Head-to-head · updated 13 September 2026

DataShield vs Gretel: synthetic training data, or governed production data?

First, the thing no one else on this page will tell you. Gretel is gone as a buyable product. NVIDIA bought the firm in 2025. gretel.ai now sends you straight to an NVIDIA page. It's called "Synthetic Data Generation for Agentic AI". The tech lives on. It runs as two NeMo microservices. Data Designer and Safe Synthesizer. Safe Synthesizer is good. It does private synthesis. It does PII swaps on table data. Run it on your own Kubernetes.

DataShield does another job. On another clock, too. We make no synthetic data. We govern real records at query time. That's when an agent must act on a real buyer. We tokenize data at ingest. Agents then query it over MCP. Turning a token back is a special, audited step. Below is where the two paths split. It includes the rows NVIDIA wins.

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The short version

Pick DataShield when

Your agents read and write real records, not stand-ins. A refund bot must find the order that actually happened. Synthetic rows can't help it.
Someone will ask you to prove an agent's access log wasn't edited. Our chain answers with math. Run the checker.
You need to pull an agent's access mid-session. The very next tool call should fail. Not wait for a token to time out.
You want a reversible token. An analyst can join two tokenized tables. A support agent can still pull the raw value, through an audited request. How that works.

Pick NVIDIA NeMo (the old Gretel) when

You need to train or tune a model. You lack enough real data, or can't use it. That's the job Safe Synthesizer and Data Designer do.
You want differential privacy. It comes with a tunable budget and a privacy score on the output. We do something else instead: quasi-identifier generalization. Dates become year, decade, or age band. ZIPs become 3 or 4 digits. Phones, SSNs, and emails get partial masking. Each column gets its own score. Not DP synthesis.
You need a safe copy of production for lower estates. Fake rows are fine there. Nobody has to trace a record back.
You already run NVIDIA GPUs and NeMo. One more microservice is a short chat with procurement.

Bottom line: NeMo makes data that looks real. You train on it. DataShield governs data that is real. An agent acts on it. We keep proof of every call. Plenty of teams need both. Only one helps when the auditor asks what your agent did last March.

Feature by feature: synthetic data generation versus governed access

Competitor cells describe what NVIDIA's public site and NeMo docs say. We use the date above. Did we get something wrong? Email support@myorg.ai. We'll fix it and credit you.

What matters	DataShield	Gretel / NVIDIA NeMo	Edge
Core job	Govern real data at query time. Classify, tokenize, generalize, and authorize each call. Seal the decision into proof.	Make synthetic data before training. The docs say output is "entirely synthetic, with no one-to-one mapping to your original records".	n/a
Synthetic data generation	None. We don't make rows. We won't say we do.	Data Designer for prompt-seeded datasets. Safe Synthesizer for tabular synthesis, with score reports and privacy scores.	◇
Tokenization and data handling	Deterministic, join-preserving, vault-reversible tokens, applied at ingest. Plus generalization on quasi-identifiers: dates to year, decade, or age band. ZIPs to 3 or 4 digits. Partial phones, SSNs, and emails. Each gets its own score. Turning a token back is a special, logged step.	PII swaps over roughly 30 entity types. Faker values, redaction placeholders, hashing (the docs call it one-way), or custom rules. Not built to be reversed.	◆
Differential privacy	Not offered. We publish no formal privacy guarantee. What we ship instead: generalization on quasi-identifiers. Dates to year, decade, or age band. ZIPs to 3 or 4 digits. Partial phones, SSNs, and emails. Each gets its own score. Plus tokens that stay reversible under audit. Epsilon is theirs, not ours.	Differential privacy is a documented training setting. It ships with a tutorial and privacy metrics on the output.	◇
Audit proof	A SHA-256 hash chain, with Ed25519-signed checkpoints. Those checkpoints are chained too. Verification names the exact failure: tampering, insertion, deletion, or truncation. Try the checker.	We found no audit logging in the Safe Synthesizer docs. Platform-level auth exists. Job-level proof isn't documented.	◆
Agent authorization	Each checked tool call passes a scope ceiling. It also carries an access tier. We re-check it before dispatch, every time. A pulled grant lands mid-session.	Not in scope. A generation job has no notion of which agent may later read which row.	◆
Break-glass	Scoped, time-boxed emergency access, just for agents. It auto-revokes on its own. It can't be quietly deleted from the log.	Not offered.	◆
GDPR erasure	Crypto-shred of per-subject key material. Plus ISO 27560 consent receipts. The audit chain stays checkable after erasure.	Synthetic output has no subject to erase. That's a real answer for training sets. Your live copy is still your problem.	n/a
MCP and agents	Native MCP endpoints sit on Auth, Ontology, and Lighthouse. MCP tool tokens carry scope ceilings, always. Per-call metering ties to the agent.	Marketed for agentic AI, but as training data. We found no MCP server or runtime agent surface in the docs.	◆
Deployment	Self-hosted, in your own cloud or data center. Or on a dedicated single-tenant server we run for you. Your keys, always.	NeMo microservices install with Docker or Helm, on your own Kubernetes. There's a hosted preview on build.nvidia.com. Good marks here.	◈
Maturity signals	Auth, Guardian and Lighthouse are all live now. Guardian and Lighthouse have run since April 2026. SOC 2 isn't certified yet, and we say so.	Gretel shipped from 2019. NVIDIA bought it in 2025. The code now sits behind one of the largest engineering teams around.	◇
Pricing	Published model, scoped instant quote, no sales wall.	No public price for the microservices. It rides the NVIDIA enterprise licensing motion.	◆

◆ DataShield leads◇ Gretel / NVIDIA NeMo leads◈ comparable

NVIDIA and Gretel claims come from nvidia.com and the NeMo docs, only. We checked them on 13 September 2026. We link them below, not just a paraphrase from memory.

