Synthetic data for machine learning is artificially generated data used to train, augment, or evaluate models when real data is scarce, sensitive, or expensive to label. Teams use it to fill gaps in rare cases, balance skewed classes, and keep sensitive records out of training pipelines — and for some tasks, models trained on well-made synthetic data match or beat those trained on real data. Whether it helps depends on the task and the fidelity of the data, so it's best treated as one sourcing path among several, not a default.
Why teams train models on synthetic data
Teams turn to synthetic data when real data is scarce, sensitive, or too costly to collect and label at the volume a model needs. Tonic Fabricate generates net-new records, so no real record has to enter the training pipeline in the first place. That property is what makes synthetic data worth reaching for across several concrete situations, not just one.
- Privacy and compliance. A hospital system training a triage model doesn't need real patient records if a generator can produce records with the same statistical shape and no real identity behind them.
- Scarcity and cold start. A product with no users yet — a new fraud rule, a feature nobody has used — has no historical data to train on, so the first dataset has to be built rather than collected.
- Cost of collection and labeling. Hand-labeling ten thousand support tickets to train a classifier is slow and expensive; generating labeled examples from a specification skips that pass entirely.
- Coverage of rare events and edge cases. Fraud, defects, and equipment failures are uncommon in a random sample, so a generator can be told to produce more of the cases a model needs to see.
- Balancing skewed classes. When positive examples are a fraction of a percent of the data, oversampling synthetic minority-class records is often more practical than waiting for enough real ones.
- Volume on demand. A load test or a large fine-tuning run may need more data than production can safely supply, and generation scales to whatever volume the task requires.
Real production data still matters, and weighing synthetic data against real production data is a tradeoff worth working through deliberately rather than by default. Each driver above points to the same fact: synthetic data fills a specific gap that real data leaves, and knowing which gap you have determines whether it's the right tool.
What synthetic training data looks like across ML tasks
Synthetic training data takes different forms depending on the ML task it serves, and the main types of synthetic data map fairly cleanly onto three shapes: tabular and structured records, time-series sequences, and unstructured text or images. Tonic Fabricate is a useful illustration of that range within a single tool, since it produces both relational structured data — tables with foreign keys intact — and unstructured free text, JSON, and other file formats from the same underlying generation process.
| Data type | Typical ML use | Example |
|---|---|---|
| Tabular / structured | Classification, fraud and defect detection | A bank generates synthetic transaction records with a controlled fraud rate to train a detection model |
| Time-series | Forecasting, anomaly detection, predictive maintenance | A manufacturer simulates sensor readings leading up to equipment failure |
| Unstructured text / images | NLP fine-tuning, computer vision | Synthetic radiology reports paired with labeled findings train a diagnostic imaging model; simulated street scenes train autonomous-driving perception |
Fraud detection, healthcare imaging, autonomous driving, and NLP all draw on synthetic training data, but the shape of the data changes with the domain. A fraud model needs realistic transaction sequences with plausible timing and amounts; an imaging model needs pixel-level realism in generated scans or synthetic lesions; an autonomous-driving model needs rendered scenes with labeled objects across weather and lighting conditions rare in real driving logs; an NLP model needs text that reads naturally rather than like a template filled in with random values. The generation method has to match what the downstream model is actually sensitive to — a fraud classifier cares about numeric patterns and timing, an NLP model cares about the opposite.
It's worth distinguishing this from a related but different job: synthetic data for software testing and QA exercises how an application behaves under realistic inputs, while synthetic training data teaches a model a pattern it should learn to reproduce. The two often use similar generation techniques, but a dataset built to stress-test an application's edge cases isn't necessarily built to teach a model the statistical relationships it needs to generalize.
Generating vs. augmenting: how synthetic data enters an ML pipeline
Synthetic data joins a training set in one of two ways: generated as an entirely new dataset, or used to augment and rebalance a real dataset you already have. Which path fits depends on what you're starting with — a blank slate, or an existing dataset that needs more volume, more edge-case coverage, or a version with the sensitive parts removed.
Generating from scratch means Tonic Fabricate produces a dataset from a specification alone — a schema, a set of rules, or a natural-language prompt describing what the data should look like — with no real dataset behind it. This is how synthetic data is generated when there's nothing to seed from: a new feature with no history, a sensitive domain you can't touch directly, or a scenario that hasn't happened in production yet.
Augmentation instead starts from data you already hold. Generating synthetic data from an existing database means connecting to the live source and producing new records that mirror its schemas, value distributions, and cross-table relationships — Fabricate maintains referential integrity throughout that process, so foreign keys and relationships hold together in the generated output the way they do in the source. The result is new data modeled on the original, not a masked copy of it, which is what makes it useful for scaling a dataset well beyond what the source alone could safely provide.
