Deepak Gupta

AI Tools · Synthetic Data Generation

Top 5 Synthetic Data Generation Platforms of 2026: MOSTLY AI vs Tonic.ai vs the Rest

Synthetic data generation platforms compared: MOSTLY AI, Tonic.ai, YData, SDV (Synthetic Data Vault), and Synthesized, after Gretel's acquisition by NVIDIA and Hazy's acquisition by SAS reshaped this market.

By Deepak Gupta·Aug 16, 2026·12 min·5 tools compared

Synthetic DataData PrivacyMachine LearningData EngineeringGDPR ComplianceTest Data Management

Quick Comparison

Platform	Best For	Data Type Focus	Privacy Approach	Pricing
MOSTLY AI	High-fidelity synthetic tabular data for ML training with measurable privacy scores	Tabular, time-series	Statistical privacy metrics published per job, Apache 2.0 open-source SDK	Free (5 credits/day); Team $3/credit; Enterprise $5/credit
Tonic.ai	De-identifying and synthesizing production databases for dev, test, and healthcare workflows	Relational databases, unstructured text (via Textual)	De-identification with optional expert determination for HIPAA	Fabricate from $0-$29/month plus usage; Structural custom, per-source pricing
YData	ML teams fixing data quality gaps (imbalance, missing values) before training	Tabular, relational, time-series	GDPR-oriented profiling plus synthesis; no published self-serve privacy certification	Custom, usage-based; contact sales
SDV (Synthetic Data Vault)	Teams that want to own the generation pipeline instead of renting a SaaS platform	Tabular, relational, time-series (Python library)	Differential privacy available as a paid Enterprise bundle, not in the free tier	Community: free (Business Source License); Enterprise: $500/month/user plus $250/month per bundle
Synthesized	Compliance teams that want data provisioning rules codified as auditable policy	Tabular, with masking and subsetting	"Data as Code" approach codifies compliance rules into the generation pipeline itself	Custom enterprise pricing

MOSTLY AI

Best For: High-fidelity synthetic tabular data for ML training with measurable privacy scores
Data Type Focus: Tabular, time-series
Privacy Approach: Statistical privacy metrics published per job, Apache 2.0 open-source SDK
Pricing: Free (5 credits/day); Team $3/credit; Enterprise $5/credit

Tonic.ai

Best For: De-identifying and synthesizing production databases for dev, test, and healthcare workflows
Data Type Focus: Relational databases, unstructured text (via Textual)
Privacy Approach: De-identification with optional expert determination for HIPAA
Pricing: Fabricate from $0-$29/month plus usage; Structural custom, per-source pricing

YData

Best For: ML teams fixing data quality gaps (imbalance, missing values) before training
Data Type Focus: Tabular, relational, time-series
Privacy Approach: GDPR-oriented profiling plus synthesis; no published self-serve privacy certification
Pricing: Custom, usage-based; contact sales

SDV (Synthetic Data Vault)

Best For: Teams that want to own the generation pipeline instead of renting a SaaS platform
Data Type Focus: Tabular, relational, time-series (Python library)
Privacy Approach: Differential privacy available as a paid Enterprise bundle, not in the free tier
Pricing: Community: free (Business Source License); Enterprise: $500/month/user plus $250/month per bundle

Synthesized

Best For: Compliance teams that want data provisioning rules codified as auditable policy
Data Type Focus: Tabular, with masking and subsetting
Privacy Approach: "Data as Code" approach codifies compliance rules into the generation pipeline itself
Pricing: Custom enterprise pricing

1

MOSTLY AI

Best Overall

Best for: Enterprises that need high-fidelity synthetic tabular data with a documented privacy score, for banking, insurance, and telecom workloads

“MOSTLY AI is the most mature dedicated synthetic data platform still operating independently in 2026, built specifically around replacing real tabular records with statistically equivalent synthetic ones without leaking individual identities. Its fidelity, utility, and privacy reports ship with every generation job, which is closer to an audit trail than anything else in this category. It stays narrowly focused on structured data and its credit pricing punishes high-frequency regeneration, but for the core job, turning a production database into something a data scientist can train on without touching real records, it is still the safest first call.”