Three things you get here that you won't get from a synthetic data generator

A token that leads back to a person

Synthetic data is safe because it maps to nobody. That's also why it can't serve a live agent. Our tokens are deterministic. Two tables still join on the same buyer. They're reversible too. An approved request can get the raw value back, under audit. See the data model.

Proof that survives an audit

A log that can be quietly edited has no evidentiary value. Ours is a hash chain with signed checkpoints. The checker tells you what went wrong. Not just that something did. That's what EU AI Act Article 12 and HIPAA §164.312(b) reviewers care about. Try it in your browser, no signup.

Authority that can change mid-flight

An analyst leaves at 14:00. Their agent is still 20 minutes into a job. DataShield re-checks the next checked tool call. Against current access, right then. It fails closed. Nothing about a training set can do that for you. How Auth does it.

Where Gretel is genuinely stronger

Gretel built a good thing. NVIDIA didn't throw it away. Safe Synthesizer still does private synthesis over sensitive tables. It still swaps PII across a long list of entity types. It now ships score reports that show how private the output is. Run it with Helm, on your own cluster. Is your blocker "we can't get enough real data to train this model, and legal won't sign off on the data we have"? That's the tool, then. We aren't in the running there. We don't make a single row.

What we'd push back on is the word "agentic". The NVIDIA page sells synthetic data for building agentic systems. Buyers read that and hear for running them. Those are two different problems, though. Once your agent is live, it queries records tied to real people. The questions become: who authorized this call? Was that OK still valid? Can you prove the log is intact? Safe Synthesizer's docs say nothing about audit logging or access control. That was never its job. It's ours.

Questions worth asking both of us

These are the questions we'd want answered if we were buying. Ask them on every vendor call, including ours.

What actually happened to Gretel?

NVIDIA bought the firm in 2025. The brand is retired. As of 13 September 2026, gretel.ai sends you to an NVIDIA use-case page. It's a plain 301 redirect. docs.gretel.ai no longer resolves at all. The word Gretel appears nowhere on the new page. The tech lives on as NeMo Data Designer and NeMo Safe Synthesizer. Are you renewing or moving? Ask NVIDIA which of your old workflows survived the port. The marketing story changed too, from privacy work to training data.

Can synthetic data replace tokenization for a production agent?

No, and NVIDIA's own docs explain why. The output is "entirely synthetic, with no one-to-one mapping to your original records". That's the whole point of it. A support agent needs a value that leads back to a real person. Under policy, every time. DataShield tokenizes data at ingest. Agents query it over MCP after that. Turning a token back is a special, audited step. Use synthetic data for the training set. Use tokens for the live system.

Can you cryptographically prove an audit log entry wasn't deleted?

DataShield: yes, it can. Each record commits to the one right before it. We sign and chain every checkpoint. Verification tells deletion from truncation from tampering, every time. Run it on a sample chain at /verify. NVIDIA: we found no audit trail in the Safe Synthesizer docs. It's a batch generation service, so that's fair enough. Just don't expect it to answer an examiner.

What happens to a revoked agent mid-session?

DataShield re-checks access on every checked tool call. It bites on the next call, always. Not when a token happens to expire. NeMo has no agent access model to pull, either. It isn't in the runtime path at all. Whatever you use, ask this. How long does a compromised agent keep working after you pull its access?

How does GDPR erasure interact with the audit trail?

DataShield crypto-shreds per-subject key material. Then it issues an ISO 27560 consent receipt. Actor identities in the chain carry HMAC commitments, not names. So the proof stays checkable after the subject is gone. On the NVIDIA side, a well-made dataset has no subject to erase. That's a clean answer, for training data. It says nothing about the live copy the request was really about.

Does DataShield have SOC 2?

Not yet. We won't imply otherwise, ever. Auth is live now. It ships with a public threat model and a checker anyone can run. Guardian and Lighthouse have run since April 2026. Design-partner terms include source escrow. You've just watched a focused vendor get folded into somebody else's platform. You'd be right to worry about that happening again. Details on the security page.

gretel.ai now redirects to NVIDIA's "Synthetic Data Generation for Agentic AI" page. The Gretel brand is retired. - gretel.ai redirect, 13 Sep 2026
NVIDIA's positioning: "Accelerate the development of agentic workflows with high-quality, domain-specific synthetic data." - nvidia.com use cases, 13 Sep 2026
Safe Synthesizer output is "entirely synthetic, with no one-to-one mapping to your original records". - NVIDIA NeMo microservices docs, 13 Sep 2026
PII replacement offers Faker values, redaction placeholders, hashing described as one-way, and custom rules. - NVIDIA NeMo microservices docs, 13 Sep 2026
NeMo microservices install locally with Docker or on Kubernetes with Helm. - NVIDIA NeMo microservices docs, 13 Sep 2026
EDPB names pseudonymization as a GDPR risk mitigation for AI models. - EDPB Opinion 28/2024

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