SynthEngyne

SynthEngyne is a synthetic-data platform for AI training and development projects that need several data modalities in one workflow. It is most compelling for teams building domain-specific datasets from internal documents and wanting one-time credit purchases rather than a subscription. The principal reservation is that it remains in closed alpha, which makes it a poor choice when dependable public access is essential.

Overview

SynthEngyne structures projects as dataspaces: create a space, generate datasets, inspect and analyze the results, then export them for AI models. It can produce text, images, tabular records, and time-series data, giving mixed-modality projects a broader toolkit than a table-only generator.

The maker describes real-time, streaming, batch, and enterprise-scale processing. Users can also derive domain-specific datasets from proprietary documents, adjusting generation parameters and methods to suit a particular use case. Quality control, validation, deduplication, and data cleaning are built into the described workflow, which should reduce some of the manual preparation normally required before model development.

Availability remains the decisive limitation. SynthEngyne is described as being in closed alpha; public availability was planned by the end of March 2026. That status makes it unsuitable for a production dependency that needs broad, predictable access today.

Key features

• Four data types: Text, image, tabular, and time-series generation covers both structured and unstructured AI workloads. The trade-off is sharply different credit consumption: one row costs one credit, while one image costs 10 credits.
• Proprietary-document datasets: Teams can create domain-specific data from their own documents and tune generation parameters and methods. This is more relevant to specialized healthcare, finance, automotive, and retail projects than to generic sample-data needs.
• Multiple processing modes: Real-time, streaming, batch, and enterprise-scale processing are presented as supported approaches. That range lets teams match generation to interactive development or larger pipeline jobs, although the alpha status tempers how confidently it can be adopted for critical operations.
• Dataset safeguards: Quality control, validation, deduplication, and cleaning address common defects in generated data. These controls are valuable when synthetic output will feed model training, but they do not replace a team's own review of fitness for purpose.
• Workspace workflow: Dataspaces, datasets, and storage are advertised as unlimited. Users can view, analyze, and export generated sets rather than treating generation as a one-off download.
• Security and retention: DataGen says it encrypts data in transit and at rest, uses role-based access controls and multi-factor authentication, performs vulnerability assessments, and maintains an incident-response program. Generated datasets are retained for 30 days by default and can be deleted sooner from the dashboard; backups are destroyed within 180 days. Processing and storage primarily use servers in India, with transfers elsewhere protected by safeguards. The company says it does not sell or rent personal data and uses contractual data-processing agreements with service providers.

Pricing

SynthEngyne uses a freemium model with monthly free credits and one-time paid packs. The free plan provides 1,000 credits every month with no credit card required. Credits do not expire, and dataspaces, datasets, and storage are unlimited. Because rows cost one credit and images cost 10, the free allowance represents 1,000 text, tabular, or time-series rows, or 100 images if spent exclusively on images.

Every paid pack is a one-time purchase with no subscription, priced at ₹0.05 per credit. Custom credit purchases range from ₹100 to ₹1,00,000. The packs are straightforward for row-heavy work, but image-heavy teams will consume their allowance quickly.

Who it's for

SynthEngyne fits AI teams in healthcare, finance, automotive, and retail that need customized synthetic data across multiple formats, especially when proprietary documents are central to the project. Its expiring dataset retention, India-centered processing, and stated security controls may also suit organizations with defined data-governance requirements.

It is not the right choice for teams that require a generally available service immediately, need a recurring subscription with predictable monthly capacity, or expect image-heavy generation to stretch a modest budget.

Pros and cons

• Pros: Four supported data modalities; document-based customization; validation, deduplication, cleaning, and quality controls; unlimited dataspaces, datasets, and storage; credits that never expire; one-time pricing with no subscription; documented encryption, access controls, MFA, and incident response.
• Cons: Closed-alpha availability limits suitability for dependable production use; image generation costs 10 credits per image versus one per row; default 30-day dataset retention requires timely export or deletion; primary data processing and storage are in India, which may matter for residency requirements.

Alternatives

Synth Studio is the better pick for a free, MIT-licensed option with up to 1 million rows per generation and broad API, desktop, web, and self-hosted platform support.

SDV Enterprise suits teams wanting a free community edition with five data types, five basic constraints, nine models, CSV and Excel support, and forum support.

YData SDK is preferable when a free monthly-credit plan, all features, and more than 20 connectors matter, with pay-as-you-go billing also available.

MOSTLY AI offers a free starting plan across API, self-hosted, and web deployments.

NVIDIA ShadowPlay is a free Windows utility rather than a synthetic-data platform.

Simudyne Pulse is aimed at cached simulations with rolling usage limits rather than general dataset generation.

Synthetic AI Data provides a free plan for getting started across API, self-hosted, and web environments.

SynthForge IO offers a free web plan with the first 20,000 rows of each dataset free and stated table, download, and storage limits.

For broader browsing, see Synthetic Data Generation Software and AI Synthetic Data Generators.

Verdict

Choose SynthEngyne if your team needs text, image, tabular, and time-series synthesis, document-driven customization, and non-expiring credits purchased once. Look elsewhere if closed-alpha access or image-credit economics would put a production project at risk.