Best AI Synthetic Data Tools 2026: Gretel.ai vs MOSTLY AI vs Tonic.ai vs Hazy

Gartner predicted in 2022 that 60% of the data used to build AI and analytics models would be synthetic by 2024, up from just 1% in 2021. By 2026 the firm raised the bar again: 75% of enterprises will use generative AI to produce synthetic customer or training data. That shift is not academic. Real customer records are now blocked by GDPR, HIPAA, and 144 national privacy laws, while the cost of labeling rare-event data has exploded. That is why a new class of ai synthetic data tools 2026 has moved from lab curiosity to a line item in the data platform budget.

This guide skips the vendor brochures. We compare four platforms that actually ship in 2026 — Gretel.ai, MOSTLY AI, Tonic.ai, and Hazy — and show the ROI math that decides whether they save you money or just add a new subscription you will regret.

What the best ai synthetic data tools 2026 actually replace

Synthetic data is artificially generated information that statistically mirrors your real records without containing a single actual person or transaction. Unlike anonymization, which still carries re-identification risk, a good generator learns the distributions of your source and prints new rows from scratch. The synthetic data generation tools on the market today target three distinct jobs, and confusing them is the most common way teams waste budget.

The first job is test data for engineering. Developers need realistic database clones for staging and QA without touching production PII, and manual de-identification is slow and legally uncertain. The second is training sets for models. Teams turn to synthetic data for machine learning when real labeled data is too sparse, too sensitive, or too expensive to collect at the volume a model demands. The third is privacy-safe sharing: an analyst hands a partner or regulator a synthetic twin of a dataset instead of the original, keeping the real records behind the firewall.

The underlying methods matter when you pick a vendor. Generative adversarial networks (GANs) train a generator and a discriminator against each other until the synthetic output is statistically indistinguishable from the source — strong for images and sensor data. Variational autoencoders compress real data into a latent space and decode new variants, which suits tabular finance records. Agent-based simulation models how synthetic customers or patients behave under rules, which is how you manufacture the rare fraud or equipment-failure cases real datasets almost never contain. Large language model augmentation generates realistic text — support transcripts, clinical notes, contracts — for NLP classifiers. For tabular and text workloads the leaders now ship measurable fidelity and privacy scores so a compliance officer can sign off instead of guessing.

If your goal is ai training data generation at scale, the gap between a $0 open-source library and a $10,000-per-month enterprise contract is not the math. It is referential integrity across tables, an audit trail a regulator will accept, and support when a pipeline breaks at 2 a.m. Knowing which you need is most of the buying decision.

The data wall and the privacy wall

Most AI teams do not have a data problem. They have two walls, and they sit on opposite sides of each other. The first is the data wall: high-quality human-generated training data is running out, and the rare events models need most — fraud patterns, rare diseases, unusual driving conditions — are exactly what real datasets have least. McKinsey's 2025 State of AI report found that data quality and availability are the top two barriers to AI scaling for 57% of enterprises, ahead of talent, budget, and infrastructure.

The second is the privacy wall. You legally cannot move the records that would solve the first problem. GDPR in Europe, HIPAA in healthcare, CCPA in California, and a growing patchwork of sector rules restrict how personal data can be stored, shared, and used for training. Compliance teams routinely block AI projects that would otherwise return real value because the training-data requirement cannot be met without privacy exposure.

The ROI case is concrete enough to put in a spreadsheet. IBM research found synthetic data can improve ML model accuracy by up to 20% in scenarios with limited real data. Industry surveys put the average reduction in data acquisition and labeling cost at around 40%, and report up to 30% shorter model development cycles. For a team spending $50,000 a year on annotation vendors, that is $20,000 back and two months of calendar time recovered. The synthetic data vs real data debate stops being philosophical the moment a compliance freeze has already stalled your roadmap for two quarters.

The catch vendors rarely print: synthetic data is a supplement, not a replacement. Train a model purely on its own synthetic outputs and you hit model collapse, where diversity and rare tails erode generation after generation — a failure formalized by Shumailov et al. in 2024. The evidence-based fix is to mix: keep fresh real data in every training cycle and use synthetic data to fill gaps and stress-test edge cases. That single discipline is the difference between a useful twin and a confident, useless one.

