Head-to-head · updated 13 September 2026
DataShield vs Monte Carlo: who watches the table, and who authorizes the agent?
Monte Carlo invented data observability. They still set the pace today. Point them at a warehouse. They learn what normal looks like. Then they page the right owner. That happens when freshness slips. It happens when a volume drops. It happens when a schema shifts under a dashboard. They pushed that same system up into the agent layer. It includes agent traces. It includes evaluation and trajectory monitors. It includes a Troubleshooting Agent. It also includes circuit breakers. These let an agent refuse to answer over a broken table. Their hero line is "Trust your agents in production." They have earned the right to say it.
We do not detect anomalies. DataShield has no freshness monitors. We have no alerts on your warehouse tables, and no incident queue. We will not imply otherwise. Data sits one layer down from here, and we tokenize it at ingest. Agents query tokenized data over MCP. Detokenization is a restricted, audited step. DataShield checks each controlled tool call. It checks the call against the agent's access. It checks before the call runs. The decision lands in a hash chain. You can check the chain without trusting us. Most teams who talk to us already own a monitoring tool. Here is the honest split. It includes rows they win.
The short version
Pick DataShield when
- Someone will ask you to prove an agent's access log was not edited. An examiner, an auditor, or Article 12 of the EU AI Act. Our chain answers with math, not a policy PDF. See how the verifier works.
- You need to pull an agent's authority mid-session and have the very next tool call fail. Not the next token refresh. The next call.
- The sensitive columns have to be found, labelled and tokenized before an agent reads them. The keys have to stay on your own infrastructure.
- You would like to see a price before you book a call. Ours is published.
Pick Monte Carlo when
- Your problem is that a table went stale on Sunday night and nobody noticed until the board deck was wrong. That is the job they were built for. It is not one we do at all.
- You want anomaly detection without writing rules. Their models learn freshness, volume, schema and distribution behaviour on their own. That is a shorter runway than authoring tests by hand.
- You need incident management: ownership routing, an on-call workflow, root-cause help, and a record of which pipeline broke what.
- Your estate is wide. Warehouses, lakes, BI tools, ETL, Oracle and SAP on the Enterprise tier. Their connector matrix is years ahead of ours. Ours is one database.
Bottom line: Monte Carlo tells you whether the data is healthy. DataShield decides whether this agent may read it and keeps proof of the decision. Those are two different obligations. A regulated team will sensibly buy both.
Feature by feature
These competitor cells describe Monte Carlo's own words. We pull from their public site, docs and blog. We checked as of the date above. Did we get something wrong? Email support@myorg.ai. We will fix it, and credit you.
| What matters | DataShield | Monte Carlo | Edge |
|---|---|---|---|
| Anomaly detection and monitoring | None. We have no anomaly detector, and no freshness or volume monitors either. Alert routing doesn't exist here. We also have no SLAs on your tables. Is that your problem? Buy them, or one of their rivals. | This is their core product. It is very good. It offers machine-learned monitoring. That covers freshness, volume, schema and distribution. You write few rules up front. It also adds ML metric monitors. It covers GenAI pipelines too. | ◇ |
| Incident management | We do not ship this. We have stewardship worklists for data decisions. That is a different thing. We will not pretend it is on-call. | They cover the incident lifecycle and handle ownership routing. ServiceNow integration comes standard. They add a Troubleshooting Agent for root cause, and a large case-study library to show for it. | ◇ |
| Profiling and trust scoring | Every dataset gets a 20-section profile. It covers completeness and field stats. It covers patterns and column meaning. It covers the relationship graph too. It covers quality metrics and lineage. It covers business rules and source fingerprint. It covers compliance checks as well. A weighted trust score rolls this up. It rolls up per entity type. It rolls up per domain and estate. Each axis gets its own trend. We recompute it hourly. | They offer statistical profiles too. They add a data health score. Their estate is much wider than ours. It wires into alerts and dashboards. We do not have those. | ◈ |
