Overview of the two tools
Synthetic data is increasingly important for organizations seeking to accelerate development cycles, strengthen privacy controls, and improve access to high-quality datasets. MOSTLY AI and K2view both address these needs, but they differ significantly in scope, architecture, and enterprise readiness.
MOSTLY AI focuses primarily on GenAI-powered synthetic tabular data for analytics and AI training. It emphasizes simplicity and privacy, and can be a reasonable fit for smaller teams or departmental analytics groups. K2view, by contrast, provides a comprehensive, enterprise-grade synthetic data generation capability rooted in its business entity approach. This enables the generation of realistic, multi-source synthetic data for testing, analytics, and AI initiatives. With a 5/5 Gartner Insights 12-month peer rating, K2view is widely recognized for its enterprise-grade depth and consistent results.
Teams researching synthetic data generation, synthetic data generation tools, or synthetic data solutions will find meaningful differences between the two offerings, especially in relational accuracy, automation, and end-to-end lifecycle coverage.
Gartner’s Market Guide for Data Masking and Synthetic Data highlights why organizations should look beyond point tools when evaluating synthetic data solutions. The report advises leaders to “prioritize SDM products that include the creation of synthetic data, synthetic records, events or tabular synthetic data, as this can greatly speed up existing test data management processes and enhance security of AI/ML model training.”
At the same time, Gartner notes that many masking and SDG vendors “rarely implement state-of-the-art capabilities to generate tabular synthetic data,” resulting in limited usefulness for enterprise programs. This highlights the gap between MOSTLY AI’s narrow tabular focus and K2view’s broader, entity-based SDG engine – which preserves referential integrity automatically and fits naturally into an integrated TDM and privacy workflow.
MOSTLY AI vs K2view comparison table
This highlights the gap between MOSTLY AI’s narrow tabular focus and K2view’s broader, entity-based SDG engine – which preserves referential integrity automatically and fits naturally into an integrated Test Data Management (TDM) and privacy workflow.
| Capability | MOSTLY AI | K2view | K2view advantages |
| Primary focus | Tabular SDG for analytics and AI training | End-to-end SDG across testing, analytics, and AI | Addresses more enterprise use cases |
| Generation methods | GenAI on tabular data | Four methods: rules, cloning, masking, GenAI | Provides flexibility for any testing or analytics scenario |
| Lifecycle coverage | Requires pre- and post-processing | Full lifecycle: subset, mask, generate, orchestrate downstream | Eliminates manual steps and accelerates delivery |
| Data types | Optimized for tabular | Built for multi-source, cross-system enterprise data | Handles enterprise complexity |
| Referential integrity | Limited; manual steps often required | Automatically preserved via the business entity approach | Ensures realistic data without any extra effort |
| Privacy handling | Treats all data as PII → slower processing | Automated sensitive data discovery and masking | Accelerates privacy workflows |
| Ease of use | No-code UI | Self-service for QA, dev, and data teams | Provides intuition and power |
| Performance | Slower due to all-PII processing | Fast generation at enterprise scale | Supports larger and more frequent refreshes |
| Enterprise readiness | Good for SMB/departmental use | Designed for large enterprise ecosystems | Fits global enterprises |
| Model transparency | Black-box GenAI | Transparent, configurable SDG methods | Enforces governance |
| Stability | Financial/scaling constraints noted | Stable and enterprise-proven | Offers long-term reliability |
Synthetic data generation with K2view
K2view synthetic data generation tools support the full lifecycle – from preparing source data to distributing high-fidelity synthetic outputs to downstream systems. Its business entity approach organizes all relevant data around an object such as a customer, policy, or account. This preserves hierarchy, behavior, and relationships across systems, ensuring synthetic test data remains realistic and highly useful during quality assurance.
Lifecycle coverage includes data subsetting, automated sensitive data discovery, PII masking, masking of training data, and synthetic generation – followed by orchestration into test environments and analytics platforms. This eliminates the pre- and post-processing work that MOSTLY AI requires.
K2view supports 4 SDG methods (rules-based, cloning-based, masking-based, and GenAI-based), giving teams the flexibility to support new functionality testing, regression testing, load testing, and analytics use cases with precision.
The K2view product integrates seamlessly into enterprise infrastructures, supporting Snowflake data masking, Workday data masking, mainframe data masking, Oracle data masking, Salesforce data masking, and data masking tools for SQL Server – as well as SAP test data management tools – and more.
Synthetic data generation with MOSTLY AI
MOSTLY AI provides a focused synthetic data offering designed primarily for tabular data used in analytics and machine learning workflows. Its no-code interface and built-in quality checks are appealing for data science teams or smaller departments that prioritize quick adoption and simplified workflows. The tool also includes a Python client for teams that prefer programmatic access.
MOSTLY AI applies a stringent privacy model, treating all data as personal data. While this can simplify privacy messaging, it also increases training and generation times – especially for large datasets. MOSTLY AI also requires manual pre- and post-processing to maintain relationships and data quality, which can become operationally burdensome in enterprise environments. Additionally, its black-box model design complicates governance, explainability, and regulatory alignment.
The tool is best suited for SMBs, departmental analytics initiatives, or organizations already using MOSTLY AI’s ecosystem. However, its limited enterprise penetration, narrower coverage, and financial constraints may limit long-term stability for teams that require a strategic, scalable approach to synthetic data generation.
Why K2view is the preferred choice
For organizations that need scalable, accurate, and enterprise-ready synthetic data, K2view stands out as the more complete and future-proof option, via:
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A business entity approach and data masking technology, that ensure cross-system relational accuracy
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4 generation methods, including a generative AI synthetic data technique, that support a wide range of testing and analytics needs
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End-to-end lifecycle automation, that eliminates operational friction
Add to the above its 5/5 Gartner Insights rating, and proven performance in complex enterprise environments, and it’s clear that K2view offers unmatched value.
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