Synthetic data vs real company data for model training

Use real company data when the model must learn how people and processes actually behave, and use synthetic data to fill gaps, add rare cases and test safely. Synthetic data cannot teach what its generator does not already know, so most strong systems combine a core of real examples with targeted synthetic additions.

What is the difference between synthetic and real data?

Real data records events that happened, while synthetic data is generated by a process, such as a simulator, a rule set or another model. The difference is where the information comes from.

Real records contain the facts of a business as it ran: the customer who wrote three messages in one sentence, the invoice with a typo in the supplier name, the sensor that drifted for a week. Nobody designed that mess. It is evidence of how the world works, and a model trained on it inherits some of that evidence.

Synthetic data carries only what the generator knows or what you told it to include. That can be very useful, because you control the content, volume and labels. But it means a synthetic dataset can never contain a surprise the generator cannot produce.

When does synthetic data work well?

Synthetic data works well when you can describe the structure of what you need, when real examples are scarce or sensitive, and when the task rewards coverage more than realism. It is a tool for extending a dataset, not for inventing the underlying truth.

Good uses include:

• Rare scenarios, such as a payment failure sequence that occurs once in thousands of cases.
• Format and wording variations of examples you already have, to improve robustness.
• Test fixtures where you cannot use real personal data in a development environment.
• Tasks with verifiable answers, such as code or maths, where outputs can be checked automatically.
• Early prototypes, before you have access to real records.

The pattern in these cases is the same. A check outside the generator, whether a unit test, a human reviewer or a real holdout set, decides whether the synthetic examples are good.

Where does synthetic data fail?

Synthetic data fails when it is asked to stand in for the real distribution, because it tends to be too clean, too uniform and biased toward whatever the generating model already favours. The result looks plausible and teaches the wrong lessons.

Several failure modes recur:

• Reduced diversity. Generated text clusters around common phrasings, so rare but important cases drop out.
• Inherited errors. If a generating model makes a systematic mistake, every example repeats it, and training reinforces the mistake.
• Feedback loops. Training repeatedly on model outputs can narrow a model's range over generations. Researchers describe this as model collapse.
• False confidence. Metrics measured on synthetic test sets can look excellent while real-world performance disappoints.
• Missing context. Real tickets, contracts and logs include references to earlier events, internal jargon and half-finished work that a generator will not invent convincingly.

None of these mean synthetic data is bad. They mean it needs a real reference to be trusted.

What does real company data give you that synthetic cannot?

Real company data gives you ground truth about a specific domain: true distributions, real edge cases and the language of actual practitioners. For vertical AI, that is often the difference between a demo and a product.

Consider the data a company produces just by operating. Support conversations show how customers describe problems before they know the vocabulary. Transaction histories show the rare sequences that precede a failure. Project records show how plans differ from outcomes.

A simulator or a language model can imitate the surface of these, but not the underlying frequencies.

Real data also carries evaluation value. Even a small set of real examples serves as a test that synthetic training cannot fake. If you are deciding how much real data you need, our guide on sourcing domain data for vertical AI covers practical sizing and selection.

What are the downsides of real data?

Real data costs more to obtain, carries rights and privacy questions, and arrives with noise you must clean. It is also finite: you cannot generate more of a particular event on demand.

Rights are the first issue. You need to know who can license the data and for which uses, including model training. Personal information in the records raises privacy obligations that vary by jurisdiction, so de-identification and contractual limits are common. This is general information and not legal advice; a qualified lawyer should review your specific plan.

Second is fit. Company data is created for operations, not for training, so schemas are inconsistent, fields are missing and labels, if they exist, reflect business needs. Expect a cleaning and formatting phase before the data helps.

Third is access. Sellers often impose security limits, and a sample may not reveal gaps in the full set. Our article on requesting a data sample under NDA explains how to test before committing.

How do privacy and rights compare?

Synthetic data is not automatically free of privacy and rights issues, and real data is not automatically unusable. The answer depends on how each was produced.

A synthetic set generated from real records can leak information from those records, especially if the generator memorises rare examples. Calling it synthetic does not erase the obligations attached to the source data, so ask how it was created and what testing was done. A fully rule-based synthetic set built from a schema carries no such link, but it also carries no real-world signal.

