Synthetic data tools are not one market — they span several distinct categories that solve different problems. They divide along three axes: the generation approach (rule-based, model-based, and agentic), the relationship to your real data (generated from scratch or seeded from an existing dataset), and how they're packaged (open-source libraries, hosted generators, and enterprise platforms). The most common point of confusion is the line between generating synthetic data and de-identifying real data — a related but fundamentally different category.
How synthetic data tools are categorized
The synthetic data tools landscape makes the most sense when you stop treating it as a single ranked list and read it along a few axes instead. Tools in this space get lumped under one label, but they solve genuinely different problems, and a tool that's ideal for one job can be a poor fit for another. Grouped by category rather than by which is "best," the real differences between them become legible.
Synthetic data is data produced by an algorithm or model rather than collected from real-world events, and it's used wherever real data is scarce, sensitive, or expensive to obtain — training and testing machine learning models, populating development environments, exercising software before real data exists. Tools differ, too, by the shape of data they handle: whether a tool produces tabular, time-series, or unstructured data matters as much as how it generates it, since a generator built for database tables rarely handles free text well.
Three axes account for most of the meaningful differences between tools:
- Generation approach — how the data is produced: rule-based, model-based, or agentic.
- Relationship to real data — where it starts: generated from scratch, or seeded from an existing dataset.
- Packaging and delivery — how it's shipped: an open-source library, a hosted generator, or an enterprise platform.
One distinction causes more buyer confusion than any other: generating synthetic data is a different category from de-identifying real data. The first invents new records; the second transforms records you already hold. They interoperate, and some vendors do both, but they are not the same thing — and knowing which one you need is often the first real decision.
Generation approaches: rule-based, model-based, and agentic
The generation approach most determines the realism you can expect and the effort you'll spend getting it. Three families sit on a rough spectrum from "you specify everything" to "you describe what you want."
Rule-based and statistical generation
Rule-based generation builds data from explicit configuration: you define the fields, the value constraints, and the distributions to sample from, and the tool produces records that satisfy them. It's predictable, fast, and fully under your control, which makes it a solid fit when you know exactly what a valid record looks like. Its limit is realism — capturing the messy correlations and cross-table relationships of real data by hand is difficult, so complex datasets tend to come out flatter than the real thing.
Model-based generation
Model-based generation learns patterns from an existing dataset instead of having you specify them. This family includes GANs (generative adversarial networks, where two neural networks compete — one generating samples, the other trying to distinguish them from real data — until the output is convincing) and VAEs (variational autoencoders, which compress data into a learned representation and sample new points from it), along with LLM-driven approaches. Because these models learn the distribution rather than approximating it by hand, they can reach higher fidelity on complex data — at the cost of setup, compute, tuning, and a representative dataset to learn from.
Agentic generation
Agentic generation is the modern, state-of-the-art approach: you describe the dataset you want in natural language, and an agent plans the schema, generates the data, and refines it. Tonic Fabricate is the exemplar — you request data in plain language and it produces referentially intact records, with a Data Agent building the dataset and a Validation Agent checking and correcting it, so a rough or underspecified prompt still yields usable output. For the mechanics beneath each family, see how synthetic data is generated.
Agentic generation produces genuinely usable output, not just plausible-looking rows. In a Tonic.ai benchmark published in March 2026, an open-source model fine-tuned only on Fabricate-generated synthetic data improved on the real-world Enron email benchmark from 80.5% to 86% — outperforming o3 and gpt-4.1-mini — without training on a single real email (reproducible on Hugging Face).
The Tonic Advantage: generation as a conversation. The older approaches share a hidden tax — the hours of configuration, scripting, and tuning before a single usable record comes out. Fabricate collapses that into a request: you describe the dataset you want in plain language and its agents plan, generate, and validate it, turning a scripting project into a conversation. Its MCP server extends that into the tools developers already use, so data can be requested and pulled back from a client like Claude, Cursor, or VS Code without leaving the editor.
Generate from scratch vs. seed from real data
The second axis is where the data starts, and it drives both fidelity and privacy posture more than any other single choice. A tool either generates from scratch — inventing records from a specification with no underlying dataset — or seeds from real data, reading an existing source to model new records on it.
From-scratch generation has no production-data dependency, which is what you want in three situations: a greenfield project where no data exists yet, a cold-start model for a domain you've never logged, or an environment where production access is locked down for compliance reasons. Because nothing real is involved, there's no sensitive data to leak and no access approval to wait on — but the realism ceiling depends entirely on how well you can specify what you need.
Seeding from real data takes the opposite trade. The tool connects to an existing database, reads its schema and its values — the column relationships, value frequencies, and cross-table structure — and generates new records that reproduce that statistical shape. The payoff is fidelity: the output behaves like the source because it was modeled on it. The cost is that you need access to representative real data, which reintroduces the privacy questions that from-scratch generation sidesteps.
Historically, this axis forced a tool choice. A generator built for from-scratch mock data couldn't model your production distributions, and one built to learn from a real dataset couldn't help before that data existed — so teams picked a tool for the situation they were in and switched when it changed. Tonic Fabricate collapses that choice: it generates synthetic data from an existing database through a live connection and generates from scratch from a prompt, in one tool. Doing both under one roof also lets the two work in tandem — net-new records generated from scratch can stay referentially intact with data modeled on your real database, so a single dataset combines invented entities and real-shaped ones without the foreign keys breaking between them.