Pros

Publishes quantified fidelity, utility, and privacy metrics with every generation job instead of asking buyers to trust a black box, which matters when a compliance team has to sign off on the output
Open-sourced its core synthetic data SDK under Apache 2.0 in late 2024, so the generation logic can be inspected and self-hosted rather than trusted blind
Won a $196,800 U.S. Department of Homeland Security contract for privacy-enhancing synthetic data capabilities, a rare instance of third-party government validation in this category
Free tier (5 credits/day, no expiration) generates genuinely usable test datasets before any purchase decision

Cons

Credit-based pricing ($3/credit Team, $5/credit Enterprise; one credit covers roughly 1 million to 10 million data points depending on job size) gets expensive fast for teams regenerating data on every CI/CD run instead of on a periodic schedule
Focused on structured, tabular, and time-series data; teams needing synthetic unstructured text or document redaction need a separate tool
Strong statistical fidelity does not by itself satisfy a specific regulation; using the output for HIPAA or GDPR purposes still requires the buyer's own privacy risk assessment, not just the platform's built-in reports

Honest Weakness: MOSTLY AI's fidelity and privacy reporting is built for one job: turning a real tabular dataset into a synthetic one that is statistically close enough to train models or run realistic tests on, with a documented privacy score attached. It is not a de-identification tool for existing production databases, that is Tonic's lane, and it does not generate synthetic unstructured text, images, or documents. A team whose actual pain point is redacting PII inside existing dev and QA database copies, rather than generating new synthetic training data from scratch, will get a better fit from Tonic Structural. Credit-based pricing also rewards batch generation over continuous, high-frequency regeneration; a team re-synthesizing data on every pull request will burn through credits faster than the flat-fee alternatives on this list.

Fidelity, Utility, and Privacy Reporting

Every generation job on MOSTLY AI produces a report scoring how closely the synthetic dataset preserves the statistical distributions, correlations, and rare-event patterns of the source data, alongside a separate privacy score measuring re-identification risk. That combination matters because the two goals pull against each other: push fidelity too high and you start memorizing real records, push privacy too aggressively and the synthetic data stops being useful for training. Publishing both numbers per job, instead of a single marketing claim of "privacy-safe," gives a data scientist or privacy reviewer something concrete to evaluate rather than a vendor's word.

Where the Open-Source SDK Fits

MOSTLY AI open-sourced its core synthetic data generation SDK under Apache 2.0 in late 2024, which lets technical teams inspect the actual modeling approach and, if needed, self-host generation for sensitive workloads that cannot leave a private environment. The hosted platform layers the credit-based UI, connector library, and managed infrastructure on top of that same open core. This matters for buyers who want to avoid vendor lock-in on the algorithm itself even if they pay for the hosted convenience, a meaningfully different posture than fully closed competitors in this space.

Free tier: 5 credits/day, no expiration. Team: $3/credit. Enterprise: $5/credit. One credit generates up to 1 million data points for standard jobs, up to 10 million for larger volume jobs.

Visit MOSTLY AI

2

Tonic.ai

Best for Privacy

Best for: Engineering and healthcare teams that need to de-identify and synthesize real production databases for dev, test, and QA environments

“Tonic.ai solves a different problem than pure synthetic-data-from-scratch platforms: it starts from a real production database (often one holding PHI or PII) and produces a de-identified, referentially intact copy that developers can actually query and test against. Tonic Structural handles the de-identification, Tonic Fabricate generates synthetic data from a defined schema, and Tonic Textual extends the same idea to unstructured text. For healthcare and fintech teams whose real bottleneck is safely hydrating lower environments from regulated production data, this is a more direct fit than a general-purpose tabular synthesizer.”

