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AI & Machine Learning · Engineering, IT & AI
Should you build or buy Synthetic Data Generation?
Synthetic data generation software creates artificial datasets that mimic the statistical properties of real data — used to train and evaluate AI models when real data is scarce, sensitive, regulated, or too costly to label at scale.
The build-vs-buy decision for Synthetic Data Generation turns on whether your use case is unstructured text and model evaluation (where AI makes building fast) or structured tabular and regulated data (where vendor privacy guarantees carry real compliance value); the specifics decide it.
- Domain
- AI & Machine Learning
- Function
- Engineering, IT & AI
- Industries
- Cross-industry
Last assessed June 2026 · re-scored quarterly via The Continuum.
Build it, buy it, or bridge?
| Build it | Buy it | Bridge (buy, then extend) | |
|---|---|---|---|
| Cost shape | Free OSS libraries for text; enterprise tabular plans run $2K–$25K+/month | Implementations at $175K–$350K; ongoing subscription on top | Open LLMs for text generation; vendor statistical guarantees for regulated structured data |
| Time to value | Fast for LLM/eval use cases; slower for privacy-proof structured data | Pre-validated statistical fidelity and compliance documentation from day one | Build for text and eval; buy for regulated structured output |
| Differentiation captured | None on the generation tooling; the trained model is the asset | None — vendor handles generation; your model quality is still your IP | Cost efficiency on text, risk coverage on structured data |
| AI feasibility today | NVIDIA, Databricks, Fireworks AI all publish production self-built pipelines using SDV, Distilabel, NeMo | Gretel and MOSTLY AI provide differential privacy and compliance documentation teams can stand behind | Distilabel or Magpie for instruction data; Gretel/Tonic for HIPAA/PCI structured sets |
| Who it fits | Teams needing instruction-response pairs, evaluation sets, or domain text augmentation | Orgs generating financial records, healthcare data, or other regulated structured datasets | Large orgs with both use cases needing different risk profiles for each |
When building Synthetic Data Generation makes sense
The AI era has specifically changed the calculus for text and unstructured data. Frontier models can generate synthetic training examples, produce instruction-response pairs, rewrite documents to match a target style, and create evaluation datasets directly — covering a large portion of what teams used to need dedicated synthetic data tooling for. Open-source libraries like SDV, Distilabel, and NeMo Data Designer are in documented production use at NVIDIA, Databricks, and Fireworks AI. The build case is strong when your data domain is narrow enough that you can validate synthetic quality internally, when you're primarily generating data for model evaluation rather than regulated production datasets, or when you're already using these libraries for adjacent ML work. The validation step matters: synthetic-only training data can lag accuracy by up to 35% on context-sensitive tasks, so quality checks are the real work.
When buying Synthetic Data Generation makes sense
For structured tabular data from regulated domains — financial records, healthcare data, PII-laden customer data — vendor platforms like Gretel and MOSTLY AI provide something genuinely hard to build: differential privacy guarantees and compliance documentation your legal team can stand behind. The statistical fidelity and formal privacy proofs these platforms provide took years to develop and audit. Buying earns its keep when you need to generate data that passes a compliance review, when your real data is too sensitive to share with a model training pipeline, or when the alternative is paying $175,000 to $350,000 to build and validate a compliant generation system from scratch. The premium over OSS tooling is a risk-adjusted cost, not pure overhead.
Getting enough labeled, privacy-safe training data is one of the most consistent bottlenecks in AI development. Vendors like Gretel and MOSTLY AI solve for statistical fidelity and differential privacy guarantees, which matter most when you're generating financial records, healthcare data, or anything that needs to pass a compliance review. Buying earns its keep when your real data is too sensitive to share with a model training pipeline, when you need formal privacy proofs your legal team can stand behind, or when you're generating structured tabular data where distributional accuracy is measurable and meaningful.
The AI era has changed the calculus for text and unstructured data specifically. Frontier models can generate synthetic training examples, rewrite documents to match a target style, or produce instruction-response pairs directly, which covers a large portion of what teams used to need dedicated synthetic data tooling for. The build case gets serious when your data domain is narrow enough that you can validate synthetic quality internally, when you're already using open-source libraries like SDV or Distilabel for adjacent work, or when your synthetic data needs are primarily for model evaluation rather than regulated production datasets.
Representative vendors
GretelMOSTLY AI
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Frequently asked
- What is Synthetic Data Generation?
- Synthetic data generation software creates artificial datasets that mimic the statistical properties of real data — used to train and evaluate AI models when real data is scarce, sensitive, regulated, or too costly to label at scale.
- When does building Synthetic Data Generation make sense?
- Building makes sense for text and model evaluation use cases, where frontier models and OSS libraries like Distilabel and SDV can produce training data cheaply. Multiple organizations including NVIDIA and Databricks run self-built synthesis pipelines in production using these tools.
- When does buying Synthetic Data Generation make sense?
- Buying makes sense for regulated structured data — financial records, healthcare data — where differential privacy guarantees and formal compliance documentation are required. Vendors like Gretel and MOSTLY AI provide proofs that self-built pipelines would take years to develop and validate independently.
- What are the main Synthetic Data Generation vendors?
- Representative vendors include MOSTLY AI, Tonic.ai, Gretel, Hazy. B4 Pro scores the full set.
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