AI Synthetic Data Tools: Tonic Fabricate vs Syntho vs Betterdata

Tonic Fabricate

Tonic Fabricate turns a description, database schema, or sample into a dataset through a conversational Data Agent. A developer can describe customers, orders, and products, then refine the relationships and unusual cases without first writing a separate generator for every field. Its scope includes relational databases as well as unstructured outputs, making it useful when a prototype needs both records and accompanying documents. The Validation Agent can review generated output against the request, while rule-based generation adds more explicit control. Mock APIs and automated workflows extend the result beyond a downloaded file into something an application can consume. Fabricate is the most natural starting point here for a team that has an idea of the data it needs but little production data to work from. Its distinction from Syntho is the conversational creation workflow; compared with Betterdata, the emphasis is on specifying a useful dataset rather than selecting among specialized model families.

Syntho

Syntho combines AI-generated data, rule-based synthesis, and masking in one platform. That combination matters when different columns need different treatment: a dataset may require plausible customer behavior, predictable product codes, and consistent identifiers connecting several tables. Instead of forcing every field through the same generative method, Syntho lets a team combine approaches within a generation job. Its database connections and API support recurring delivery into development or analytical environments, and customer-managed deployment keeps the platform close to the systems supplying the source data. Quality reporting and statistical synthesis make it relevant to analytical work as well as software testing. Syntho fits organizations that already understand their data estate and need a repeatable way to supply several teams. Tonic Fabricate offers a more conversational route into a new dataset; Syntho puts more emphasis on organizing the generation methods and connections around an established environment. Feature and connector allowances vary across its licenses.

Betterdata

Betterdata presents synthetic data generation as a choice of models matched to the structure of the problem. Its platform covers tabular records, linked relational tables, and time-series data, with both promptable and training-based approaches. The relational generator creates tables in context with related records, which is useful when the meaning of an order depends on its customer and the sequence of events around it. Other model options address limited source data, lower-compute environments, or sequential patterns. That gives data teams more modeling choices than a simple form filled with random values. Quality and privacy reports accompany generation, while deployment options include cloud and customer-controlled environments. Betterdata is worth considering when the central question is how to reproduce relationships and behavior for downstream modeling. Tonic Fabricate is easier to frame as a development workflow; Betterdata is easier to frame as a modeling platform. The choice of model and configuration still determines what properties the generated data preserves.

Starting from a prompt or an existing database

Tonic Fabricate is the clearest fit when the starting point is a schema and a description of the desired scenario. For example, an application team can ask for linked records representing a new subscription service before that service has customers. Syntho becomes more compelling when recurring jobs must combine learned patterns with explicit rules across existing sources. Betterdata spans both starting points, but its model choices are especially relevant when the available data is limited or has distinctive relationships. This is a workflow distinction, rather than a claim that one product always creates more realistic records.

Relationships, rules, and unusual cases

For a connected dataset, believable individual rows are only part of the job. Tonic Fabricate centers iteration on the requested schema and provides a validation loop. Syntho gives explicit rules and consistent mapping a place alongside AI synthesis, which suits cases where certain values must stay predictable. Betterdata emphasizes relational dependencies and offers separate approaches for sequential data. A useful distinction is whether the next change will be a request such as adding an unusual customer scenario, a reusable rule across environments, or a change to how a model represents linked events.

From a dataset to a repeatable workflow

Fabricate is attractive when generated data needs to reach developers quickly through exports, mock APIs, or an MCP client. Syntho is attractive when the organization wants generation jobs connected to source and destination systems through its interface or API. Betterdata suits teams that want generation embedded in a broader data-science environment with control over the model and deployment. For a small prototype, the first workflow may be enough. For a shared data service, the latter two offer a more infrastructure-oriented buying decision, with the implementation scope becoming a substantial part of the comparison.

How the pricing models compare

Fabricate combines a published subscription with credits and metered additional usage, so repeated refinement can affect consumption. Syntho quotes a feature-based license without per-generation consumption charges; connector and deployment scope still matter. Betterdata does not publish enough commercial detail to establish an equivalent starting cost. The table links to current profile pricing and source pages. Fabricate therefore provides the clearest self-service entry point, while the other two need a quote tied to the actual generation environment.

Which tool should you choose?

Choose Tonic Fabricate for building a new application dataset through conversation and iteration. Choose Syntho when several teams need repeatable data jobs mixing AI, rules, and masking. Choose Betterdata when linked tables, sequences, and model choice drive the decision. For model training, the key difference is the generation approach that matches your data structure, rather than the number of records advertised.