Pros

Three purpose-built products (Structural for de-identification, Fabricate for from-scratch generation, Textual for unstructured text) cover a broader real-world workflow than a single generation engine
Offers an expert determination path for HIPAA Safe Harbor and Statistical de-identification, a concrete compliance mechanism rather than a generic privacy claim
Referential integrity across foreign keys is preserved automatically when de-identifying a relational database, avoiding the broken joins that hand-rolled masking scripts routinely produce
Fabricate's free tier ($5 in monthly usage credits) and $29/month Plus tier give small teams a real entry point instead of forcing an enterprise sales conversation immediately

Cons

Tonic Structural pricing scales per connected database or warehouse and reportedly runs from a few thousand to tens of thousands of dollars annually per source, which adds up fast for an organization with dozens of microservice-owned databases rather than one central data warehouse
Fabricate's synthetic-from-scratch generation is newer than Tonic's de-identification lineage; teams whose primary need is pure statistical fidelity for ML training, not database de-identification, should benchmark it against MOSTLY AI before committing
Expert determination for HIPAA still requires engaging a qualified statistician to certify the methodology; Tonic provides the workflow, not the legal sign-off itself

Honest Weakness: Tonic.ai is built for teams whose real problem is an existing production database, often one with real patient or customer records, that needs to become a safe, realistic copy for developers to work against. That framing is a strength for healthcare and fintech dev/test pipelines, and a mismatch for teams that do not have a production database to start from and instead need to generate synthetic training data for a model from a schema alone; MOSTLY AI's fidelity and privacy reporting is the better fit there. Structural's per-source pricing also means the total bill scales with how many databases you connect, not just how much data you generate, so an organization with many small service-owned databases should model that cost curve before assuming Tonic is cheaper than a flat-rate alternative.

De-identification Versus Generation From Scratch

Tonic's core lineage is de-identification: point Structural at a real production database and it produces a synthetic, referentially consistent copy where names, addresses, and other identifiers are replaced with realistic-but-fake values while foreign key relationships stay intact across every table. Fabricate, the newer product, flips the starting point: instead of transforming real data, it generates synthetic data from a defined schema using an agent-assisted workflow, useful when no representative production dataset exists yet or when the source data itself is too sensitive to touch even for de-identification. Buyers should be clear on which problem they actually have before picking a product, since the two solve genuinely different starting conditions.

HIPAA and Regulated Data Workflows

For healthcare teams, Tonic supports both HIPAA Safe Harbor de-identification (removing the 18 specified identifier categories) and Statistical/Expert Determination de-identification, where a qualified expert certifies that re-identification risk is very small given the specific dataset and context. The platform automates the mechanical transformation work; it does not replace the legal requirement that a qualified person certify the statistical determination. Teams evaluating Tonic for HIPAA-covered workflows should budget for that expert review as a real line item, not assume the software output is automatically compliant on its own.

Fabricate: Free ($5/month usage credits); Plus $29/month ($25 credits plus pay-as-you-go, roughly $0.17-$0.37 per generation "turn"); Enterprise custom. Structural and Textual: custom, per-source pricing, typically thousands to tens of thousands of dollars annually per connected database.

Visit Tonic.ai

3

YData

Runner Up

Best for: ML teams that need to detect and fix data quality problems, class imbalance, missing values, distribution gaps, before training, not just generate more rows

“YData Fabric's real differentiator is pairing automated data profiling with synthetic data generation, so the platform first tells you what is wrong with your dataset (imbalanced classes, missing segments, drift) and then generates synthetic records to fix the specific gap rather than blindly multiplying the dataset. KPMG's acquisition of YData in October 2025 adds enterprise credibility and integration into consulting engagements, but it also means the product's roadmap now runs through a professional services firm rather than a standalone synthetic data vendor.”

Pros

Combines automated data profiling with synthetic generation in one workflow, surfacing class imbalance and missing-value patterns before generating data to correct them, rather than treating generation as a separate blind step
Both a no-code interface and a Python SDK (the open-source ydata-synthetic package, still actively maintained as of April 2026) are available, covering both business analysts and engineers
GDPR-oriented framing built into the profiling and generation workflow, useful for teams that need to document a privacy rationale, not just produce output
KPMG acquisition (October 2025) brings enterprise sales, support, and integration capacity that a 41-person standalone startup would not otherwise have

Cons

No published self-serve pricing; every evaluation, even a small one, requires a sales conversation, which slows down teams that just want to try the product against a real dataset
Post-acquisition product direction is now KPMG's call, not YData's original team's alone, and buyers should ask directly what changes to standalone availability or roadmap independence the acquisition brings before committing
Positioned primarily for ML training data quality rather than production database de-identification, so it is not a direct substitute for Tonic's dev/test/PHI workflow

