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. It's a good one, too. Their TabularARGN models learn the shape of a real table. Then they build a fake table. It behaves like the real one. They build in differential privacy. The Synthetic Data SDK is Apache-2.0. It's free to use. Say your problem is this. You need to share banking data with a partner. Or train a model. Or fill a test setup. And legal keeps saying no. So far, they solve that problem well.
DataShield does a different job. We keep the real rows. Nothing about them stays readable. We tokenize datasets at ingest. Agents query tokenized data over MCP. Detokenization is a privileged action. We log it every time. So the real question isn't which vendor is better. It's this: does your AI agent act on a real customer? Or a plausible one? Below is the honest split. It even shows where 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. You need the very next tool call to 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 instead: dates to year, decade or age band; ZIPs to 3 or 4 digits; partial phones, SSNs and emails. Each column gets a measured cardinality-reduction score. That is a weaker, 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
The MOSTLY AI cells describe what their public site, blog, and GitHub say. That's as of the date above. Did we get something wrong? Email support@myorg.ai. We'll fix it, and credit you.
| What matters | DataShield | MOSTLY AI | Edge |
|---|---|---|---|
| Audit evidence | SHA-256 hash chain. Ed25519-signed checkpoints, chained together too. The check names the failure: tampering, insertion, deletion, or truncation. Try the verifier. | Automated quality and privacy reports on generated datasets. We found no tamper-evident access log in their public docs. | ◆ |
| Agent authorization | Every checked tool call hits three gates first. A scope ceiling. An access tier. A revocation re-check. Revocation lands mid-session, right away. | Not the product. Their AI Assistant writes and runs Python. It runs over data you already gave it. | ◆ |
| Break-glass | Scoped, time-boxed emergency access for agents. It auto-revokes on its own. No one can quietly delete it from the log. | Not offered. | ◆ |
| GDPR erasure | We crypto-shred per-subject key material. We add ISO 27560 consent receipts too. The audit chain still checks out after erasure. | Synthetic output holds no individual to erase. That's a real plus. But the source table it learned from is still yours to handle. | ◈ |
| Data handling | We apply deterministic, join-preserving, vault-reversible tokens at ingest. We also generalize quasi-identifiers. Dates become year, decade, or age band. ZIPs become 3 or 4 digits. Phones, SSNs and emails get partly masked. Each column gets a measured cardinality-reduction score. Real rows, made unreadable. | One-way synthetic generation, using TabularARGN plus differential privacy. New rows come out, with no link back to a person. | ◈ |
| Privacy guarantee | Quasi-identifier generalization. Dates become year, decade, or age band. ZIPs become 3 or 4 digits. Phones, SSNs and emails get partly masked. Each column gets a measured cardinality-reduction score. We also add deterministic HMAC tokenization. It stays reversible under audit. No formal privacy guarantee here, and no epsilon. That ground is theirs. | They build differential privacy into training, with a tunable budget. That's a stronger formal guarantee than ours. | ◇ |
| Model training data | We don't make new rows. We control the ones you already have. | Upsampling, conditional simulation, rebalancing, fairness controls. Mixed tabular, text, time-series, multi-table data too. | ◇ |
| Open source | Closed source. Design-partner terms include source escrow. | Apache-2.0 Synthetic Data SDK. It's on PyPI and GitHub, with roughly 799 stars. Genuinely free to start. | ◇ |
| MCP and agents | Native MCP endpoints on Auth, Ontology, and Lighthouse. MCP tool tokens carry scope ceilings. We meter per-call, and tie each call back to the agent. | "Agentic data science" means a chat assistant that looks at data. We found no MCP server in their docs. We found no agent identity model either. | ◆ |
| Deployment | Self-hosted in your own cloud, or your own data center. Or a dedicated single-tenant server we run for you. Your keys, always. | Kubernetes or OpenShift. Plus a LOCAL SDK mode, where data never leaves your machine. A strong sovereignty story. | ◈ |
| Maturity signals | Auth, Guardian and Lighthouse are live in production. Guardian and Lighthouse have run since April 2026. SOC 2 isn't certified yet. We say so, plainly. | Founded in 2017. A $25M Series B in 2022. A Gartner Cool Vendor pick in 2022. Named customers include 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
We draw MOSTLY AI claims from mostly.ai, their blog, and their news archive. We also checked the mostly-ai/mostlyai GitHub repository, last on 13 September 2026. So we link them below. We don't 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 someone can quietly edit proves nothing. Ours is a hash chain, with signed checkpoints. The verifier tells you what broke. Not just that something did. That's what EU AI Act Article 12 reviewers care about. HIPAA §164.312(b) reviewers care too. 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 system re-checks the next checked tool call. It checks against current access rights. It fails closed. How Auth does it.