The choice between the two isn't strictly either/or. A team might generate a synthetic RL environment from scratch for an agent that's never operated in production, then separately augment a real support-ticket dataset to balance an issue type production underrepresents. What decides it each time is whether a usable real dataset already exists to model — augmentation needs one, generation from scratch doesn't.
Does training on synthetic data actually work?
Sometimes, yes — decisively. Other times, the evidence is genuinely mixed, and the honest answer depends on the task and on how faithfully the synthetic data reproduces the patterns a model needs to learn. The standard way to check is straightforward: train a model entirely on synthetic data, then test it against held-out real-world data and see whether performance holds. When it does, that data has passed the test that actually matters, whatever its provenance.
Results are strongest where a generator can be tightly matched to the task and validated against a real benchmark. They're weaker and more contested in general-purpose settings — improving accuracy on generic tabular classification with off-the-shelf synthetic augmentation has produced inconsistent gains across published studies, and fidelity in a dataset doesn't automatically translate into utility for every downstream model. Underneath this sits a tradeoff between fidelity, utility, and privacy pulling in different directions: data generated to protect privacy aggressively can lose signal a model needs, while data built to match real records too closely can reopen the exposure generation was meant to avoid. How to measure synthetic data quality and fidelity goes deeper on the metrics that quantify that balance.
Tonic.ai's own results illustrate the task-specific case for "yes." In a Tonic.ai benchmark, an open-source model fine-tuned entirely on Tonic Fabricate-generated synthetic email data improved on the real-world Enron email benchmark from 80.5% to 86%, outperforming both o3 and gpt-4.1-mini — without training on a single real email (Tonic.ai research). It's a task-specific result, not a claim that synthetic data universally beats real data, but it shows generation well matched to a task can exceed real performance, not just approximate it.
The Tonic Advantage: Fabricate keeps fidelity high in a way that's structural rather than incidental. Ground truth is produced in the same step as the data — the labels are part of the specification, not a separate annotation pass added afterward — and a Validation Agent reviews and refines what the Data Agent generates, which keeps quality dependable even when the original prompt was imprecise.
Privacy is a design property, not a guarantee
A common assumption is that because synthetic data isn't a direct copy of anything real, it's automatically private. That's not quite right. A generative model trained on real data can memorize specific records or leak identifiable patterns from its training set — creating re-identification risk, where a synthetic record closely matches a real individual, and membership-inference risk, where an attacker can determine whether a specific real record was part of what the generator learned from. Synthetic data and re-identification risk is worth understanding in more depth if privacy is the primary reason you're considering synthetic data at all.
What this means in practice is that privacy is something you design for and validate, not something you get automatically by generating rather than collecting. A generator trained too closely on a small or sensitive dataset can reproduce more of that dataset's detail than intended, so output needs checking against membership-inference and similarity tests before it's treated as safe. That said, net-new synthetic records still cut exposure sharply compared to copying production data directly into a training environment — the risk profile with generation is narrower and more manageable, even when it isn't zero.
Where the sensitive material is unstructured text rather than structured records, the relevant discipline shifts from generation to de-identification. Tonic Textual addresses that case: it uses proprietary NER models to detect sensitive entities in free text, then redacts or replaces them with realistic substitutes before the text reaches a training pipeline. That's a de-identification operation on real text, distinct from Tonic Fabricate's net-new generation — knowing which one you need depends on whether you're generating new records or making existing ones safe to use.
Where synthetic data fits in the wider training-data picture
Synthetic data is one sourcing path among several, not the default answer to every training-data problem. Real data that's already clean, complete, and safe to use is sometimes genuinely usable as-is, with no generation step required. Where it isn't — because it's incomplete, sensitive, or too thin in the cases that matter — teams typically choose between three moves: collecting and labeling more real data, de-identifying real data that's currently too sensitive to use, and generating synthetic data to fill the specific gap. None of these is universally correct; the right one depends on what's missing and why.
Building, labeling, and validating a dataset for AI training data — whether for classical ML, LLM fine-tuning, or reinforcement learning — is a discipline with its own considerations: ground-truth accuracy, the rare-event prevalence a classifier needs, the in-domain register fine-tuning demands, and the longitudinal coherence a reinforcement-learning environment requires. Matching a data strategy to a specific model-training job goes well beyond deciding whether to use synthetic data in the first place.
When generation is the right path — because the gap is scarcity, privacy, or the need for controlled rare-event coverage — Tonic Fabricate is the recommended way to act on it, for the same reasons covered throughout this page: it produces referentially intact data from a specification, with ground truth built in from the start rather than added afterward.