Honest Weakness: YData Fabric is the right tool when the actual problem is dataset quality, not just dataset size: a fraud model with too few positive examples, a churn dataset missing an entire customer segment, training data with drift versus production. Its profiling-first workflow catches those problems before generation, which most pure synthesizers do not do. The trade-off is real: YData is not built for de-identifying an existing regulated production database (Tonic's job) and, since the KPMG acquisition, its pricing and roadmap now run through an enterprise consulting relationship rather than transparent self-serve tiers. Teams that want to self-evaluate quickly without a sales call, or that specifically need HIPAA-grade de-identification, will hit friction here that they will not hit with MOSTLY AI's free tier or Tonic's published Fabricate pricing.

Profiling-First Workflow

YData Fabric runs automated data profiling before generation, flagging class imbalance, missing values, and distribution gaps in the source dataset as a distinct step rather than folding quality assessment silently into the generation model. That ordering matters for ML teams specifically, because synthetic data generated from a flawed source dataset just reproduces the flaw at scale; a synthesizer that does not surface the imbalance first can hand back a larger, equally biased dataset without anyone noticing until the model underperforms in production.

The KPMG Acquisition

KPMG acquired YData in October 2025, folding a previously independent, VC-backed synthetic data startup into a Big Four consulting firm's technology practice. As of mid-2026 the product continues shipping (the open-source ydata-synthetic package saw updates as recently as April 2026), and the acquisition brings real enterprise distribution YData did not have alone. Buyers evaluating YData for a multi-year commitment should ask directly about standalone product roadmap independence and self-serve availability going forward, since consulting-firm ownership structures have historically shifted product priority toward services engagements over self-serve software.

Custom, usage-based pricing tied to compute and data volume; no published self-serve tiers as of this writing. Contact sales for a quote.

Visit YData

4

SDV (Synthetic Data Vault)

Best Open Source

Best for: Engineering teams that want to own the synthetic data pipeline as code instead of renting a hosted SaaS platform

“SDV is the open-source foundation this entire category grew out of, originally an MIT project, now maintained commercially by DataCebo as a Python library covering tabular, relational, and time-series synthesis. The Community edition is free and has millions of downloads, which makes it the honest answer for teams that want to prototype or run synthetic data generation without a subscription. It requires real engineering effort to operate and validate, and the free tier's Business Source License caps how far you can take it in production without eventually paying.”

Pros

Community edition is free with no data volume cap, covering 5 data types, 9 modeling algorithms, and 5 basic constraints, enough to build and test a real synthetic data pipeline before spending anything
Millions of downloads and an active GitHub project (sdv-dev/SDV) give it the largest independent community of any tool in this comparison, meaning more Stack Overflow answers and third-party tutorials
Runs entirely inside your own infrastructure since it is a Python library, not a hosted SaaS platform, which matters for teams that cannot send data to a third-party service under any circumstances
Differential privacy is available as a paid add-on for teams that need a formal, mathematically defined privacy guarantee rather than a heuristic one

Cons

Free Community edition is licensed under the Business Source License, not a fully permissive open-source license, and explicitly limits commercial use, so legal should review the license terms before shipping it inside a commercial product
No managed hosting, dashboards, or built-in fidelity/privacy reporting in the free tier; a data engineer has to build and interpret that validation layer manually, which is real, uncompensated engineering time
Enterprise tier ($500/month/user plus $250/month per feature bundle) is priced per user rather than per data volume, which can get expensive fast for a team of more than two or three people who all need Enterprise features like differential privacy or advanced constraints

Honest Weakness: SDV is the right call for a team with real Python and data engineering capacity that wants to own the generation pipeline outright, self-host it, and avoid recurring SaaS fees for straightforward tabular synthesis. It is the wrong call for a team that wants a turnkey product with a UI, published fidelity and privacy reports out of the box, and vendor support on day one; all of that exists only in the $500/month/user Enterprise tier, which erases most of the cost advantage of "free and open source" once a team of any real size needs those features. Compliance teams should also read the Business Source License terms carefully before assuming the free Community edition is safe to embed in a commercial product without a paid license.

