Best Synthetic Data Tools 2026
Privacy-preserving artificial datasets for AI training, software testing, and analytics. Six platforms compared with verified June 2026 pricing.
MOSTLY AI is the strongest self-service pick for ML teams needing high-fidelity tabular and time-series data with a transparent credit-based pricing model. Tonic.ai is the most developer-friendly option, covering structured de-identification, AI-native generation (Fabricate), and unstructured text in a single platform. Syntho is the best choice for privacy-first enterprises in regulated industries that want predictable feature-based licensing with no consumption charges.
Synthetic data has moved from a niche research technique to a core part of the AI development pipeline. Privacy regulations (GDPR, HIPAA, CCPA), data scarcity in specialized domains, and the explosion of LLM fine-tuning workloads have all converged to make purpose-built synthetic data tooling essential for ML and data engineering teams in 2026.
The market saw two landmark acquisitions in the past year: NVIDIA bought Gretel in March 2025 for a reported $320 million and folded it into its NeMo ecosystem for agentic AI, while KPMG acquired YData's IP and technology assets in October 2025 to build a Synthetic Data Center of Excellence. Both moves signal how strategic the category has become, but they also changed the commercial landscape for buyers: Gretel is no longer available as a standalone SaaS product, and YData is now primarily an internal KPMG capability.
For teams shopping for a standalone synthetic data platform today, the clearest options are MOSTLY AI, Tonic.ai, Syntho, and Synthesized. Each takes a different angle: MOSTLY AI leads on statistical fidelity for tabular and time-series data; Tonic.ai bundles de-identification, generation, and unstructured text under one roof; Syntho emphasizes feature-based enterprise licensing with no consumption surprises; and Synthesized targets complex enterprise systems like SAP and Oracle. This guide covers all six names with verified pricing as of June 2026.
Top Picks
Based on features, user feedback, and value for money.
| Tool | Starting price | Rating | Best for |
|---|---|---|---|
| MOSTLY AI | Free plan | 4.5(17) | ML and data teams, self-service |
| Tonic.ai | Free plan | 4.2(107) | Software dev and AI engineering teams |
| Syntho | Custom | 4.6(16) | Enterprise compliance and privacy teams |
| Gretel (NVIDIA NeMo) | Custom | n/a | Teams already in the NVIDIA ecosystem |
| YData | Custom | 4.7(13) | Data scientists, KPMG advisory clients |
| Synthesized | Custom | n/a | Enterprises on SAP, Oracle, or Workday |
ML and data teams, self-service
Value 45/100. The pricing for Mostly AI is quite expensive, with the Marketplace tier starting at $3,000 per month, which is a significant investment for synthetic data.
Watch out: SSO* implies potential extra cost for SSO.
Software dev and AI engineering teams
Value 70/100. The 'Pay-As-You-Go' tier at $19/table/month (after 20 free tables) offers a fair entry point for smaller use cases, especially with unlimited processing for included tables.
Watch out: Overage fees for tables beyond 20 on Pay-As-You-Go
Enterprise compliance and privacy teams
Value 50/100. Syntho's pricing model, based on 'Contact us' for all tiers, makes it impossible to assess fairness or value directly.
Watch out: Custom pricing means potential high costs
Teams already in the NVIDIA ecosystem
Data scientists, KPMG advisory clients
Enterprises on SAP, Oracle, or Workday
Other Data & Databases worth considering
Beyond the editorial top picks, these are also strong choices we evaluated.
What It Is
Synthetic data tools generate artificial datasets that preserve the statistical properties, relationships, and distributions of real data without exposing any actual records. They use generative models (GANs, VAEs, diffusion models, LLM-based agents) to produce tabular records, time-series sequences, or unstructured text that behaves like production data for AI training, software testing, load testing, and analytics. The output passes standard validation checks and privacy audits, making it safe to share across teams, with vendors, or in public research.
Why It Matters
In 2026, three forces are driving adoption. First, privacy regulations now make it genuinely risky to use production data in development and test environments; synthetic data eliminates that exposure entirely. Second, AI training pipelines constantly hit data scarcity: edge cases, rare events, minority classes, and future scenarios simply do not have enough real examples. Synthetic generation fills those gaps without months of manual labeling. Third, the rise of agentic AI means teams need to validate AI agents against production-realistic scenarios before deployment, and real production data cannot be used in pre-production environments at scale. Synthetic data solves all three problems simultaneously.
