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Synthetic Data Statistics: 2026 Report

By Darin ten Bruggencate, Founder, AI Industry Reviews · September 18, 2026

AI models learn from examples, and collecting real examples and having people label them is slow and expensive. Synthetic data is made by a computer instead, for example by a simulator that renders images with their labels already attached. But it has caught on much faster for images than for other kinds of data. So we looked at where it is used, what it costs and how it affects accuracy.

Our analysts surveyed 412 organizations running machine learning in production, 61 percent of which had at least one model serving live traffic, spanning financial services, healthcare, retail, industrial, and software. Alongside the survey, we priced synthetic generation against collected and labeled data across seven modalities and tracked accuracy across 1,140 training runs.

Synthetic Data Adoption by Use Case

In the table below, we report the share of surveyed organizations using synthetic data for each purpose, the median share of the data in that workflow that is synthetic, and the driver respondents ranked first.

The Synthetic Data Adoption by Use Case, 2026

Insights

• We found that 63 percent of organizations using synthetic data use it for at least two of the purposes above, and that the ones doing so report better outcomes than single-purpose adopters.
• The highest synthetic shares are in computer vision and agent evaluation, where collecting the real equivalent is physically expensive or impossible to arrange on demand.
• Privacy compliance drives the highest-adoption uses.

Synthetic Share of Training Data by Modality, 2021 to 2028

Forecasts that a majority of all training data would be synthetic by the middle of the decade have been repeated widely, and they conflate three modalities that have moved at very different speeds. In the table below, we separate them, then plot the same series.

The Synthetic Share of Training Data by Modality, 2026

Computer vision crossed the halfway mark in 2025 and the other two modalities have not, for mechanical reasons. A rendered scene carries its own labels, so a simulator produces perfectly annotated data at the marginal cost of compute, and the gap between rendered and photographed imagery has narrowed to the point where models trained on the former transfer reliably. Tabular generation is constrained by the difficulty of preserving the joint distribution across correlated columns, and language generation is constrained by the fact that the generator and the consumer are increasingly the same class of model, which is the condition under which quality degrades across generations.

Synthetic Generation Cost Against Human Labeling

In the table below, we price 1,000 usable records of each type, generated synthetically and collected and labeled by people, and report the time to generate a million records at the same quality bar.

The Synthetic Generation Cost Against Human Labeling, 2026

Generation cost per record falls to near zero, and the work moves to validation: deciding whether a generated distribution matches the real one closely enough to train on, and proving it to whoever signs off on the model. In our sample, validation consumed 44 percent of the total hours on a synthetic data project against 11 percent on a human labeling project, which closes roughly a third of the headline saving. The saving that survives is still large, and it is largest in the categories where human labeling is slowest, which is why segmentation and medical imaging show the highest adoption of generation among teams that also report the strictest review processes.

Model Performance by Training Data Mix

In the table below, we report what happened to model accuracy across 1,140 training runs in our sample, indexed against an identical model trained only on real data, along with the number of successive generations of self-training before we measured a statistically significant degradation.

The Model Performance by Training Data Mix, 2026

Insights

• We found the optimum at roughly a quarter synthetic, where the generated examples add coverage of cases the real set underrepresents without displacing the distribution the model needs to learn.
• Accuracy fell below the real-only baseline once synthetic content passed 60 percent of the training set, and the decline steepened sharply after that point.
• Keeping half the training set real delayed degradation from four generations of self-training to nine.

Synthetic Data Market Size and Validation Practice

In the table below, we set market size against the share of buyers who could describe a documented process for validating that generated data matches the distribution it is standing in for.

The Synthetic Data Market Size and Validation Practice, 2026

The market is growing at better than 30 percent a year, and three quarters of buyers cannot describe how they check the product. The failures are quiet. A generated dataset that is statistically plausible but misses a tail the real data contained produces a model that performs well in evaluation and badly on the cases that matter, and the gap appears only in production. Teams in our sample took a median of eleven days to detect a degradation traceable to generated training data, against two days for one traceable to a data-processing error, because the former looks like ordinary drift until someone checks the source. Providers that sell validation alongside generation are a minority today.

What Synthetic Data Trends Mean for 2027 and Beyond

Companies use computer-made data mainly to protect privacy. It helps train and test AI, but models get worse when too much of their training data is synthetic, so real data still matters.

In our runs the best accuracy came from a mix of synthetic and real data, so the mix decides how well the model works.

So if your team uses synthetic data, keep a solid share of real data in every training set and check results against a model trained on real data alone.

Requesting a Copy of This Report

If you would like a PDF copy of this report, or the survey instrument and run-level data behind it, you can reach out here.