From MIT Research Project to Commercial Library

SDV began as an academic project at MIT and has since become the most widely used open-source library for synthetic tabular, relational, and time-series data, now maintained commercially by DataCebo. The library ships a range of modeling approaches, from simpler statistical copulas to deep-learning-based synthesizers (CTGAN and variants), letting a team choose a trade-off between generation speed and statistical fidelity rather than being locked into one vendor's proprietary model.

The Real Cost of "Free"

The Community edition genuinely costs nothing to run and has no artificial data volume limit, which is a real advantage over credit-metered SaaS platforms for teams doing occasional or exploratory synthetic data work. What it does not include is turnkey validation: MOSTLY AI and Tonic both ship fidelity and privacy scoring as a built-in report; SDV expects the team to write and interpret that evaluation themselves, typically using SDV's own separate SDMetrics package. For a well-staffed data engineering team that is a reasonable trade for zero license cost. For a smaller team without a dedicated data engineer, that DIY validation burden is the real price of the free tier, paid in engineering hours instead of dollars.

Community: free (Business Source License, limited commercial use, 5 data types, 9 models). Enterprise: $500/month per user, plus $250/month per optional feature bundle (differential privacy, constraint-augmented generation, targeted sampling, and others).

Visit SDV (Synthetic Data Vault)

5

Synthesized

Honorable Mention

Best for: Compliance and data governance teams that want synthetic data provisioning codified as auditable, version-controlled policy rather than one-off scripts

“Synthesized's "Data as Code" pitch, treating compliance and masking rules as version-controlled configuration rather than manual, one-off transformations, is a genuinely useful framing for governance-heavy organizations. It is the smallest and least established platform in this comparison, with roughly 14 employees and a narrower documented integration ecosystem than the other four, which is a real consideration for a multi-year platform commitment. It earns its place here for the specific buyer who values that governance framing over raw benchmark fidelity.”

Pros

"Data as Code" approach lets compliance rules for masking, subsetting, and synthesis be defined once, version-controlled, and reused consistently across environments instead of re-implemented by hand each time
Combines synthetic data generation with data masking and subsetting in one platform, useful for teams that need more than pure generation, specifically selective real-data provisioning alongside synthesis
UK-based with explicit GDPR-first positioning, relevant for organizations whose primary regulatory exposure is European rather than U.S. HIPAA-style rules
Raised a Series A round in May 2025 with five participating investors, evidence of continued external validation rather than a company coasting on old funding

Cons

Smallest team of the five platforms compared here (around 14 employees as of early 2025), a real factor in long-term support capacity and roadmap velocity versus better-resourced competitors
Narrower documented public integration and partner ecosystem than MOSTLY AI, Tonic, or YData, meaning more custom integration work for teams outside its core supported data sources
No published pricing tiers found publicly; every evaluation requires a direct sales conversation before you know if it fits budget

Honest Weakness: Synthesized earns its spot on this list for a specific reader: a compliance or data governance team that wants masking, subsetting, and synthesis rules defined once as reviewable, version-controlled configuration, then enforced consistently, rather than a team purely chasing the highest statistical fidelity benchmark. For that governance-first buyer, the "Data as Code" model is a real, distinct advantage over the other four platforms. For a buyer whose main question is raw synthetic data quality at scale, or who wants a large existing customer base and integration ecosystem to lean on, Synthesized's small team size and thinner public track record compared to MOSTLY AI or Tonic are real risk factors worth weighing before a multi-year commitment.

Data as Code

Synthesized's core differentiator is treating data provisioning rules, which fields get masked, which get synthesized, which subsetting logic applies, as version-controlled code rather than manual configuration inside a UI. For a governance team that already runs infrastructure-as-code practices elsewhere, this maps compliance requirements onto a workflow they already understand: pull requests, code review, and audit trails for changes to how sensitive data gets handled, instead of a change log buried inside a vendor dashboard.

