FakerForge suits developers, QA engineers, data analysts, and product teams who need synthetic data, mock databases, or API endpoints.
Best AI Synthetic Data Generators 2026
Synthetic data generators create artificial datasets for development, analysis, or other data-dependent work. Compare support for relational, time-series, and unstructured data with the formats you need, and consider maximum rows per job when assessing scale. Privacy-risk metrics and deployment options are also useful distinctions when weighing tools against your environment and data requirements. Free-plan availability and paid-from pricing help frame access and cost. Among the entries are NVIDIA ShadowPlay, REaLTabFormer, and Synthehol Dataset, alongside Tonic Fabricate and MOSTLY AI. Looking across the listed capabilities can help you identify which tools align with the kinds of data you work with and how you need to handle it.
Synthehol Dataset
It suits teams generating data for ML training, QA fixtures, or analytics workloads.
WebAPISelf-hostedSynth Studio
Synth Studio may suit healthcare, fintech, machine-learning teams, and organizations sharing enterprise data that need synthetic tabular datasets.
WebWindowsMac+3SimpleTest
It is positioned for business users, QA teams, Salesforce administrators, and DevOps or release teams.
WebBrowser extensionAPI+1SynthEngyne
SynthEngyne may suit AI teams that need synthetic data in text, image, tabular or time-series formats.
Tabularis.AI suits organizations that need synthetic data, custom AI models, or labeling workflows deployed on their own infrastructure.
REaLTabFormer suits researchers or project teams that need synthetic tabular or relational data and can work with Python and pandas.
It suits teams that need synthetic data generation, data analysis or integration into applications and workflows.
SDV suits developers and data teams who need synthetic datasets for single-table, sequential or relational data.
Fabricate suits teams that need generated data for software development, testing, AI work, or demos, including those working with databases, APIs, and files.
It suits developers building computer vision models and organizations handling industrial inspection, robotics, facade inspection, or other computer vision work.
SynthCity suits Python users building or evaluating synthetic data generators, including those working with tabular, time-series, survival, or image data.
It suits data scientists and owners of sensitive data who need to collaborate on synthetic data from a first-normal-form table.
It suits automotive, trucking, drone, robotics, agriculture, and defense teams validating perception and planning.