SynthEngyne

SynthEngyne generates synthetic datasets for AI training and development projects. It supports text, image, tabular and time-series data, with a workflow for creating dataspaces, generating datasets, then viewing, analyzing and exporting them for AI models. Users can make domain-specific datasets from proprietary documents and adjust generation parameters and methods. The product lists quality control, validation, deduplication and data cleaning, along with real-time, streaming, batch and enterprise-scale processing.

Credit use depends on the output: text, tabular and time-series generation costs 1 credit per row, while image generation costs 10 credits per image. The free plan provides 1,000 credits every month without requiring a credit card. Paid credit bundles are one-time purchases: Starter is ₹1,500, paid once, Popular is ₹3,000, paid once, Pro is ₹6,000, paid once, and Enterprise is ₹10,000, paid once. The pricing page says credits never expire and dataspaces, datasets and storage are unlimited. SynthEngyne is described as being in closed alpha, with public availability planned by the end of March 2026. Generated datasets are retained for 30 days by default and can be deleted sooner.

SynthEngyne offers multiple synthetic data formats, adjustable generation and a monthly free credit allowance. Its stated closed-alpha status and default 30-day dataset retention are key points for prospective users.

SynthEngyne may suit AI teams that need synthetic data in text, image, tabular or time-series formats. The maker names healthcare, finance, automotive and retail among industries that could use it.

SynthEngyne is a synthetic-data generator for AI training and development, with text, image, tabular, and time-series outputs. It is best suited to teams that want several data types and one-time credit purchases. Its generation and data-quality tools are promising, but closed-alpha availability is a serious constraint.

Overview

The workflow moves from creating dataspaces to generating datasets, then viewing, analyzing, and exporting them for AI models. Teams can create domain-specific data from proprietary documents and adjust the generation parameters and methods. Real-time, streaming, batch, and enterprise-scale processing give teams several ways to run generation workloads.

Validation, deduplication, quality control, and data cleaning help address dataset preparation. That combination could suit teams building varied training data, especially in healthcare, finance, automotive, or retail. However, SynthEngyne is currently described as being in closed alpha, with public availability planned by the end of March 2026. That makes it difficult to recommend for projects that need dependable access now.

Key features

• Four data types: Generate text, images, tabular data, and time-series data. The range suits projects spanning structured records and image data, but image generation uses ten credits per image compared with one credit per text, tabular, or time-series row.
• Custom datasets from documents: Build domain-specific datasets from proprietary documents with adjustable parameters and generation methods. This offers a way to tailor output to a team's subject matter, though it does not change the per-unit credit costs.
• Multiple processing modes: Real-time generation, streaming, batch processing, and enterprise-scale processing cover different workload patterns. These options are useful for teams with varying throughput needs, but closed-alpha status limits their practical appeal for production-dependent work.
• Dataset quality and workflow: Create dataspaces, generate data, then view, analyze, and export datasets. Validation, deduplication, quality control, and cleaning support preparation before data is used with AI models.
• Security and retention: DataGen describes encryption in transit and at rest, role-based access, multi-factor authentication, vulnerability assessments, and an incident-response program. Generated datasets are retained for 30 days by default, can be deleted sooner from the dashboard, and backups are destroyed within 180 days. Data is primarily processed and stored in India, with possible transfers elsewhere under safeguards. That may suit teams comfortable with those arrangements, but organizations with stricter location or retention requirements should check fit before use.

Pricing

SynthEngyne uses freemium, credit-based pricing. Credits never expire, and dataspaces, datasets, and storage are described as unlimited. Text, tabular, and time-series generation costs one credit per row; images cost ten credits each, so image-heavy projects use an allowance much faster.

Custom credit purchases range from ₹100 to ₹1,00,000. The one-time packs avoid renewal commitments, and the non-expiring credits allow use over time. The main pricing tradeoff is modality: one image uses the same credits as ten rows, so the displayed row equivalents do not translate to equivalent image capacity.

Platforms

SynthEngyne is described as a web-based service through its try page and dashboard workflow. No seat limits are stated. Support is available through a contact form and [email protected], including for privacy-related requests.

Who it's for

SynthEngyne is a plausible fit for AI teams that need synthetic data across several modalities, want to customize domain-specific datasets from documents, and can work within a credit budget. Its monthly free credits offer a low-commitment starting point, while the one-time packs suit teams that prefer to avoid subscriptions.

It is a poor fit for projects requiring public, dependable access today, or for organizations that cannot accept a 30-day default dataset retention period, backups lasting up to 180 days, or primary processing and storage in India.

Pros and cons

• Pro: Four supported data types let teams work with text, images, tables, and time series in one product.
• Pro: Validation, deduplication, and cleaning provide practical dataset-preparation tools alongside generation.
• Pro: Credits do not expire, and paid packs are one-time purchases with no subscription renewal.
• Con: Closed-alpha availability makes it a risky choice for projects that need broad access or operational continuity.
• Con: Images cost ten credits each, which can make image-heavy generation consume a pack quickly.
• Con: Default 30-day dataset retention and India-centered data processing may not work for every team's data policies.

Alternatives

For a free Windows option with a required supported GPU and GeForce 551.52 driver or later, consider NVIDIA ShadowPlay. For a freemium service with API, self-hosted, and web platforms and a $29 monthly Fabricate Plus plan that includes $25 in monthly credits, basic exports, and Discord and email support, consider Tonic.ai.

SDV Enterprise is another option. Simudyne Pulse is another option. Synth Studio is another option. Synthetic AI Data is another option. SynthForge IO is another option. Tabularis.AI is another option.

Browse more options in Synthetic Data Generation Software and AI Synthetic Data Generators.

Verdict

SynthEngyne is best for AI teams seeking varied synthetic data, built-in quality controls, and credits that can be bought once and used without an expiry date. Its strongest reason to choose it is the combination of four data types, document-based customization, and non-expiring credits. Look elsewhere if the project needs a generally available service now or has strict data retention or location requirements.