A Smaller Bet in a Consolidating Market

Synthesized operates in a market that has seen real consolidation: Gretel was acquired by NVIDIA and its standalone product discontinued, Hazy was acquired by SAS and folded into SAS Data Maker, and YData was acquired by KPMG. Synthesized has stayed independent and raised a Series A as recently as May 2025, but buyers should weigh that independence against the real possibility of acquisition or discontinuation that every smaller player in this space has faced in the last two years, and should ask directly about data portability and exit options before a long-term commitment.

Custom enterprise pricing; no published self-serve tiers.

Visit Synthesized

Which One Should You Pick?

Use Case	Our Recommendation
Data science team needs to train an ML model on production-representative data without touching real customer records	MOSTLY AI is the strongest fit: its published fidelity and privacy reports give a documented basis for the compliance sign-off that using synthetic training data usually requires.
Engineering team needs to safely populate dev, test, and staging databases from a HIPAA-covered production database	Tonic.ai's Structural product, with its expert determination and Safe Harbor de-identification paths, is purpose-built for exactly this workflow, provided the team budgets for the required expert statistical review.
ML team's model is underperforming because of class imbalance or missing data segments, not just insufficient data volume	YData Fabric's profiling-first workflow identifies the specific gap before generating synthetic data to fix it, rather than blindly multiplying a flawed dataset.
Small team or research group wants to prototype synthetic data generation without a subscription or sales call	SDV's Community edition is free, has no data volume cap, and runs entirely inside your own infrastructure, though it requires real Python engineering effort to validate the output.
Data governance team wants masking, subsetting, and synthesis rules defined once as auditable, version-controlled policy	Synthesized's "Data as Code" model fits this governance-first requirement better than the other four platforms, with the trade-off of a smaller team and less public track record.

How we evaluated

Synthetic data generation sits at the intersection of two goals that pull against each other: statistical fidelity high enough to be useful for testing or training, and privacy risk low enough to actually replace real data. This comparison weighs which platforms give buyers a defensible, documented answer to both questions, not which ones have the longest feature list.

Each platform was assessed on the criteria that decide real outcomes, the same dimensions you see in the comparison table above:

Best fit: the buyer profile and data problem each platform actually solves, whether that is ML training data, database de-identification, data quality repair, or governance-first provisioning, not the scenario its marketing targets.
Fidelity and privacy measurement: whether the platform publishes quantified fidelity, utility, and privacy metrics per job, or asks buyers to trust an unverified claim of "privacy-safe."
Compliance readiness: whether the workflow supports an actual regulatory determination, such as HIPAA Safe Harbor or Expert Determination, versus a generic privacy statement with no audit trail behind it.
Data type and workflow coverage: tabular, relational, time-series, or unstructured text, and whether the platform starts from a real production database (de-identification) or generates from a schema alone (synthesis from scratch).
Pricing model: how cost scales with data volume, rows generated, connected data sources, or per-user licensing, and whether list pricing exists at all.

What we reviewed

This comparison draws on official documentation and publicly posted pricing where available, and verified current company status for every platform given how much this market has consolidated: Gretel was acquired by NVIDIA in March 2025 and its standalone product discontinued, Hazy was acquired by SAS and folded into SAS Data Maker, and YData was acquired by KPMG in October 2025. It reflects the market as of 2026 and is refreshed as platforms ship, reprice, or get acquired.

Note

Editorial independence: this is a vendor-neutral comparison with no paid placements, sponsorships, or affiliate links. Rankings reflect fit for the stated use cases, not commercial relationships.

Frequently Asked Questions

What is synthetic data, and how is it different from anonymized real data?

Synthetic data is generated by a statistical or machine learning model trained to learn the patterns, distributions, and correlations in a real dataset, then produce entirely new records that share those statistical properties without corresponding to any real individual. Anonymized or de-identified data, by contrast, starts from real records and removes or masks identifying fields, but the underlying rows are still derived directly from real people. The distinction matters for risk: well-implemented synthetic data has no one-to-one mapping back to a real record, while de-identified real data can sometimes be re-identified if the masking was incomplete or the dataset is cross-referenced with other sources. Several platforms in this comparison, notably Tonic.ai, blend both approaches: de-identifying real data and generating fully synthetic replacement values within the same pipeline.

