Tabular synthetic data
Tabular synthetic data is generated rows that copy the statistical shape of a real table without copying its records. Five vendors on our list sell it: Tonic.ai, Syntho (with MOSTLY AI), Aindo, K2view and SAS Data Maker. None published prices when we checked.
Every vendor page cited here was last checked on .
Who sells tabular synthetic data?
| Vendor | What it makes | Delivery | Published price | Privacy claim on its pages |
|---|---|---|---|---|
| Tonic.ai | Three products: Structural (databases and other structured data), Textual (free text and files) and Fabricate (new data from a schema or prompt) | Software platform | Not published | Not published on the pages we read |
| Syntho (and MOSTLY AI, powered by Syntho) | Synthetic tabular data platform based in Amsterdam; now also runs the MOSTLY AI brand | Software platform | Not published | Not published on the pages we read |
| Aindo | Synthetic data platform from Trieste, Italy, aimed at regulated sectors | Software platform | Not published | Lists a Europrivacy certification (a GDPR certification scheme) on its security page |
| K2view | Data management vendor whose synthetic data tool mixes AI generation, business rules, cloning and masking | Software platform | No prices on the page; offers a "Start Free" option | Not published on the pages we read |
| SAS Data Maker | SAS's synthetic data product, which took in Hazy's software | Software platform | Not published | Not published on the pages we read |
Some vendors in this table may pay an introduction fee when a team we put in touch signs with them. Whether a vendor pays has no effect on the list, its order or our notes.
This site is independent. It is not affiliated with, sponsored by or endorsed by any company named on it, and it has not tested or audited any of their products: everything here comes from what each company publishes.
How is tabular synthetic data made?
A generator is trained on the source table, learns how values are distributed and how columns move together, then samples new rows. The model can be statistical (a Bayesian network or copula), a neural network, or a mix with hand-written business rules on top. Rules matter more than most demos show: an order date must come before its ship date, and a ZIP code must belong to its state.
Three things set vendors apart in practice:
- Relational support. Generating one flat table is easy. Keeping referential integrity across ten linked tables is the hard part.
- Where it runs. Software inside your own cloud account keeps source data at home; a hosted service means the source data travels.
- What comes with each dataset. A fidelity and privacy report per run saves you building one.
What is tabular synthetic data used for?
The common jobs are test data for software (realistic rows without production records in a staging database), sharing data with partners or other teams, analytics where the raw data is locked down, and topping up thin classes for model training.
The last one needs the most care. A model trained on generated rows can look strong on generated test rows and fail on real ones. Always score it on real data the generator never saw; our writeup on testing synthetic data quality sets out the checks.
When masking is the better buy
If the goal is a staging database that behaves like production, masked and subsetted production data is often enough, and cheaper. Synthetic generation earns its cost when the data must leave the team, when you need more rows than you have, or when you need a privacy claim you can defend.
What should a tabular trial include?
- Two or three linked tables, not one flat extract.
- A column with rare categories and one with long-tailed numbers.
- A model or query you already run, to compare answers on real and synthetic data.
- A privacy test: distance from each generated row to its closest real row.
- The vendor's per-run report, checked against your own numbers.
Where these facts come from
- Tonic.ai press release on Fabricate, read 7 Nov 2026
- Syntho announcement on the MOSTLY AI brand, read 7 Nov 2026
- Aindo security and compliance page, read 7 Nov 2026
- K2view synthetic data generation tools page, read 7 Nov 2026
- SAS press release on Hazy, read 7 Nov 2026
Tabular synthetic data questions
What is tabular synthetic data?
Rows and columns generated by a model trained on a real table or database. The generated table keeps the column types, value ranges and relationships between columns, but the rows are new.
Is masking the same as synthetic data?
No. Masking keeps each real row and hides or swaps sensitive values in it. Synthetic generation creates new rows. Several vendors, including K2view and Tonic.ai, offer both, and masking is often enough for a test database.
Can a generator keep foreign keys between tables?
Multi-table generation is a feature to test, not assume. Give the vendor two or three linked tables in the trial and check that every key in a child table still points to a parent row, and that counts per parent look real.
Which tabular vendor publishes prices?
None of the five on our list printed prices on the pages we read on 7 Nov 2026. K2view offers a free start.
Can it run inside our own cloud account?
Ask each vendor directly. Deployment options are rarely spelled out on public pages, and they decide whether your source tables ever leave your environment.
Get a quote
Three parts: the company location, a contact for replies, then what the generated data has to do. The final part is optional.
Disclosure: this is an introduction service, and any vendor it introduces you to can pay the site for that introduction. Each brief is read by a person and matched against vendors' published terms; you get the names first, and nothing goes to any vendor without your go-ahead.
Please do not send data, samples, schemas with real values or credentials through this form. A written description is all we need.
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- We email you the names of the ones that fit, and why.
- A vendor hears about your project only if you tell us to go ahead.