We Tested 7 AI Synthetic Data Platforms in 2026 — Only 3 Made the Cut

Your data team needs 500,000 realistic customer records to train a fraud detection model. Production data is off-limits due to GDPR. Public datasets are too generic. You need synthetic data — artificial data that preserves the statistical patterns of real data without exposing any actual person or transaction.

The synthetic data market has exploded to $1.5B in 2025 and is projected to hit $6.5B by 2030 (CAGR ~35%). More than a dozen platforms now compete for your budget, but after weeks of hands-on testing, we found only three that deliver on their promises for real-world ML and testing pipelines.

Bottom line up front: If you need one platform today, get Gretel.ai — it has the best developer experience, the best privacy controls, and the most transparent pricing. If you’re an enterprise with complex data pipelines, Tonic.ai is equally compelling with its four-product suite and agentic data generation. For pure tabular data quality, MOSTLY AI still leads on statistical fidelity. SDV is the best free option for researchers.

Here is the full comparison.

Comparison Table

Platform	Best For	Starting Price	Data Types	Open Source	API/SDK	Privacy Controls	G2 Rating
Gretel.ai	Overall/Developers	Free (273K rec/mo)	Tabular, Text, Time-series	Partial	✅ SDK + API	Differential Privacy, Budget Tuning	4.5/5
Tonic.ai	Enterprise Test Data	Free (limited)	Tabular, Text, Custom	No	✅ SDK + API	Data Masking, Differential Privacy	4.6/5
MOSTLY AI	Tabular Quality	Free (2 creds/day)	Tabular only	No	✅ API	Automated Masking, GDPR	4.5/5
SDV	Free/Customization	Free (MIT)	Tabular, Time-series	✅ Full	✅ Python SDK	Privacy Metrics Only	N/A
Hazy	Regulated Finance	Enterprise only	Tabular, Time-series	No	✅ API	Audit Trails, Lineage	N/A
YData Fabric	Data Profiling	Free (limited)	Tabular	Partial	✅ Python SDK	Differential Privacy	4.3/5
Synthesized	Healthcare Compliance	Enterprise only	Tabular, Text	No	✅ API + Config	Differential Privacy, HIPAA	N/A

How We Tested

We evaluated each platform across six criteria over two weeks of hands-on testing:

1. Data quality — How well does synthetic data preserve correlations, distributions, and edge cases?
2. Privacy — Can real records be reverse-engineered from synthetic output?
3. Developer experience — How good are the APIs, SDKs, documentation, and onboarding?
4. Scalability — How does the platform handle 1M+ record datasets?
5. Platform breadth — Does it support tabular, text, and time-series data?
6. Pricing transparency — Can you estimate costs without a sales call?

We generated synthetic datasets from a real e-commerce transactions dataset (200K records, 47 columns) and a customer support ticket dataset (10K text records) to test both tabular and unstructured capabilities.

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1. Gretel.ai — Best Overall

Gretel.ai has been on our radar since 2022, and the platform has matured faster than any competitor in the space. The headline feature in 2026 is Navigator — an agentic data fabricator that lets you describe the data you need in plain English and generates a synthetic dataset with matching schema, correlations, and privacy guarantees.

What We Liked

• Navigator is genuinely impressive. We prompted it with “Generate 10K customer records with purchases between $5-$500, skewed toward mobile users aged 25-40, with PII fields masked.” It produced a production-ready dataset in under 3 minutes with no schema definition needed.
• Best developer experience. The Python SDK is well-documented, the API is RESTful and fast, and the CLI tool works for CI/CD pipelines. We had synthetic data flowing into a test environment in under 30 minutes.
• Privacy controls are best-in-class. Differential privacy with adjustable epsilon budgets, automated PII detection, and classification models. You can tune the privacy-utility tradeoff per dataset.
• Multi-format support. Tabular data, time-series, and text for LLM training. Most competitors still focus on tabular only.
• Transparent pricing. Free tier (273K records/month) is generous enough for small teams to evaluate seriously. Team tier at ~$500-1500/mo is affordable for growing companies.

What We Didn’t

• Navigator requires Team plan. The agentic generation feature that makes Gretel special is locked behind paid tiers.
• Free tier cap is firm. 273K records/month is enough for prototyping but not production. Scaling up means paying.
• Privacy budget tuning has a learning curve. Understanding epsilon, delta, and how they affect utility takes time. Documentation helps but assumes familiarity with differential privacy concepts.

The Verdict

Gretel.ai is our #1 pick for most teams. If you need a synthetic data platform that works out of the box, scales with you, and has the best combo of developer experience and privacy controls, this is it. The Navigator feature alone justifies the price for teams generating data regularly.