Does synthetic data actually satisfy GDPR or HIPAA compliance requirements?

It depends entirely on implementation, and no platform's marketing claim substitutes for that analysis. Under GDPR, data that is truly anonymous, meaning re-identification is not reasonably possible by any party, falls outside the regulation's scope, but that is a high bar; synthetic data generated from a small or unusual real dataset can still carry re-identification risk if outlier records are reproduced too faithfully. Under HIPAA, de-identification has two recognized paths: Safe Harbor (removing 18 specified identifier categories) and Expert Determination (a qualified statistician certifies re-identification risk is very small for the specific dataset and context). Tonic.ai explicitly supports both HIPAA paths as a workflow, but the platform automates the mechanical work; the actual legal determination still requires a qualified expert's sign-off, not just software output. MOSTLY AI and similar tools publish privacy risk scores that inform this analysis but do not themselves constitute a regulatory certification. Treat every synthetic data platform as a tool that supports a compliance determination your own privacy or legal team still has to make, not a compliance guarantee purchased off the shelf.

What happened to Gretel? Is it still available?

NVIDIA acquired Gretel in March 2025 for a reported nine-figure sum. As of mid-2026, Gretel's standalone self-serve product has been discontinued: gretel.ai redirects to NVIDIA's site and the old pricing page no longer resolves. Gretel's underlying technology was not abandoned, though; it was absorbed into NVIDIA NeMo as the Data Designer and Safe Synthesizer microservices, available through NVIDIA AI Enterprise rather than as an independent product. Teams that previously relied on Gretel's self-serve platform need to migrate to one of the still-independent platforms covered here, or evaluate NVIDIA NeMo directly if already invested in NVIDIA's AI Enterprise stack.

Is open-source synthetic data generation, like SDV, good enough for production use?

For teams with real data engineering capacity, yes, with caveats. SDV's Community edition is used widely (millions of downloads) and covers tabular, relational, and time-series generation with a range of modeling algorithms. The gap versus commercial platforms is not generation quality so much as the surrounding workflow: SDV's free tier does not ship built-in fidelity or privacy scoring, formal differential privacy, or managed hosting, all of which a team has to build or pay for separately (SDV's paid Enterprise tier, or a commercial platform like MOSTLY AI). Also check the license: SDV Community is released under the Business Source License, which restricts certain commercial uses, not a fully permissive open-source license, so legal review is warranted before embedding it in a commercial product.

How much does synthetic data generation actually cost?

It varies by pricing model more than by vendor quality. Credit-based platforms like MOSTLY AI charge $3-$5 per credit, with each credit covering roughly 1-10 million data points, which rewards periodic batch generation over continuous regeneration. Usage-based platforms like Tonic Fabricate start at $0-$29/month plus per-generation fees, while Tonic Structural's per-database de-identification pricing runs from a few thousand to tens of thousands of dollars annually per connected source. Open-source SDV is free at the Community tier and $500/month per user at Enterprise. YData and Synthesized publish no self-serve pricing at all and require a sales conversation. The real cost driver is usually not the platform license but the engineering time to validate the synthetic output is actually fit for purpose, which every platform on this list requires in some form.

Can synthetic data be used to train production AI or ML models, or is it only good for testing?

Both, but the bar for each is different. For dev, test, and QA environments, synthetic data mainly needs to be structurally realistic (correct data types, referential integrity, believable value ranges), which every platform in this comparison handles well. For training a production ML model, the synthetic data additionally needs to preserve the statistical relationships the model is meant to learn, correlations, rare-event frequencies, class balance, without simply memorizing and reproducing real training examples (which would defeat the privacy purpose and risk overfitting). MOSTLY AI and YData are the two platforms here most explicitly built for that higher bar, with fidelity and utility reporting designed to validate whether a model trained on synthetic data actually performs comparably to one trained on real data before it goes to production.

About the author

Deepak Gupta is the founder and creator of LoginRadius, a customer identity platform he built and scaled to over a billion users. He is now the founder of GrackerAI, a GEO platform for B2B SaaS and cybersecurity teams, and has spent more than 15 years building identity and security products.

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