Gretel.ai: the developer-API engine

Gretel was acquired by NVIDIA in March 2025, and the engineering posture shows it. This is the most developer-first option in the field: a polished Python SDK, a REST API, and a Tabular LLM that generates complex datasets from natural-language prompts. If your team lives in CI/CD, Gretel slots in as a data-on-demand service — hit the API, get a Parquet or JSON export of safe data, and skip the legal review every time a new test dataset is needed.

On synthetic data pricing, Gretel is the rare vendor that publishes numbers on the marketing site. The free Developer tier gives 15 credits a month, enough for roughly 100,000 high-quality records, with PII detection across up to 2 million records. The paid Team tier starts at $295 per month and includes 1 million synthetic records plus SDK access; higher tiers run $3,500 and $10,000 monthly for enterprise on-prem, SOC 2, and a dedicated success engineer. That transparency matters in a category where most competitors hide behind "contact sales" until late in the cycle.

Strengths are real: best-in-class tabular fidelity, a BigQuery integration that returns a privacy-preserving DataFrame in place, SOC 2 / HIPAA / GDPR / CCPA certifications, and privacy routers that scrub PII from LLM prompts in real time. A known gotcha is that the Navigator generative features are credit-heavy and can consume half a monthly budget on a single complex schema. It is also strictly a generation engine, not a labeling UI — if you need humans drawing bounding boxes, this is the wrong tool. For the gretel ai vs mostly ai question, Gretel wins on developer experience and published pricing; MOSTLY AI wins on relational depth and EU residency, covered next.

MOSTLY AI: privacy-first and EU-resident

MOSTLY AI has built dedicated synthetic data since 2017, based in Austria with GDPR-EU data residency — a hard requirement for European buyers who do not trust US vendors under Schrems II. Its core SDK was open-sourced under Apache 2.0 in late 2024, so you can inspect and self-host the generation logic instead of trusting a black box.

On mostly ai pricing, the self-serve Marketplace tier runs about $3,000 per month, with a free trial of 100,000 synthetic rows and a limited daily-credit free tier (roughly 2 credits per day). Enterprise contracts climb toward $12,000 monthly for self-hosted, multi-region deployments with SLAs and SSO. That positions it as a premium, enterprise-focused product rather than a solo-developer toy.

The proof points are unusually specific. Swiss Post reported unlocking 89% more customer data by replacing restricted access with a synthetic twin. Erste Group uses synthetic data in every non-production Databricks environment because real data is barred from testing. MOSTLY AI also won a U.S. Department of Homeland Security contract for privacy-enhancing synthetic data — rare third-party validation in this category.

Strengths: the most mature relational synthesis available, with published fidelity, utility, and privacy metrics on every job that read closer to an audit trail than a dashboard, plus referential integrity across multi-table datasets. Weaknesses: credit pricing punishes high-frequency regeneration on every CI run, and the platform is narrowly focused on structured, tabular, and time-series data rather than images or video.

Tonic.ai and Hazy: enterprise database cloning

Tonic.ai is the enterprise heavyweight for de-identifying and cloning production databases. It masks, subsets, and synthesizes relational systems with near-perfect referential integrity, which is why healthcare and finance teams use it to build safe dev and test environments without copying patient or customer records. Pricing is custom enterprise only — contracts typically land between $15,000 and $30,000 per year for small setups and exceed $100,000 for large deployments, with a free 14-day trial up to 10GB of sample data.

Hazy, now part of SAS, is the UK-based option historically strong in financial services. Its enterprise tier runs in the high four figures per month. Both tools are overkill for a startup prototyping a single model, but they are the right call when you must mirror a multi-terabyte legacy database without exposing it — the use case where a generic generator simply cannot preserve the relationships across hundreds of tables.

