Synthehol Dataset

Synthehol Dataset is a synthetic-data service that turns a representative sample into generated records while measuring privacy risk and output quality. It fits teams that need repeatable data for machine-learning pipelines, QA fixtures, or analytics, especially when API and deployment choices matter. The main trade-off is clear: a capable free tier exists, but serious volume and team use quickly move into expensive subscriptions.

Overview

The process starts with a sample upload. Synthehol analyzes distributions and correlations, creates synthetic rows, and evaluates each dataset for privacy, fidelity, utility, and similarity. That assessment is more useful than generation alone because it gives practitioners several dimensions for deciding whether an output is suitable for development or analysis. Privacy-risk metrics are explicitly included.

The service claims it can generate 10 million rows in about 12 seconds, making it aimed at workloads where throughput matters. Its stated use cases span healthcare, banking and finance, pharmaceuticals, insurance, retail, and load testing. Synthehol says customer data is not sold, shared, or used beyond delivering its services, and describes the product as built for HIPAA, GDPR, SOC 2, and ISO 27001. Enterprise adds on-premise deployment, while the platform description says data never leaves your environment, a potentially important distinction for regulated teams.

Key features

• Sample-driven generation: Distribution and correlation analysis helps preserve relationships found in the input instead of producing arbitrary rows. This suits teams that need realistic test or training data.
• Quality and privacy scoring: Privacy, fidelity, utility, similarity, and privacy-risk measures provide a framework for reviewing generated datasets before use.
• High-volume output: The stated 10-million-row, approximately 12-second generation speed targets large data-refresh and load-testing jobs, although actual results will depend on the workload.
• Formats and automation: CSV and JSON are offered on lower tiers; Professional adds Parquet and Avro. Full REST API access and batch generation support pipeline integration rather than one-off manual exports.
• Deployment and support: Web, API, and self-hosted access cover different operating models. Enterprise provides dedicated infrastructure, on-premise deployment, custom integrations, white labeling, and a 99.9% uptime SLA. Support progresses from basic on Free to email on Starter, priority and Slack on Professional, and phone, email, Slack, plus a dedicated account manager on Enterprise.

Pricing

Free — $0 per month. This is a genuine starting tier with 1,000 credits, 50,000 rows, five projects, five datasets, one user, and 1 GB of storage each month. Data is retained for 30 days. CSV and JSON exports, API access, and basic support are included. It suits an individual evaluating the workflow or running small fixtures, but the single-seat limit, short retention, and modest quotas constrain shared or recurring production work. A free trial is also offered.

Starter — $399 per month. The allowance rises to 5,000 credits and 5 million rows per month, with 25 projects, 100 datasets, 25 users, 50 GB of storage, and 90-day retention. CSV and JSON, API access, and email support remain the core package. This is the practical step for a small team that has outgrown Free; it still lacks the additional export formats, custom models, and advanced clustering reserved for Professional.

Professional — $2,999 per month. This plan supplies 50,000 credits, 50 million rows monthly, 125 projects, 500 datasets, 50 users, and 500 GB of storage, with one-year retention. Parquet and Avro join CSV and JSON, while priority and Slack support, custom models, and advanced clustering address larger or more specialized pipelines. The price is difficult to justify for occasional generation, but teams with sustained high-volume workloads and broader collaboration have materially more headroom.

Enterprise — custom pricing. Custom credits and volume pricing accompany dedicated infrastructure and unlimited projects, datasets, users, storage, and retention. Dedicated account management, phone/email/Slack support, white labeling, custom integrations, a 99.9% uptime SLA, and on-premise deployment make this the tier for organizations with procurement, isolation, or service-level requirements. Buyers should expect a tailored commercial discussion rather than a published monthly figure.

Platforms

Synthehol Dataset is offered through a web interface, an API, and self-hosted deployment. REST access and batch generation make it suitable for automated pipelines, while self-hosted and Enterprise on-premise options address organizations that cannot place sensitive data in a shared service.

Who it's for

Choose Synthehol when your team needs synthetic records plus measurable privacy and utility signals, particularly for ML training, QA data, analytics, or load testing. Free is appropriate for a solo proof of concept. Starter fits a small collaborative team; Professional is aimed at organizations generating tens of millions of rows and needing richer formats or modeling controls. Enterprise is the better match when isolation, integrations, support coverage, or uptime commitments are mandatory.

Pros and cons

Pros

• Combines generation with privacy, fidelity, utility, and similarity scoring.
• REST API, batch generation, and multiple export formats support production pipelines.
• Self-hosted and on-premise choices address stricter data-handling requirements.
• Free tier allows evaluation without payment and includes API access.

Cons

• Free is limited to one user, 50,000 rows, 1 GB, and 30-day retention.
• Starter costs $399 monthly and still omits Parquet, Avro, custom models, and advanced clustering.
• Professional costs $2,999 monthly, creating a steep jump for teams between experimentation and large-scale use.

Alternatives

For a broader comparison, see AI Synthetic Data Generators.

• FakerForge is a freemium, cross-platform option with a free database, token, row, table, API-request, and mock-API allowance; choose it when those bounded mock-data quotas fit better.
• REaLTabFormer is free, MIT-licensed software for Linux, macOS, Windows, and self-hosting; pick it when you want a local open-source tool rather than a paid hosted plan.
• Synth Studio is free to use under an MIT license with up to 1 million rows per generation, making it preferable for smaller open-source workloads.
• MOSTLY AI offers a free freemium entry point across API, web, and self-hosted environments; consider it when you want that alternative platform.
• NVIDIA ShadowPlay is free Windows software for supported GeForce systems, so it is relevant only to that different use case.
• SimpleTest provides a free testing tier with 50 parallel runs and 500 execution minutes monthly; choose it when test execution, rather than a standalone synthetic-data platform, is the priority.
• SynthEngyne offers monthly free credits and supports text, tabular, time-series, and image generation; it may suit mixed data types.
• Synthetic AI Data has a free starting plan across API, self-hosted, and web platforms.

Verdict

Synthehol Dataset is a strong fit for teams that need synthetic data, API-driven automation, and explicit privacy and quality evaluation in one workflow. Its Free plan is useful for proving the process, while Professional and Enterprise provide the volume, controls, and deployment support for larger programs. Look elsewhere if your workload is small, your budget cannot absorb $399 or $2,999 per month, or an open-source self-hosted tool is sufficient.