Head-to-head · updated 13 September 2026
DataShield vs Gretel: synthetic training data, or governed production data?
First, the thing nobody else on this page will tell you: Gretel no longer exists as a product you can buy. NVIDIA acquired the company in 2025, and gretel.ai now redirects straight to an NVIDIA page called "Synthetic Data Generation for Agentic AI". The technology lives on as two NeMo microservices, Data Designer and Safe Synthesizer. Safe Synthesizer is good. It does differentially private synthesis and PII replacement over sensitive tabular data, and you can run it on your own Kubernetes.
DataShield does a different job on a different clock. Synthetic data is made before training, and by design it maps to nobody. We govern real records at query time, when an agent has to act on an actual customer. Datasets are tokenized at ingest; agents query tokenized data over MCP; detokenization is a privileged, audited operation. Below is where the two split, including the rows NVIDIA wins.
The short version
Pick DataShield when
- Your agents read and write real records, not stand-ins. A refund bot has to find the order that actually happened. Synthetic rows can't help it.
- Someone will one day ask you to prove an agent's access log wasn't edited after the fact. Our chain answers with math. Run the verifier.
- You need to pull an agent's authority mid-session and have the very next tool call fail, instead of waiting for a token to time out.
- You want a reversible token, so an analyst can join two tokenized tables and a support agent can still get the raw value through an audited request. How that works.
Pick NVIDIA NeMo (the old Gretel) when
- You need to train or fine-tune a model and you don't have enough real data, or you're not allowed to use it. That is the job Safe Synthesizer and Data Designer were built for.
- You want differential privacy with a tunable budget and a privacy score on the output. We do quasi-identifier generalization (dates to year, decade or age band; ZIPs to 3 or 4 digits; partial phones, SSNs and emails) with a measured cardinality-reduction score per column, not DP synthesis.
- You need a safe copy of production for lower environments, where fake rows are fine and nobody has to trace a record back.
- You already run NVIDIA GPUs and NeMo, and adding one more microservice to that stack is a short conversation with procurement.
Bottom line: NeMo makes data that looks real so you can train on it. DataShield governs data that is real so an agent can act on it, and keeps proof of every call. Plenty of teams need both. Only one of them 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 microservices docs say as of the date above. If we've mischaracterised something, email support@myorg.ai and we'll correct it, credited.
| What matters | DataShield | Gretel / NVIDIA NeMo | Edge |
|---|---|---|---|
| Core job | Govern real data at query time. Classify, tokenize, generalize, authorize each call, seal the decision into evidence. | Generate synthetic data before training. The docs say output is "entirely synthetic, with no one-to-one mapping to your original records". | — |
| Synthetic data generation | None. We don't make rows, and we won't pretend otherwise. | Data Designer for prompt-seeded datasets, Safe Synthesizer for tabular synthesis with evaluation reports and privacy scores. | ◇ |
| Tokenization and data handling | Deterministic, join-preserving, vault-reversible tokens applied at ingest, plus quasi-identifier generalization (dates to year, decade or age band; ZIPs to 3 or 4 digits; partial phones, SSNs and emails) with a measured cardinality-reduction score per column. Detokenization is a privileged, logged operation. | PII replacement over roughly 30 entity types using Faker values, redaction placeholders, hashing (the docs call it one-way), or custom rules. Not designed to be reversed. | ◆ |
| Differential privacy | Not offered, and we publish no formal privacy guarantee. What we ship instead is quasi-identifier generalization (dates to year, decade or age band; ZIPs to 3 or 4 digits; partial phones, SSNs and emails) with a measured cardinality-reduction score per column, plus deterministic tokens that stay reversible under audit. Epsilon is theirs, not ours. | Differential privacy is a documented training setting with a tutorial and privacy metrics on the output. | ◇ |
| Audit evidence | SHA-256 hash chain with Ed25519-signed checkpoints that are themselves chained. Verification names the failure: tampering, insertion, deletion, or truncation. Public verifier. | We found no audit logging described in the Safe Synthesizer documentation. Platform-level auth exists; job-level evidence isn't documented. | ◆ |
| Agent authorization | Every governed tool call passes a scope ceiling, an authority tier, and a revocation re-check before dispatch. Revocation 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 for agents. It auto-revokes and 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 verifiable after erasure. | Synthetic output has no subject to erase, which is a real answer for training sets. Your production copy is still your problem. | — |