The decision tree across these four is short. Solo developer or CI pipeline → Gretel. EU-regulated relational data → MOSTLY AI. Clone a massive production database with referential integrity → Tonic or Hazy. Want to own the pipeline for free → an open-source library. Privacy preserving synthetic data is the through-line in all four; the difference is who operates the engine and where the data sits.

Comparison table

The open-source row is not a footnote. Synthetic Data Vault (SDV) lets a team own the generation pipeline for free, which is often the right starting point before committing to a contract — and it is the same engine many paid platforms wrap.

Who should actually buy this (and who shouldn't)

The teams that get the most from ai synthetic data tools 2026 are not the ones building foundation models. They are teams stuck behind a compliance freeze, teams drowning in annotation bills, and teams that need rare-event data no one has collected. If any of those describe you, the payback is measured in weeks, not quarters.

Skip it if you already have abundant, clean, non-sensitive data and a small model. Buying best synthetic data software to generate data you already possess is pure overhead with no return. Also skip it if you cannot validate the output: synthetic data is only as good as the real data it learned from, so a garbage source produces a confident, useless twin that will quietly degrade your model.

For practical adoption, start with synthetic data python libraries like SDV or MOSTLY AI's open-source SDK. Generate a small twin, run your model, compare the metrics, and only then talk to a sales team. That sequence de-risks the purchase and gives you a benchmark to negotiate against — you walk into the demo knowing what your free run already achieved.

Getting started without blowing the budget

You do not need a six-figure contract on day one. Install SDV or the MOSTLY AI SDK, point it at a sample CSV, and generate 10,000 rows locally in minutes. Validate referential integrity and a privacy score, then expand the volume. Use the free tiers from Gretel and MOSTLY AI to prototype before committing a dollar.

The trigger to pay is usually one of three things: you need audit-grade privacy certification a regulator will accept, you need to clone a relational database with referential integrity across hundreds of tables, or legal requires the data to stay in a specific jurisdiction. Until one of those is true, the free path covers most experimentation and almost all evaluation. Pay for the constraint, not the capability.

Frequently Asked Questions

Is synthetic data as good as real data for training models?

In many cases it is close, and in sparse-data cases it is better. IBM research shows up to 20% accuracy gains when real data is limited. The synthetic data vs real data question is best framed as a mix: use real data for the core distribution and synthetic data to fill gaps, balance classes, and stress-test edge cases. Train only on synthetic output and you risk model collapse, where diversity erodes generation after generation.

How much do synthetic data tools cost?

Synthetic data pricing spans from $0 to six figures a year. Open-source libraries like SDV are free. Gretel's Team tier starts at $295 per month. MOSTLY AI's Marketplace tier is about $3,000 per month. Tonic.ai and Hazy quote custom enterprise contracts from $15,000 annually upward. Most teams should prototype on free tiers and only pay when a hard constraint appears.

Can I generate synthetic data with Python for free?

Yes. The synthetic data python ecosystem is mature: Synthetic Data Vault (SDV), MOSTLY AI's open-source SDK, and Faker all run locally at no cost. They handle tabular, relational, and basic text generation. You trade away audit-grade privacy certification and enterprise support, but for prototyping and research they are enough to validate the approach before buying a contract.

Which is better, Gretel or MOSTLY AI?

The gretel ai vs mostly ai choice depends on your stack. Gretel is the better fit for developer-led teams that want an API, SDK, and CI/CD integration, plus published $295/month pricing. MOSTLY AI is stronger for European, relational, compliance-heavy workloads that require EU data residency and per-job privacy metrics. Neither is universally better; pick by jurisdiction and data shape.

Bottom line

Synthetic data is no longer a research toy. With Gartner forecasting three-quarters of enterprises using it by 2026, the platforms above have matured from experiments into procurement decisions with real contracts behind them. If your roadmap depends on sensitive records you cannot move, ai synthetic data tools 2026 are no longer optional — they are the fastest legal way to unblock model development. Start free, validate against real metrics, and pay only when the audit trail or the jurisdiction demands it. For related reading on working with enterprise data, see our breakdown of the best AI data analysis tools and the top AI API platforms that consume this data in production.