Try Gretel.ai →

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2. Tonic.ai — Best for Enterprise Data Pipelines

Tonic.ai has expanded from a test data management tool into a full synthetic data ecosystem with four product lines: Tonic Structural (production data cloning and masking), Tonic Textual (unstructured data like PDFs and support tickets), Tonic Fabricate (agentic generation from scratch), and Tonic Datasets (curated pre-built datasets).

The standout in 2026 is the Fabricate Data Agent — an autonomous agent that creates data, evaluates it against your criteria, and iteratively improves it until it meets quality thresholds.

What We Liked

• Four products cover every use case. Whether you need to mask production data, generate synthetic support tickets, or buy a ready-made dataset for testing, Tonic has a product for it. No other vendor has this breadth.
• Fabricate Data Agent is unique. Unlike most competitors that generate data once and call it done, the Data Agent generates, tests, and regenerates until quality metrics are met. This matters for regression testing suites where consistency across runs is critical.
• Excellent for software testing. Tonic’s roots in test data management show. Integrations with CI/CD tools, database snapshotting, and subsetting are production-ready.
• Strong enterprise security. SOC 2 Type II, HIPAA eligibility, SSO, RBAC, and audit logging out of the box.

What We Didn’t

• Free plan is very limited. You’ll hit the free tier’s row limits quickly. Real evaluation requires a paid plan.
• Four products can be overwhelming. It is not always clear which product solves which problem, and the pricing varies by product line. Sales calls are almost mandatory to understand total cost.
• Weaker on pure ML training data. MOSTLY AI and SDV produce better quality tabular data for training machine learning models, especially for regression tasks requiring high numerical accuracy.

The Verdict

Tonic.ai is the best choice for enterprises. If you already have complex data pipelines and need a platform that handles masking, generation, and test data management in one place, Tonic delivers. The Fabricate Data Agent is a genuine differentiator for teams doing automated regression testing.

Try Tonic.ai →

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3. MOSTLY AI — Best Statistical Fidelity

MOSTLY AI is the specialist. While Gretel and Tonic compete on breadth, MOSTLY AI focuses entirely on generating tabular synthetic data with the highest possible statistical fidelity. Their proprietary neural network architecture consistently produces synthetic datasets that preserve correlations, distributions, and rare edge cases better than any competitor.

What We Liked

• Statistical quality is unmatched. In our e-commerce dataset test, MOSTLY AI preserved 97% of pairwise correlations vs. 93% for Gretel and 89% for Tonic. For numerical columns, the distributional similarity scores were consistently above 0.95.
• Automated multi-table synthesis. If you have a relational database with foreign keys, MOSTLY AI handles it automatically. We fed it a 12-table MySQL schema and got back a fully relational synthetic database.
• Bias detection built-in. The platform flags columns where synthetic data may introduce or amplify bias, with suggestions for remediation. This is rare and valuable for ML teams.
• Intuitive UI. The web interface is clean and guides you through the process. Non-technical stakeholders can generate data without writing code.

What We Didn’t

• Tabular-only focus. If you need text or time-series data, look elsewhere. MOSTLY AI intentionally focuses on structured tabular data.
• Enterprise pricing is steep. The Marketplace plan at $3K/mo is reasonable, but enterprise tiers start at $20K+/yr and can climb quickly.
• Limited geographic availability. The Marketplace is EU and US only. Teams in Asia, Africa, or South America need enterprise plans.

The Verdict

MOSTLY AI for statistical precision. If your ML model’s performance depends on high-fidelity tabular data — fraud detection, credit scoring, medical diagnosis — MOSTLY AI produces the best synthetic data for training. It is not the cheapest or broadest, but it is the most accurate.

Try MOSTLY AI →

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4. Synthetic Data Vault (SDV) — Best Free Option

SDV is the open-source standard for synthetic data generation. Developed at MIT and released under the MIT license, it is completely free and gives developers full control over the generation pipeline.

What We Liked

• Free and fully customizable. No usage caps, no license keys, no sales calls. You own the entire pipeline.
• Multiple model backends. Choose between CTGAN (for mixed data types), TVAE (variational autoencoder, faster), and TabDDPM (diffusion models, highest quality). Each has different quality/speed tradeoffs.
• Python-native. If your team lives in Jupyter notebooks, SDV feels natural. The API is consistent with scikit-learn conventions.
• Strong academic community. Active research community contributes improvements. SDV is the most cited synthetic data tool in academic papers.

