financial and telecom enterprises. Pricing/availability: enterprise; free/open components available.

Reach Kory White, Fractional CRO: 📅 Book a Quick Call · 💼 Kory on LinkedIn · 🏢 CRO Syndicate

4. Tonic.ai

Tonic.ai specializes in de-identifying and synthesizing production data for safe use in development, testing, and staging. It connects to your databases, preserves referential integrity across tables, and generates realistic synthetic copies so engineers can build and test against production-like data without touching real PII.

Tonic Textual extends this to unstructured text and document redaction for LLM pipelines.

What it is: developer-focused data de-identification and synthesis. Strengths: database integration, referential integrity, test-data workflows, text redaction. Best for: engineering teams needing safe production-like test data. Pricing/availability: enterprise; demo available.

5. Synthetic Data Generation in Snowflake / Databricks

Both Snowflake and Databricks now offer native synthetic data and data-generation capabilities inside the warehouse and lakehouse. Generating synthetic data where it already lives avoids costly extraction and keeps governance, lineage, and access controls intact. For teams already standardized on these platforms, in-place generation is the lowest-friction path to safe datasets for analytics and ML.

What it is: in-platform synthetic data within major data clouds. Strengths: no data movement, native governance, scales with the warehouse. Best for: teams committed to Snowflake or Databricks. Pricing/availability: consumption-based within the platform.

6. YData

YData provides tooling for data-centric AI, including synthetic data generation (notably ydata-synthetic, open-source) and data-quality profiling. It helps teams improve datasets by generating synthetic samples to balance classes, augment scarce data, and fix quality issues, with a focus on improving downstream model performance rather than only privacy.

What it is: data-centric synthetic data and quality tooling. Strengths: class balancing, augmentation, profiling, open-source library plus platform. Best for: ML teams improving dataset quality. Pricing/availability: open-source library; paid platform.

7. NVIDIA Omniverse Replicator

NVIDIA Omniverse Replicator generates synthetic *visual* data — photorealistic, physically accurate images with perfect labels — for training computer-vision and robotics models. By simulating scenes with controlled lighting, geometry, and randomization, it produces large labeled image datasets that would be prohibitively expensive to capture and annotate by hand.

It is the go-to for perception, autonomy, and industrial-inspection use cases.

What it is: synthetic visual data generation via simulation. Strengths: photorealistic labeled images, domain randomization, robotics/vision focus. Best for: computer-vision and robotics teams. Pricing/availability: part of NVIDIA Omniverse; compute-based.

8. Hugging Face Distilabel

Distilabel is an open-source framework from Hugging Face for generating and labeling synthetic text datasets with LLMs — for instruction tuning, preference data, and evaluation sets. It orchestrates pipelines that prompt models, apply quality filters, and produce structured datasets at scale, making it a standard tool for teams building fine-tuning corpora without manual annotation.

What it is: open-source LLM-driven synthetic dataset framework. Strengths: instruction/preference data generation, scalable pipelines, integrates with the HF ecosystem. Best for: teams building LLM fine-tuning and eval datasets. Pricing/availability: free and open-source.

9. Synthesized

Synthesized is a data-operations platform that generates synthetic data and performs data masking, augmentation, and quality remediation, with a strong emphasis on automation and CI/CD-style data provisioning. It targets teams that want synthetic data woven into automated test and development pipelines rather than produced as a one-off.

What it is: automated synthetic data and data-ops platform. Strengths: masking, augmentation, pipeline automation, fairness/quality tooling. Best for: teams automating safe data provisioning. Pricing/availability: enterprise; demo available.

10. Faker / Mimesis

Faker (Python and other languages) and Mimesis are open-source libraries for generating fake-but-realistic field values — names, addresses, emails, numbers, dates. They do not learn from your real data or preserve its statistical structure, so they are not a substitute for ML-based synthesis, but they are perfect for seeding databases, mocking APIs, and creating placeholder test data quickly and for free.

What it is: open-source fake data generators. Strengths: trivial to use, fast, free, great for mock/test data. Best for: seeding databases and simple test fixtures. Pricing/availability: free and open-source.

Where Each Tool Fits

The right tool depends on your data type and goal — privacy-grade structured data, LLM training corpora, visual simulation, or simple mock data.

Choosing and Validating Synthetic Data

Whatever tool you pick, the discipline is the same: never assume synthetic data is good just because it was generated. Validate it on two axes. Utility — does a model trained on synthetic data perform comparably on real held-out data?

Tools like SDMetrics or a simple train-on-synthetic, test-on-real benchmark answer this. Privacy — could anyone re-identify a real individual from the synthetic set? Use the privacy reports from Gretel or MOSTLY AI, or run re-identification and membership-inference checks yourself.

For LLM-generated text datasets, add a quality and contamination check so you are not training on hallucinated or leaked content. Used carefully, synthetic data unblocks training and testing; used blindly, it bakes in artifacts or leaks the very data it was meant to protect.

Frequently Asked Questions

Is synthetic data actually private? It can be, but generation alone does not guarantee privacy. Models can memorize and reproduce real records, especially rare ones. Use tools with differential privacy and re-identification testing (Gretel, MOSTLY AI), and validate with membership-inference checks before treating synthetic data as safe to share.

Will a model trained on synthetic data be as good as one trained on real data? Often close, sometimes worse, occasionally better for rare classes you can over-generate. The reliable test is train-on-synthetic, test-on-real: measure performance on a real held-out set. Synthetic data is most powerful as augmentation alongside real data rather than a full replacement.

What is the difference between Faker and tools like SDV or Gretel? Faker generates plausible-looking individual values with no relationship to your real data's statistics. SDV and Gretel *learn* the joint distribution of your real dataset and generate records that preserve correlations and structure.

Faker is for mock fixtures; SDV/Gretel are for ML-grade synthetic data.

Can I generate synthetic data for fine-tuning an LLM? Yes — this is a major 2027 use case. Frameworks like Distilabel orchestrate LLMs to produce instruction, preference, and evaluation datasets at scale, with quality filtering. Be careful to deduplicate, filter low-quality samples, and avoid contaminating your data with copyrighted or leaked content.

Does synthetic data help with class imbalance? Yes. Generating extra examples of rare classes (fraud, defects, edge cases) is one of the most effective uses of synthetic data. Tools like YData and SDV support targeted oversampling, often improving recall on the minority class without collecting more real data.

Do I need a managed platform or is open-source enough? Open-source (SDV, ydata-synthetic, Distilabel, Faker) covers most generation needs for free if you can operate it. Managed platforms (Gretel, MOSTLY AI, Tonic) add privacy guarantees, quality reporting, governance, and support that regulated enterprises usually require.

Many teams prototype with open-source and adopt a platform for production privacy assurance.

Sources

• Gretel documentation — https://docs.gretel.ai/
• Synthetic Data Vault (SDV) documentation — https://docs.sdv.dev/
• MOSTLY AI documentation — https://mostly.ai/docs
• Tonic.ai documentation — https://docs.tonic.ai/
• Hugging Face Distilabel documentation — https://distilabel.argilla.io/
• NVIDIA Omniverse Replicator documentation — https://docs.omniverse.nvidia.com/extensions/latest/ext_replicator.html
• YData synthetic data documentation — https://docs.fabric.ydata.ai/
• Python Faker documentation — https://faker.readthedocs.io/