Head-to-head · updated 13 September 2026
DataShield vs MOSTLY AI: synthetic data for the lab, or governed tokens for production?
MOSTLY AI is a synthetic data platform, and a good one. Their TabularARGN models learn the shape of a real table and emit a fake table that behaves like it. Differential privacy is built in. The Synthetic Data SDK is Apache-2.0 and free. If your problem is "I need to share banking data with a partner, or train a model, or fill a test environment, and legal keeps saying no," they solve that cleanly.
DataShield does a different job. We keep the real rows and make them unreadable. Datasets are tokenized at ingest; agents query tokenized data over MCP; detokenization is a privileged, audited operation. So the question isn't which vendor is better. It's whether your AI agent needs to act on a real customer or a plausible one. Below is the honest split, including the rows MOSTLY AI wins.
The short version
Pick DataShield when
- Your agent has to touch a real customer. Freezing a real card or reading a real claim can't run on invented rows.
- Someone will eventually ask you to prove an agent's access log wasn't edited. Our chain answers that with math. Run the verifier.
- You need to pull an agent's authority mid-session and have the very next tool call fail, not wait for a token to expire.
- You need authorized re-identification. Our tokens are deterministic, join-preserving, and vault-reversible under policy. Synthetic rows are one-way by design.
Pick MOSTLY AI when
- You need to hand data to someone outside your walls: a partner bank, a vendor, a research team, a hackathon. Synthetic rows travel where real ones legally can't.
- You're training or benchmarking a model and want more rows than you have, rebalanced or upsampled. That's their home turf and we don't do it.
- You want a differential privacy guarantee with a knob you can tune. 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, which is a weaker and different promise.
- You want to start free.
pip install -U mostlyaiand you're generating today, Apache-2.0, no sales call.
Bottom line: synthetic data is for the lab; governed tokens are for the live system. Most regulated data programmes end up wanting both. If your agents are already in production and nobody can prove what they did, start here.
Feature by feature
Competitor cells describe what MOSTLY AI's public site, blog, and GitHub say as of the date above. If we've mischaracterised something, email support@myorg.ai and we'll correct it, credited.
| What matters | DataShield | MOSTLY AI | Edge |
|---|---|---|---|
| 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. | Automated quality and privacy reports on generated datasets. We found no tamper-evident access log in their public docs. | ◆ |
| 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 the product. Their AI Assistant writes and runs Python over data you already gave it. | ◆ |
| Break-glass | Scoped, time-boxed emergency access for agents that 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 holds no individual to erase, which is a real advantage. The source table it learned from is still yours to handle. | ◈ |
| 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. Real rows, unreadable. | One-way synthetic generation with TabularARGN and differential privacy. New rows, no link back to a person. | ◈ |
| Privacy guarantee | 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 HMAC tokenization that stays reversible under audit. No formal privacy guarantee and no epsilon: that ground is theirs. | Differential privacy built into training, with a tunable budget. A stronger formal guarantee than ours. | ◇ |
| Model training data | We don't generate rows. We govern the ones you have. | Upsampling, conditional simulation, rebalancing, fairness controls. Mixed tabular, text, time-series, multi-table. | ◇ |
| Open source | Closed source. Design-partner terms include source escrow. | Apache-2.0 Synthetic Data SDK on PyPI and GitHub, roughly 799 stars. Genuinely free to start. | ◇ |
| MCP and agents | Native MCP endpoints on Auth, Ontology, and Lighthouse. MCP tool tokens with scope ceilings; per-call metering attributed to the agent. | "Agentic data science" means a chat assistant that analyses data. We found no MCP server or agent identity model in their docs. | ◆ |
| Deployment | Self-hosted in your own cloud or data center, or on a dedicated single-tenant server we operate. Your keys. | Kubernetes or OpenShift, plus a LOCAL SDK mode where data never leaves your machine. Strong sovereignty story. | ◈ |
| 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. | Founded 2017, $25M Series B in 2022, Gartner Cool Vendor 2022, named customers including Erste Group and Swiss Post. | ◇ |
| Pricing | Published model, scoped instant quote, no sales wall. | Free SDK, then "Request a demo" for the platform. No dollar figures published. | ◈ |
◆ DataShield leads◇ MOSTLY AI leads◈ comparable
MOSTLY AI claims are drawn from mostly.ai, their blog, their news archive, and the mostly-ai/mostlyai GitHub repository, 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 platform
Proof that survives an audit
A log that can be quietly edited proves nothing. Ours is a hash chain with signed checkpoints, and the verifier tells you what broke, 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 2pm. Their agent is still halfway through a 40-minute job. With DataShield the next governed tool call is re-checked against current authority and fails closed. How Auth does it.