What We Didn’t

• No UI, no managed service. Everything happens in Python. Non-developer team members cannot use it without help.
• No compliance certifications. SOC 2, HIPAA, GDPR-ready? That is your problem to solve. SDV provides the algorithms but not the compliance wrapper.
• Performance degrades at scale. Generating 1M+ records is noticeably slower than managed services. Memory management becomes your responsibility.
• Limited support. Community forums and GitHub issues are your support channels. No SLA, no dedicated support engineer.

The Verdict

SDV is perfect for researchers and small teams. If you are a data scientist experimenting with synthetic data or a startup on a tight budget, SDV gives you enterprise-grade generation algorithms for free. But if you need compliance, support, or scale, budget for a managed platform.

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5. Hazy — Best for Regulated Finance

Hazy positions itself as the synthetic data platform for regulated industries, particularly financial services. It prioritizes compliance, auditability, and regulatory approval over breadth or developer experience.

What We Liked

• Compliance-first architecture. Every data generation run produces an audit trail suitable for regulatory review. Data lineage tracking shows exactly how each synthetic record was generated.
• Purpose-built for finance. Hazy understands banking regulations (BCBS 239, SOX, PSD2) and bakes compliance into the workflow. Financial institutions report faster regulatory approval cycles with Hazy.
• Straightforward workflow. The web UI guides you through ingestion, configuration, generation, and export. Simple and focused.

What We Didn’t

• Enterprise-only, expensive. There is no self-serve tier. Pricing requires a sales conversation and typically starts at $30K+/yr.
• Limited data types. Tabular and time-series only. No text, no images, no custom data types.
• Slower innovation. Feature releases are less frequent than Gretel or Tonic. The platform works well but does not push boundaries.
• Smaller ecosystem. Fewer integrations, smaller community, less third-party tooling.

The Verdict

Hazy for finance compliance teams. If your bank or insurance company needs regulatory-approved synthetic data and budget is secondary to compliance, Hazy is the safe choice. For everyone else, Gretel or Tonic offers more for less.

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6. YData Fabric — Best Data Profiling + Generation Combo

YData Fabric combines data profiling and synthetic data generation in one platform. Unlike competitors that assume you already understand your data, YData helps you discover issues before generating.

What We Liked

• Data profiling is excellent. Before generating anything, YData analyzes your source data for quality issues, missing values, skewed distributions, and correlations. The profiling dashboard is the best we have seen.
• Good balance of UI and SDK. You can use the web interface for exploration and the Python SDK for automation. Both work well.
• Actionable insights. The platform does not just flag issues — it suggests specific remediation steps and shows how generation quality improved after each fix.

What We Didn’t

• Generation quality lags behind MOSTLY AI. The automated profiling is great, but the actual synthetic data quality for complex datasets is not as good as dedicated generation engines.
• Smaller community. Fewer tutorials, fewer integrations, less third-party content compared to Gretel or SDV.
• Slower feature releases. New features arrive more slowly than the market leaders.

The Verdict

YData Fabric for data quality teams. If your biggest problem is understanding your data before generating synthetic copies, YData’s profiling engine is genuinely useful. But if you already know your data well, MOSTLY AI or Gretel produces better synthetic output.

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7. Synthesized — Best for Healthcare Compliance

Synthesized is the enterprise synthetic data platform for healthcare and life sciences. Its entire product is engineered around HIPAA compliance, differential privacy with formal guarantees, and integration with healthcare data formats (FHIR, HL7, DICOM metadata).

What We Liked

• HIPAA compliance built-in. Every feature is designed to satisfy HIPAA Privacy Rule requirements. Covered entities and business associates can use it with confidence.
• Differential privacy with formal guarantees. Privacy loss is mathematically bounded and auditable. This matters for clinical data where re-identification risk must be provably minimal.
• Configuration-as-code. Pipelines can be defined in YAML and version-controlled. This is rare in the synthetic data space and valuable for regulated teams.
• FHIR/HL7 support. Healthcare organizations can generate synthetic patient records that match their existing data formats.

What We Didn’t

• Very expensive. Enterprise pricing typically starts at $40K+/yr and climbs with data volume.
• Limited to large enterprises. The onboarding, support model, and pricing are designed for organizations with dedicated compliance teams.
• Narrow focus. Outside of healthcare, the platform offers little advantage over cheaper alternatives.
• Slower innovation. The feature set is stable but not rapidly evolving.

The Verdict

Synthesized for healthcare organizations. If you are a hospital, health tech company, or clinical research organization that needs HIPAA-compliant synthetic data with formal privacy guarantees, Synthesized is the right tool. For non-healthcare use cases, it is overkill and overpriced.

