A buyer's guide to synthetic data platforms

Synthetic data has gone from research topic to procurement line item, and the market is now crowded with tools that all promise "realistic, private data." They are not equivalent. Here's a practical checklist for cutting through the demos — the questions that separate a platform you can build a program on from one that handles a toy dataset.

1. Does it handle your data, not a demo's?

Tabular data is table stakes. Ask about the hard parts: multiple related tables with foreign keys, time-series and sequences, mixed types, high-cardinality categoricals, and skewed distributions. If the tool only shines on a single clean table, it will struggle with your actual schema.

2. Can it prove fidelity — with your metric?

Everyone shows a nice distribution overlay. Push further: can it run train-synthetic, test-real on your downstream task and show the model performs? Fidelity you can't tie to a real task is decoration.

3. Can it prove privacy — quantitatively?

"It's synthetic, so it's private" is not an answer. Ask for membership-inference results and distance-to-closest-record distributions on every run. A serious platform treats privacy as a measured output, not a marketing claim. (More on this in how to measure synthetic data.)

4. Where does it run?

Your most sensitive data usually can't leave your environment. Can the platform run in your cloud or VPC, or is it SaaS-only? Who sees the source data during training? Deployment flexibility is often the deciding factor for regulated buyers.

5. Does it fit your workflow?

A tool that lives in a separate UI gets used once. Look for a real API and SDK, connectors to your data sources, and the ability to run generation as a pipeline step (see synthetic data in your dev pipeline). Integration is what turns a purchase into a program.

6. What's the total cost — including your time?

License price is the visible number. The real cost includes the engineering time to onboard each dataset, tune generation, and validate output. Ask how long a typical first dataset takes to go from raw to trusted. Weeks of professional-services glue can dwarf the license.

7. Is there a human when it's hard?

Your first few datasets will be messy. A vendor who pairs the platform with people who can help you land the first use case — and hand the capability back to your team — is worth more than one who ships a login and wishes you luck.

A short scorecard

Score each platform 1–5 on: data-type coverage, provable fidelity, provable privacy, deployment fit, workflow integration, time-to-first-trusted-dataset, and support. The winner usually isn't the flashiest demo — it's the one that will still be delivering value on your tenth dataset, not just your first.

Frequently asked questions

What should I ask a synthetic data vendor first?

Whether it handles your data and not a demo's: multiple related tables with foreign keys, time-series, mixed types, high-cardinality categoricals and skewed distributions. A tool that only shines on a single clean table will struggle with your real schema.

How can a platform prove the data is private?

With measured results on every run, not a claim. Ask for membership-inference results and distance-to-closest-record distributions. "It's synthetic, so it's private" is not an answer.

How do I prove the synthetic data is useful?

Run train-synthetic, test-real on your downstream task: train a model on the synthetic data, evaluate it on held-out real data, and compare it with a model trained on real data. Fidelity you can't tie to a real task is decoration.

What does a synthetic data platform really cost?

License price is only the visible number. The real cost includes the engineering time to onboard each dataset, tune generation and validate output, so ask how long a typical first dataset takes to go from raw to trusted.