Key Features to Look For
Tabular data generation with preserved referential integrity across related tables
Time-series synthesis that captures temporal dependencies and seasonality patterns
Privacy risk scoring (differential privacy guarantees, re-identification risk metrics)
Data quality reports comparing synthetic vs. original distributions (fidelity scores)
Unstructured text and document generation for LLM fine-tuning and RAG pipelines
Subsetting and masking to produce realistic but smaller development datasets from large production sources
API-first design for integration with CI/CD pipelines and MLOps platforms
Deployment flexibility: cloud SaaS, self-hosted, or on-premises for air-gapped environments
What to Consider
Evaluation Checklist
Pricing Comparison
| Tool | Free tier | Paid entry | Higher tier | Best for |
|---|---|---|---|---|
| Gretel (NVIDIA NeMo) | None (sunset) | Custom via NVIDIA | Custom via NVIDIA | GPU-native agentic AI pipelines |
| MOSTLY AI | 2 credits/day (capped at 25/month) | ~$3/credit (Team) | ~$5/credit (Enterprise) | Self-service tabular and time-series generation |
| Tonic.ai | $10 monthly credits (Fabricate Free) | $29/month (Fabricate Plus) | Custom (Structural + Enterprise) | Dev/test data for software and AI teams |
| YData | Free plan with monthly credits | $1.00/credit (pay-as-you-go) | Custom (Enterprise, via KPMG) | Data-centric AI with profiling and synthesis together |
| Synthesized | None (demo only) | Custom (contact sales) | Custom (contact sales) | Complex enterprise systems: SAP, Oracle, Workday |
| Syntho | None (evaluation via demo) | Custom (Basic tier quote) | Custom (Ultimate tier quote) | Regulated industries needing feature-based licensing |
Pricing verified June 2026; confirm on the vendor site. Gretel is now NVIDIA NeMo and no longer sold as a standalone SaaS. YData was acquired by KPMG in October 2025; standalone commercial availability may be limited. Credit costs for MOSTLY AI are per 1 million data points generated.
Mistakes to Avoid
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Evaluating synthetic data quality by visual inspection alone rather than running statistical distribution tests: data that looks correct can still diverge significantly on tail distributions
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Generating synthetic data from a biased or incomplete source dataset and expecting the bias to disappear: synthetic generation amplifies existing data quality problems, it does not fix them
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Using synthetic data for model evaluation as well as training without maintaining a held-out sample of real data for final validation: this masks real-world performance gaps
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Picking a cloud-only tool before confirming data can legally leave the jurisdiction: regulatory requirements discovered post-contract force expensive replanning
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Overlooking the open-source SDK option (MOSTLY AI, YData): for teams with engineering capacity, self-hosted generation eliminates per-credit costs at scale
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Conflating test data management (de-identifying existing data for dev/test environments) with synthetic data generation from scratch: both have valid use cases but require different tooling and validation approaches
Expert Tips
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Start every evaluation by profiling your source data first (use YData Profiling or a comparable tool): understanding the real distribution before generation makes it much easier to validate whether the synthetic output is actually representative
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For LLM fine-tuning workloads, combine synthetic tabular data (MOSTLY AI, Tonic Fabricate) with a rule-based augmentation layer to inject domain-specific edge cases that generative models tend to underrepresent
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If budget is constrained, use the MOSTLY AI open-source SDK for bulk generation and reserve the paid SaaS tier for quality reporting and team collaboration features only
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Always maintain a 10-20% held-out sample of real data that never touches the synthetic generation pipeline: use it as the ground-truth benchmark for final model validation
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For time-series generation specifically, require the vendor to demonstrate preservation of autocorrelation structure, not just mean and variance: many tools pass simple distribution tests but fail on temporal dependency
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In regulated industries, get written confirmation from the vendor that their synthetic data meets the definition of anonymized data under the relevant regulation (GDPR Recital 26, HIPAA Safe Harbor) before using it as a compliance justification
Red Flags to Watch For
- !Vendor claims GDPR compliance without providing a formal re-identification risk score or differential privacy guarantee: marketing language is not a substitute for mathematical privacy guarantees
- !No published benchmark comparing synthetic output distributions against real data: fidelity claims without reproducible metrics are unverifiable
- !Cloud-only deployment with no data processing agreement (DPA) available: a red flag for regulated industries where data cannot cross jurisdictions
- !Credit or consumption pricing with no cap or alert mechanism: open-ended billing models can produce unexpected invoices at scale
- !Vendor recently acquired with no published product roadmap or explicit continuity commitment for existing contracts
The Bottom Line
For most ML and data engineering teams, MOSTLY AI is the default starting point: its open-source SDK removes lock-in risk, the free SaaS tier is genuinely functional, and its tabular fidelity benchmarks remain among the strongest in the category. Teams with both structured and unstructured data needs, or those running CI/CD-heavy workflows, should look at Tonic.ai for its multi-product coverage and developer-first pricing. Enterprises in regulated industries (finance, healthcare, insurance) where consumption-based pricing is a governance concern will find Syntho's feature-based licensing model a better fit. Note that Gretel (now NVIDIA NeMo) and YData (now KPMG) are no longer independent commercial products and should only be evaluated by teams already embedded in those ecosystems.