Seeding from real data is not the same as transforming it. Seeding reads a source to learn its patterns and then generates new records; transformation edits the real records themselves. That second mechanism is a different category, and it's where the trade-offs between synthetic and real production data get sharpest.
How tools are packaged: libraries, hosted generators, and platforms
Packaging is the axis that most "synthetic data tools" lists actually conflate — grouping a code library and an enterprise platform as if they were interchangeable when they serve completely different users. Four delivery models dominate:
- Open-source libraries — code-level tools you install and script against. Faker generates realistic field values (names, addresses, dates) and SDV (the Synthetic Data Vault) fits statistical and deep-learning models to tabular data. Both are free and flexible for developers comfortable writing code, though relational integrity across many tables and governance features are generally something you build yourself.
- Hosted, form-based generators — web tools where you define a schema in a form and download sample data. Mockaroo is the representative example, well suited to quick test data at modest scale without writing code.
- Enterprise and commercial platforms — tools that add governance, scale, and compliance features on top of generation. NVIDIA NeMo Data Designer (formerly Gretel) and GenRocket sit in this tier, aimed at organizations that need synthetic data as managed infrastructure rather than a one-off script.
- Developer-workflow and agentic tools — generation that lives inside the developer's environment and responds to plain-language requests. Tonic Fabricate is the example here, with an MCP server that lets developers generate data from an MCP-compatible client without leaving their editor.
Faker and Mockaroo each warrant their own head-to-head comparison with an agentic tool, since they're the categories developers most often weigh Fabricate against. The packaging tier you want tends to track your scale and governance needs: a library is often enough for a single developer generating mock data and synthetic APIs locally, while a platform earns its keep when synthetic data becomes shared infrastructure that many teams depend on and compliance has to sign off on.
The adjacent category people confuse it with: de-identification and masking
De-identification and masking are frequently filed under "synthetic data tools," but they belong to a fundamentally different category: they transform real data rather than synthesize new data. The mechanism is the distinction. A generator invents records that never described a real person; a de-identification tool takes real records and alters the sensitive parts — masking values, subsetting rows — so the result is safe to use without re-identification risk while remaining, at its core, derived from real data.
For structured production data, Tonic Structural is the exemplar of this category: it masks and subsets data from production databases while preserving referential integrity, so foreign-key relationships stay intact across every table it touches and the de-identified copy still behaves like the original. The output isn't invented — it's the real database, transformed to remove what's sensitive while keeping the data realistic enough to stand in for production in testing and development.
Unstructured data needs a different mechanism, because sensitive values are buried in free text rather than sitting in labeled columns. Tonic Textual handles this with proprietary NER models — named entity recognition, the task of locating and classifying spans of text such as names, dates, and account numbers — that detect sensitive entities and then either redact them or replace them with realistic synthetic substitutes. That synthesis step is the nuance worth holding onto: Textual can generate replacement values inside otherwise-real text, so the boundary between the two categories is real but permeable. The categories also interoperate — de-identified output can itself be seeded into a generator, letting a team make a sensitive dataset safe and then expand it with generation.
The Tonic Advantage: two categories, one product suite. Most teams need both mechanisms and buy them from separate vendors. The Tonic.ai product suite covers both: Textual de-identifies sensitive unstructured text, and Fabricate can then treat that de-identified output as a model to generate more of it. For a scarce, sensitive corpus — too risky to use raw, too small to be useful — that pairing makes the data safe and then scales it up, without the two steps living in disconnected tools.
Matching the approach to your constraints
Which category serves you follows from a handful of real constraints, not from which tool ranks highest: the type and structure of your data, how much fidelity you need, your privacy and compliance posture, the scale you're generating at, whether you can access production data, and how your team prefers to work. Rule-based tools reward you when the schema is simple and control matters most; model-based and agentic tools earn their added complexity when realism on complex, relational data is the priority.
The guidance is situational. With no data yet, you need from-scratch generation. With production data you can access, seeding from it buys fidelity — unless that data is sensitive, in which case de-identification comes first. A solo developer scripting local fixtures is well served by a library; synthetic data that's becoming shared infrastructure across many teams, under compliance oversight, calls for a platform. For the reader weighing software testing and QA against machine learning and AI use cases, the deciding factor shifts toward control over schema, coverage, and ground truth.
Whatever category you land in, evaluate tools on the quality of what they produce rather than a brand claim. The most reliable test is downstream: generate a dataset, use it for its intended purpose, and measure whether it holds up — which is what measuring synthetic data quality and fidelity is for.
| Approach | Data type fit | Fidelity | Production-data dependency | Typical user |
|---|---|---|---|---|
| Agentic (e.g., Tonic Fabricate) | Structured, unstructured, and mixed; multi-table | High; validated, referentially intact output | Optional — generates from scratch or seeds from a live database | Developers and AI engineers wanting fast, realistic data in their workflow |
| Rule-based / statistical | Best for simple structured data | Moderate; limited on complex relationships | None | Developers who know the schema and want predictable control |
| Model-based (GANs, VAEs) | Primarily structured/tabular | High on data it learned from | Required — needs a representative dataset | Data scientists modeling an existing dataset |
| De-identification / masking | Real structured or unstructured data | High when done well — preserves the utility that makes data usable in testing | Required — operates on real data | Teams holding sensitive data that must be made safe to use |