A token you can turn back
Synthetic rows are one-way on purpose. That's the whole point of them. But a fraud analyst sometimes has to know who a pattern belongs to. Our tokens can reverse through the vault. Only under policy. We write each request 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. It shows. TabularARGN is a named architecture. It's not just a marketing word. It handles multi-table and time-series data. Most privacy tools quietly refuse that work. Differential privacy is a real math guarantee. You can set a budget for it. We don't offer that. Their SDK is Apache-2.0, and it runs locally. So nothing leaves your laptop. It also prints a privacy report. Hand that to your DPO. Erste Group and Swiss Post are on the record as customers. A bank sometimes needs to send a partner something that looks like customer data. But it must contain no real customer. Synthetic generation is the right answer there. Tokens are not.
Here's our push-back, though. Synthetic data solves the sharing problem. It solves the training problem too. It does not solve the production problem. An agent might need to read a real balance. Or update a real policy. Or freeze a real card. At that moment, it's back in the real table. The questions that follow are about access and evidence. Was this agent allowed to make that call, right then? Can you prove the record wasn't touched after the fact? Gartner expects most unauthorized agent transactions through 2028 to be internal policy breaks. Not outside 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. Ask us too.
Can you cryptographically prove an audit log entry wasn't deleted?
DataShield: yes. Each record commits to the one before it. We sign and chain checkpoints too. The check tells deletion apart from truncation, and from tampering. Run it against a sample chain at /verify. MOSTLY AI: their reports cover privacy quality on generated data. Not access history. We found no tamper-check feature in their public docs. Ask them about it.
What happens to a revoked agent mid-session?
DataShield re-checks access on each checked tool call. Revocation bites on the very next call. MOSTLY AI doesn't publish an agent access model. Their assistant runs Python inside a workspace you already trusted. So ask them this. How long does a compromised session keep working, after you pull access?
Is a MOSTLY AI alternative for production data even the right search?
Probably not. We'll say so straight up. Need GDPR-safe data to share, train on, or fill a test setup? Buy synthetic data. Need agents to work on real records under policy? That's a different tool. So we lose deals to synthetic data vendors, when the use case really is the lab. And that's fine by us.
How does GDPR erasure interact with the audit trail?
DataShield crypto-shreds per-subject key material. We also issue an ISO 27560 consent receipt. We HMAC-commit actor identities in the chain. So the proof still checks out, even 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. But the source table it learned from is still your problem. That's where we come in.
Do you do differential privacy like TabularARGN does?
No, and we won't pretend we do. We do quasi-identifier generalization instead. Dates become year, decade, or age band. ZIPs become 3 or 4 digits. Phones, SSNs and emails get partly masked. Each column gets a measured cardinality-reduction score, plus HMAC tokenization. No epsilon here, and no formal budget. Need a formal privacy guarantee on a released dataset? Theirs is the stronger answer. Need control over who may read the real value? That's ours. Ask them which epsilon they run in production. The default matters a lot.
Does DataShield have SOC 2?
Not yet. We won't imply otherwise. Auth is live, with a public threat model and a verifier anyone can run. Guardian and Lighthouse have run in production since April 2026. Design-partner terms include source escrow. So a small vendor isn't a single point of failure. More detail on the security page.
- MOSTLY AI's current site 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. It installs with pip. It supports TabularARGN, differential privacy, and a LOCAL mode. That mode 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: a bank and an insurer combine behavioural data for fraud patterns. - MOSTLY AI blog, 29 Jul 2025
- Last publicly dated funding: a $25M Series B, announced 11 January 2022. Also a Gartner Cool Vendor pick in Data-Centric AI, noted 19 January 2023. - mostly.ai news archive, 13 Sep 2026
- By 2028, most unauthorized agent transactions will be internal policy violations, not attacks. Gartner puts the share at 80% or higher. - Gartner, 2026
- EDPB names pseudonymization as a GDPR risk mitigation for AI models. - EDPB Opinion 28/2024
Other head-to-heads
DataShield vs Gretel
Generated rows for the lab. Checked tokens for the live system.
Test dataDataShield vs Tonic.ai
Masked lower setups. Versus access and evidence in production.
DirectDataShield vs Skyflow
A strong PCI vault. Plus the agent questions it doesn't answer.
AllEvery comparison
One honest scorecard per vendor. Sources at the bottom.
See both mechanisms run in your browser. Break a live audit chain. Revoke an agent mid-session. Then decide what belongs in the lab, and what belongs in production. Demo Center access is free with a work email.
Get free Demo Center accessYou've seen the proof
Ready for a number? Scope your deployment and we'll price it against your own economics.
Get your quote →