| MCP and agents | Native MCP endpoints on Auth, Ontology, and Lighthouse. MCP tool tokens with scope ceilings, and per-call metering attributed 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 operate. Your keys. | NeMo microservices install with Docker or Helm on your own Kubernetes, and there's a hosted preview on build.nvidia.com. Good marks here. | ◈ |
| Maturity signals | Auth, Guardian and Lighthouse are live in production (Guardian and Lighthouse since April 2026). SOC 2 not yet certified, and we say so. | Gretel shipped from 2019 and was acquired by NVIDIA in 2025. The code now sits behind one of the largest engineering organisations in the industry. | ◇ |
| 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 are drawn from nvidia.com and the NeMo microservices documentation, last checked 13 September 2026. We link them below rather than 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, so two tables still join on the same customer, and reversible, so 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 silently edited has no evidentiary value. Ours is a hash chain with signed checkpoints, and the verifier tells you what went wrong, not just that something did. That's the property 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. With DataShield the next governed tool call is re-checked against current authority and 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, and NVIDIA didn't throw it away. Safe Synthesizer still does differentially private synthesis over sensitive tables, still replaces PII across a long list of entity types, and now ships with evaluation reports that score how private the output is. You can run it with Helm on your own cluster. If your blocker is "we can't get enough real data to train this model, and legal won't sign off on the data we have", that is the tool, and we are not in the running. We don't generate a single row.
What we'd push back on is the drift in the word "agentic". The NVIDIA page sells synthetic data for building agentic systems. Buyers read that and hear for running them. Those are different problems. Once your agent is live, it queries records that belong to actual people, and the questions become who authorized this call, was that authority still valid, and can you prove the log is intact. Safe Synthesizer's documentation says nothing about audit logging or access control, because 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 acquired the company in 2025. The brand is retired. As of 13 September 2026, gretel.ai returns a 301 redirect to an NVIDIA use-case page, docs.gretel.ai no longer resolves, and the word Gretel appears nowhere on the successor page. The technology shipped on as NeMo Data Designer and NeMo Safe Synthesizer. If you're renewing or migrating, ask NVIDIA which of your old workflows survived the port, because the marketing story changed from privacy engineering 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 point of it. A support agent looking up a real order needs a value that leads back to a real person under policy. DataShield tokenizes datasets at ingest, agents query tokenized data over MCP, and detokenization is a privileged, audited operation. 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. Each record commits to the one before it, checkpoints are signed and chained, and verification tells deletion apart from truncation and from tampering. Run it on a sample chain at /verify. NVIDIA: we found no audit trail described in the Safe Synthesizer docs. It's a batch generation service, so that's fair. Just don't expect it to answer an examiner.
What happens to a revoked agent mid-session?
DataShield re-checks authority on every governed tool call, so revocation bites on the next call, not when a token expires. NeMo has no agent authority model to revoke, because it isn't in the runtime path. Whatever you use, ask how long a compromised agent keeps working after you pull its access.
How does GDPR erasure interact with the audit trail?
DataShield crypto-shreds per-subject key material and issues an ISO 27560 consent receipt. Actor identities in the chain are HMAC-committed, so the evidence stays verifiable after the subject is gone. On the NVIDIA side, a properly synthesized dataset has no subject to erase, which is a genuinely clean answer for training data. It says nothing about the production copy the request was actually about.
Does DataShield have SOC 2?
Not yet, and we won't imply otherwise. Auth is live a public threat model and a verifier anyone can run. Guardian and Lighthouse have been in production since April 2026. Design-partner terms include source escrow, because you've just watched a focused vendor get absorbed into somebody else's platform and you'd be right to worry about it 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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