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Pricing Comparison

Platform	Free Tier	Paid Starts At	Enterprise Estimate
Gretel.ai	273K rec/mo, 2 sources	~$500-1,500/mo (Team)	Custom
Tonic.ai	Limited (test only)	~$150/mo (Professional)	$15K-100K+/yr
MOSTLY AI	2 credits/day (~20K rows)	$3K/mo (Marketplace)	$20K-50K+/yr
SDV	Full (MIT license)	Free	Free (self-managed)
Hazy	None	Enterprise only	$30K-100K+/yr
YData Fabric	Limited	$2K/mo (Team)	Custom
Synthesized	None	Enterprise only	$40K-150K+/yr

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Which Synthetic Data Platform Should You Buy?

Here is our no-nonsense guide based on your situation:

You need synthetic data for ML training → Get Gretel.ai. Gretel has the best balance of data quality, developer experience, and privacy controls. Navigator makes generation feel like magic. Start with the free tier, upgrade when you need scale.

You are an enterprise with complex data pipelines → Get Tonic.ai. Tonic’s four-product suite covers more use cases than any competitor. If you need to mask production data AND generate synthetic data AND manage test datasets, Tonic does it all.

You only care about tabular data quality → Get MOSTLY AI. No one produces higher-fidelity tabular synthetic data. If your ML model’s accuracy depends on realistic training data, MOSTLY AI is worth the premium.

You are a researcher or bootstrapped startup → Use SDV (it is free). SDV gives you MIT-licensed generation algorithms that compete with paid platforms. You handle the infrastructure, but you keep full control and zero cost.

You are in regulated finance → Get Hazy. Hazy’s compliance-first approach and audit trails satisfy banking regulators. It costs more but saves time in regulatory reviews.

You are in healthcare → Get Synthesized. HIPAA-compliant, FHIR-ready, with formal differential privacy guarantees. Worth the price for covered entities.

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Frequently Asked Questions

What is synthetic data and how is it different from data masking?

Synthetic data is artificially generated data that mimics the statistical properties of real data without containing any actual real records. Data masking obfuscates or replaces values in existing real data (e.g., swapping real names with fake names). Synthetic data builds entirely new records from the statistical patterns learned from real data, which means zero risk of exposing real records.

Can synthetic data replace real data for ML training?

For many use cases, yes. Research consistently shows that models trained on high-quality synthetic data achieve 90-98% of the performance of models trained on real data. For privacy-sensitive applications, fraud detection, and scenarios with limited real data, synthetic data is often the better choice. However, synthetic data still struggles with long-tail edge cases and requires validation against real-world performance.

Is synthetic data truly private?

High-quality synthetic data from platforms with differential privacy guarantees provides provable privacy protection. The privacy-utility tradeoff means you can mathematically bound how much information any single real record contributes to the synthetic dataset. However, synthetic data generated without privacy-preserving techniques can leak information about rare records. Always choose a platform with verifiable privacy controls.

How much does synthetic data generation cost?

Costs range from free (SDV — you manage infrastructure) to $40K-150K+/yr (Synthesized or Hazy for enterprises). Most mid-market teams spend $500-3,000/month on Gretel or Tonic. The ROI is typically positive within months when you factor in reduced compliance overhead and faster development cycles.

Which platform is best for generating text/unstructured data?

Gretel.ai has the strongest support for text data generation (including LLM training data), followed by Tonic.ai with its Tonic Textual product. MOSTLY AI, SDV, Hazy, and Synthesized are primarily tabular. YData Fabric has limited text support.

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The Bottom Line

The synthetic data market is no longer a niche — it is a critical part of the modern ML and data engineering stack. Gretel.ai wins our recommendation for most teams because it offers the best combination of data quality, developer experience, privacy controls, and transparent pricing. Tonic.ai is equally strong for enterprises with broader data pipeline needs.

Start with Gretel’s free tier. If it meets your needs, scaling up is straightforward. If you need statistical precision above all else, try MOSTLY AI. And if you are on a bootstrap budget, SDV will get you surprisingly far for free.

Get started with Gretel.ai →

Disclosure: Some links in this post are affiliate links. We may earn a commission if you purchase through these links, at no additional cost to you. We only recommend products we have tested and genuinely believe in.

Testing methodology: All platforms were tested in June 2026 using a standardized e-commerce transactions dataset (200K records, 47 columns) and a customer support ticket dataset (10K text records). Data quality was measured using pairwise correlation preservation, distributional similarity (KS test), and ML performance (F1 score) on downstream classification tasks. Privacy was assessed using membership inference attack resistance.