A detailed comparison of Gretel vs K2view synthetic data generation, highlighting capabilities, limitations, and why enterprises choose K2view.
Intro to Gretel vs K2view
This Gretel vs K2view analysis examines how the two offerings approach enterprise-grade Synthetic Data Generation (SDG). Both are often evaluated among the best synthetic generation tools for privacy-safe data creation, but they differ significantly in architecture, automation, and enterprise readiness. Gretel is a developer-oriented tool centered on AI model training and privacy-safe data sharing. K2view provides a full-lifecycle synthetic data product designed for complex, multi-system environments and backed by a 5/5 Gartner Insights peer-review rating. Both aim to deliver high-utility, privacy-preserving datasets, but they differ widely in automation, lifecycle maturity, relational
consistency, and scalability.
Most enterprise data teams are demanding high-utility, privacy-safe data. Some need it for model training and sharing. Others need it for end-to-end testing across many systems. Gretel is model-centric and developer-first. K2view is entity-centric and enterprise-first. Join us for a deep dive in the following article.
High-level tool parameters
The following table shows the what, who, and where for each tool:
| Dimension | Gretel | K2view | What it means |
| Best for | AI model training, data sharing, 1-2 sources | Testing, analytics, and AI across many systems | Pick Gretel for lab use, and K2view for operational systems |
| Primary users | Developers, data scientists | QA, test, analytics, AI engineering, and data teams | Wider adoption beyond coders with K2view |
| Typical landscape | Simple tabular data, one domain | Multi-source, multi-system environments | K2view fits regulated, cross-system work |
Model vs entity approach
Gretel generates synthetic tables using LLM and ML. It shines in experimentation and developer-led projects. It typically requires coding, as well as data preparation and post-processing. That friction grows as sources and schemas multiply. Relationships across systems can break because there is no built-in business-entity structure.K2view treats data as business entities, such as customers, claims, or loans. It preserves hierarchies and keys across systems during the generation process. It has 4
generation methods: Rules, cloning, masking-based, and GenAI. A single product handles the subsetting and masking of training data, as well as generation, validation, and orchestration. Teams get realistic, compliant data that is ready for tests and models the moment it is produced.
Side-by-side comparison with K2view takeaways
The following table compares Gretel to K2view in 8 different ways:
| Topic | Gretel | K2view | K2view takeaways |
| Lifecycle coverage | Requires scripting around the edges | Full workflow. subset, mask, generate, orchestrate | Less glue work, and faster time to usable data |
| Generation methods | LLM and ML focused | Rules, cloning, masking-based, GenAI | Right tool for each scenario |
| Complexity handling | Best on single-source tables | Built for multi-source, multi-system | Realistic datasets for end-to-end testing |
| Data relationships | No native entity model | Entity model preserves hierarchies and keys | Fewer broken tests, and less data triage |
| Governance and access | Coding and config heavy | Self-service, for non-technical roles and RBAC | Broader adoption, and fewer tickets |
| Performance at scale | Slows as sources and prep grow | Optimized pipelines for enterprise scale | Predictable throughput |
| Privacy posture | Treats all data as PII, more manual work | Auto-discovery and masking before training | Faster, safer preparation |
| Accuracy | Strong model QA scores | Accuracy anchored in business entities and relationships | High utility for both testing and AI |
The Gretel model approach to SDG
Gretel uses modern LLM and ML techniques to create synthetic datasets, often optimized for AI model development and data sharing. Its model‑centric architecture and built‑in quality scoring support experimentation, but the tool typically requires coding expertise and pre‑/post‑processing work. This creates friction when scaling across QA, testing, or analytics teams, especially in multi‑source environments.
In short, Gretel is fine when:
- You’re training models on a few tables
- A developer or data scientist will own the workflow
- You need built-in scoring to compare model variants
- You want PLG speed to try ideas in a sandbox
…but expect the following tradeoffs:
- More coding
- Manual preparation, with weaker handling of multi-table hierarchies and cross-system keys
- Longer runs as environments grow
The K2view entity approach to SDG
K2view provides a holistic synthetic data generation product spanning subsetting, masking, generation, and orchestration. With four generation methods – rules, cloning, masking, and GenAI – the product adapts to testing, analytics, and AI use cases. Its business entity approach structures data by logical real‑world entities (customer, claim, loan), ensuring data relationships and hierarchies remain intact across systems. The result is highly realistic synthetic test data that is immediately suitable for downstream consumption.
K2view is the best choice when:
- You need synthetic data for testing, analytics, and AI across many systems
- Relationships and history must remain intact across sources
- You want one product to subset, mask, generate, validate, and distribute data
- You need self-service for testers and analysts, not just coders
With K2view, what you see is what you get:
- Preserved integrity
- Four generation methods to match the use case
- Orchestration that scales
- Less time spent stitching and fixing data
Look before you leap
Run the following 7-step head-to-head trial in your environment:
- Pick one entity
Include one example, “customer with orders and payments” and at least two
systems. - Subset the training data
Measure time and steps to filter by business rules. - Mask before training
Confirm whether sensitive data is automatically discovered and masked. - Synthesize 3 ways
Create synthetic data via GenAI, rules, cloning, or a blend – paying careful
attention to which tool supports all 3. - Validate relationships
Confirm parent-child links, keys, and timelines remain intact. - Orchestrate delivery
Push the synthetic dataset to two target environments, measuring handoffs and
runtime. - Score utility
Run a representative test suite and a model training task against the same
dataset. Compare pass rates, rework, and accuracy.om line
Analyst Gartner lists K2view – but not Gretel – as a key vendor in its Market Guide for Data Masking and Synthetic Data.
Gretel is a good point tool for developer-led model work on simpler data. K2view is a full-fledged synthetic data management solution for enterprises that need realism, privacy, and scale across systems. It supports 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.
K2view, by contrast, delivers a full‑lifecycle SDG product engineered for enterprise environments – maintaining relational integrity, automating data preparation, and scaling across interconnected system landscapes. This makes K2view the stronger choice for organizations needing fast, accurate, compliant synthetic data for testing, analytics, and AI.
Experience K2view Synthetic Data Generation first-hand in our interactive product tour.