Frequently Asked Questions
Is Gretel AI still available as a standalone product after the NVIDIA acquisition?
No. NVIDIA acquired Gretel in March 2025 for a reported $320 million. As of 2026, gretel.ai redirects to NVIDIA's use-case page for synthetic data generation, and the technology is integrated into NVIDIA NeMo (specifically NeMo Data Designer). There is no standalone SaaS subscription available. Teams that need Gretel's capabilities today must access them through NVIDIA's cloud programs or GPU infrastructure.
What happened to YData after the KPMG acquisition?
KPMG announced the acquisition of YData's IP and technology assets in October 2025, with YData's founders joining KPMG to build a Synthetic Data Center of Excellence. The ydata.ai site still displays the platform as available (including a pay-as-you-go SDK at $1.00 per credit), but the long-term commercial roadmap for independent buyers is uncertain. Enterprise support now routes through KPMG, which adds procurement complexity for companies outside the KPMG client base.
What is the difference between synthetic data generation and test data management (TDM)?
Test data management typically refers to de-identifying or subsetting real production data so it can be used safely in development and test environments. Synthetic data generation creates entirely new records modeled on real data distributions, with no original records in the output. Some platforms (Tonic.ai Structural, Synthesized) do both; others (MOSTLY AI, Syntho) focus primarily on generation. For compliance purposes, the distinction matters: generated synthetic data may qualify as anonymized under GDPR, while masked or subsetted data may still be considered personal data.
How do I measure whether synthetic data is actually high quality?
Use three metrics together: fidelity (do univariate and multivariate distributions of the synthetic data match the real data, measured by metrics like Jensen-Shannon divergence or Wasserstein distance), utility (does a model trained on synthetic data perform comparably to one trained on real data), and privacy (does the data pass re-identification risk tests such as singling out, linkage, and inference attacks). All the major platforms in this guide provide automated fidelity and privacy reports; utility testing requires running your actual downstream model training pipeline.
Can synthetic data be used to fine-tune large language models?
Yes, and this is one of the fastest-growing use cases in 2026. NVIDIA NeMo Data Designer is specifically designed for LLM fine-tuning dataset creation. MOSTLY AI and Tonic Fabricate both support text generation alongside tabular data. The key consideration is that LLM fine-tuning requires structured instruction-response pairs or domain-specific text, not just raw tabular records, so confirm the platform supports your specific output format (JSONL, Parquet, CSV) and can generate semantically coherent text at scale.
Which synthetic data tools support self-hosted or on-premises deployment?
Tonic.ai offers self-hosted deployment on Enterprise plans for both Structural and Fabricate. Syntho supports on-premises Kubernetes deployment across all its tiers. Synthesized supports AWS, Azure, GCP, and on-premises deployment. MOSTLY AI offers cloud SaaS and an open-source SDK that can be run locally. YData similarly provides an open-source SDK and a Kubernetes-based self-hosted option. NVIDIA NeMo runs on NVIDIA GPU infrastructure (cloud or on-premises), which means substantial hardware investment.
How does credit-based pricing work for MOSTLY AI and what does it cost at scale?
MOSTLY AI's free tier provides 2 credits per day (capped at 25 per month). One credit generates up to 1 million data points for standard volumes, scaling to 10 million data points when source datasets exceed 1 billion rows. The Team paid tier is priced at approximately $3 per credit; the Enterprise tier at approximately $5 per credit. A team generating 20 million data points per month on the Team tier would use roughly 20 credits, costing around $60. At high-volume workloads (billions of data points), costs scale proportionally, making the open-source SDK or an enterprise plan the better option.
What privacy guarantees should I require from a synthetic data vendor?
At minimum, require a formal re-identification risk report covering three attack types: singling out (can an attacker identify a specific individual), linkage (can synthetic records be linked to external datasets to re-identify individuals), and inference (can attributes be inferred about individuals in the source data). For regulated industries, ask specifically whether the output qualifies as anonymized data under GDPR Recital 26 or HIPAA Safe Harbor, and get this in writing. Differential privacy with a stated epsilon value provides a stronger mathematical guarantee and is worth requiring for sensitive healthcare or financial data.
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