Real data with direct identifiers removed may still be personal data under some laws if individuals could be re-identified. Treat de-identification as a risk reduction measure and verify it. A seller who can describe their redaction method, and show you a sample of the output, gives you much more to work with than one who simply says the data is anonymised.

How do you combine synthetic and real data?

The practical approach is a real core, a synthetic supplement and a real evaluation set that is never used for generation. This keeps the benefits of both and gives you an honest measure of progress.

1. Start with an evaluation set built from real examples, kept separate from training data.
2. Train or fine-tune on the real data you have, and record the baseline result.
3. Identify failure categories where real examples are too few.
4. Generate synthetic examples aimed at those categories, with a human or automated check on quality.
5. Add them in small batches and measure each addition against the real evaluation set.
6. Stop adding when the real metric stops improving, or when a category's gain comes at the cost of another.

A common mistake is to evaluate on data generated by the same method as the training data. That proves the model learned the generator's habits, not the task. Keep your test set real and independent.

What to check if you are offered synthetic data

Synthetic data is sold and shared like any other dataset, and the questions are different from those you would ask about real records. Ask the provider for answers to these before you commit:

• What generated it: rules, a simulator, or a language model, and which one?
• Was any real data used as a seed, and if so, under what rights?
• What testing shows it does not reproduce rare source records?
• How was quality checked, and by whom?
• Does it come with a real held-out set for evaluation, or will you need to build your own?
• Are there any limits on using it to train models you sell?

A provider who has thought about these will answer in specifics. If the answers are vague, the dataset is probably hard to trust and hard to defend later.

What does each type cost in time?

Real data costs time up front in sourcing, legal review and cleaning, then pays back in signal. Synthetic data costs little at the start and then costs time in review, because someone has to find the failures it hides. Neither is free, and the cheaper option on day one is not always cheaper by the time you have a model you trust.

How should you decide for your project?

Decide by asking what the model needs to learn and whether an independent check exists. Four signals point toward real data:

• The task depends on how people or machines behave in practice.
• Errors are costly, so you need evidence the model works on real inputs.
• The vocabulary is specialist, and a generator would only guess at it.
• You need to defend the model's training data to customers or auditors.

If the knowledge comes from real-world behaviour and you cannot verify synthetic examples against reality, you need real data.

Hypothetical example: a team building a model to classify freight exceptions might generate synthetic descriptions of delays to train a first classifier. It would still need real exception notes to learn how dispatchers actually abbreviate, mis-spell and omit details, and a real test set to confirm the classifier works on them.

If your answer leans toward real data, a marketplace can shorten the search. On LogitAtlas, companies list data they already hold, you can use the message thread to ask how it was collected, and payment is held in escrow until you confirm delivery. For a wider view of options, see where to buy AI training data.

Frequently asked questions

Is synthetic data cheaper than real data?

Per example, usually yes, because generation scales and avoids licensing. But total cost includes designing the generator, checking quality and fixing the failures it creates. For domain tasks, synthetic data that needs heavy review can cost more than expected, so compare the whole workflow, not the per-row price.

Can I train a model only on synthetic data?

You can, and for verifiable tasks like code or maths it is often effective. For tasks that depend on real-world behaviour, a purely synthetic approach risks narrow, overconfident models. At minimum, evaluate on real examples to find out whether performance holds outside the generator's patterns.

What is model collapse?

Model collapse is the loss of diversity and accuracy that can occur when models are trained repeatedly on the outputs of earlier models. Rare cases fade and the output narrows. Mixing in fresh real data and avoiding recursive generation without checks reduces the risk.

Does synthetic data solve privacy problems?

Not automatically. If it is generated from real personal data, it may reproduce parts of it, while fully schema-based synthetic data avoids that link but lacks real signal. Ask how it was produced and tested. This is general information, not legal advice; consult a qualified lawyer on specifics.

How much real data do I need to start?

Often less than people expect, if it is representative and well labelled. A modest real set can anchor evaluation and fine-tuning, with synthetic data extending coverage. Begin with a pilot, measure, and increase only where results show a gap.

If you need real operational data for a specific task, you can post a data request and describe what your model must learn.