A token you can turn back
Synthetic rows are one-way on purpose, which is the whole point of them. But a fraud analyst sometimes has to know which actual person that pattern belongs to. Our tokens are reversible through the vault, under policy, with the request written into the chain. See the architecture.
Where MOSTLY AI is genuinely stronger
We'd rather you hear this from us. MOSTLY AI has been at this since 2017 and it shows. TabularARGN is a named architecture, not a marketing word, and it handles multi-table and time-series data that most privacy tools quietly refuse. Differential privacy is a real mathematical guarantee with a budget you can set, and we don't offer one. Their SDK is Apache-2.0, runs locally so nothing leaves your laptop, and prints a privacy report you can hand to your DPO. Erste Group and Swiss Post are on the record. When a bank needs to send a partner something that looks like customer data but contains no customer, synthetic generation is simply the right answer and tokens are not.
Here's the push-back. Synthetic data solves the sharing problem and the training problem. It does not solve the production problem. The moment an agent has to read a real balance, update a real policy, or freeze a real card, it's back in the real table, and the questions that follow are about authority and evidence: was this agent allowed to make that call at that moment, and can you prove the record of it wasn't touched afterwards. Gartner expects most unauthorized agent transactions through 2028 to be internal policy violations rather than attacks. A perfectly private synthetic dataset says nothing about those. That's the layer we sell.
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.
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 against a sample chain at /verify. MOSTLY AI: their reports cover the privacy quality of generated data, not access history. We found no tamper-evidence mechanism in their public docs. Ask them.
What happens to a revoked agent mid-session?
DataShield re-checks authority on every governed tool call, so revocation bites on the next call. MOSTLY AI doesn't publish an agent authority model; their assistant runs Python inside a workspace you already trusted. Ask how long a compromised session keeps working after you pull access.
Is a MOSTLY AI alternative for production data even the right search?
Probably not, and we'll say so. If you need GDPR-safe data to share, train on, or fill a test environment, buy synthetic data. If you need agents to work on real records under policy, that's a different tool. We lose deals to synthetic data vendors when the use case really is the lab, and that's fine.
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 still verifies after the subject is gone. MOSTLY AI has a neat answer on the output side: there's no individual in a synthetic row to erase. The source table it learned from is still your problem, and that's where we sit.
Do you do differential privacy like TabularARGN does?
No, and we won't pretend. 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 and HMAC tokenization. No epsilon, no formal budget. If a formal privacy guarantee on a released dataset is the requirement, theirs is the stronger answer. If the requirement is controlling who may read the real value, that's ours. Worth asking them which epsilon they actually run in production, because the default matters.
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 so a small vendor isn't a single point of failure. Details on the security page.
- MOSTLY AI's current positioning: hero "Data for Everyone", sub-head "Powering the world's best data teams", primary CTA "Request a demo". Named customers include Erste Group and Swiss Post. — mostly.ai, 13 Sep 2026
- The Synthetic Data SDK is Apache-2.0, installs with pip, supports TabularARGN, differential privacy, and a LOCAL mode that trains on your own resources. Roughly 799 GitHub stars. — github.com/mostly-ai/mostlyai, 13 Sep 2026
- "By recreating the statistical truths of private datasets without exposing individuals, it turns the unreachable into the usable." — MOSTLY AI, "Data for Everyone: A Manifesto", 29 Sep 2025
- AWS Clean Rooms partner motion: merge sensitive multi-party data, then emit one shareable synthetic dataset. Example given is a bank and an insurer combining behavioural data for fraud patterns. — MOSTLY AI blog, 29 Jul 2025
- Last publicly dated funding: $25M Series B, announced 11 January 2022. Gartner Cool Vendor in Data-Centric AI noted 19 January 2023. — mostly.ai news archive, 13 Sep 2026
- ≥80% of unauthorized agent transactions through 2028 will be internal policy violations rather than attacks. — Gartner, 2026
- EDPB names pseudonymization as a GDPR risk mitigation for AI models. — EDPB Opinion 28/2024
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AllEvery comparison
One honest scorecard per vendor, sources at the bottom.
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