| PII and PHI classification | We cover 129 field classes. They cover PII, PHI, financial data and secrets. That includes all 18 HIPAA Safe Harbor identifiers. It includes eight non-US national ID formats too. We check with regex plus checksums. That means Luhn, NPI, Verhoeff, ABA, IBAN and GTIN. We add column-name lexicons. We add anti-pattern suppressors. There is no model here. So verdicts stay deterministic. They reproduce from a config digest. | This is not part of their product. Their Scale tier filters PII. But that PII is what they collect. It protects their own telemetry. It is not a classifier for your columns. | ◆ |
| Audit evidence | We use a SHA-256 hash chain. Ed25519 signs each checkpoint. The checkpoints chain to each other too. The check returns one of three verdicts. It names the failure type. That means tampering, insertion, deletion or truncation. Tamper an already-proven chain again? It un-proves itself right away. | Audit logging sits in their Enterprise tier. We found no public tamper-check system. We found no verifier a third party could run. | ◆ |
| Agent authorization | Every controlled tool call passes four checks. It passes them before it runs. Check one: a scope ceiling. Check two: a consented-tool allowlist. Then an access tier check, and a fresh revocation check to close it out. Any check fails? The call fails closed. | Circuit breakers do gate an agent. This happens before it reads a serving table. That is real. We credit it. But the input is a data-quality rule status. It is not who the agent is. It is not what the agent may touch. | ◆ |
| Break-glass | This is scoped, time-boxed emergency access for agents. An admin must approve it with step-up. It also needs an IP allowlist. It auto-revokes on its own. No one can quietly wipe it from the log. | Their public material does not describe this. | ◆ |
| GDPR erasure | We crypto-shred each subject's key material, and add ISO 27560 consent receipts. Actor identities get HMAC-committed in the chain. So the proof still checks out. It checks out even after the subject is gone. | Their product does not take this job on. Trace content stays in your setup. Their agent docs say so. That is a good design. But they do not describe how erasure works. | ◆ |
| Tokenization and data handling | We apply tokens at ingest. They are deterministic. They preserve joins. A vault can reverse them. We also generalize quasi-identifiers. Dates become a year, decade or age band. ZIPs shrink to 3 or 4 digits. Phones, SSNs and emails get partly masked. Each column gets a cardinality-reduction score. We audit every detokenization. We refuse the lookup if that audit write fails. | They do not use this term. Their system reads metadata and query history. It does not read your rows. That is a sensible way to dodge the problem. It does not solve it, though. | ◆ |
| MCP and agents | We run more than 200 MCP tools today. They span Ontology, Auth, Corpus and Lighthouse. MCP tool tokens carry a scope ceiling. Delegation uses RFC 8693 token exchange. That ceiling stays enforced. We meter every per-call use. We attribute it to the agent. | They announced an MCP server in October 2025. They called it a private preview. Agents ask about alerts in plain language. They also ask about lineage and data health. Their own post is plain about it. It serves observability state. It does not serve the datasets themselves. | ◆ |
| Agent output quality | Nothing here. We do not score hallucinations or run evals. We do not watch tool-call trajectories either. | They document four monitor types. These cover context, performance, behaviour and output. They are evaluation, metric, trajectory and validation. This is a real product area. We are not in it. | ◇ |
| Deployment | You can self-host this in your own cloud. You can self-host it in your data center. Or we run a dedicated single-tenant server for you. Docker images ship for Auth, Ontology, Corpus and Lighthouse. Guardian checks the signed deploy manifest. Ed25519 audit-signing keys stay on your instance. They sit in a key directory you control. Kid-keyed rotation fails closed on an unknown key. KMS or HSM custody is a built seam in Auth. It is not a shipped integration yet. HMAC tokenization keys sit in your setup today. Or they derive from your machine key. They do not sit in a KMS. | They are SaaS first. Scale and Enterprise tiers add a customer-hosted agent. They also add a PrivateLink option. So raw rows need not cross into their cloud. That is a metadata-plane design. It is not self-hosting the platform. | ◆ |
| Maturity signals | Guardian and Lighthouse went live in April 2026. We are not SOC 2 certified yet, and we say so right on the page. It's not buried in a footnote. | They were founded in 2019. They have raised $236M or more. Their October 2025 round valued them at roughly $1.6B. They claim 400 or more enterprise customers. They also have a long public case-study list. | ◇ |
| Pricing | We publish pricing up front. You get a scoped instant quote. There is no sales wall. | Tier features are public. Dollars are not. You pay per table. You choose pay-as-you-go or committed usage. Third-party estimates put a typical deal in the low six figures a year. But Monte Carlo publishes no figures itself. | ◆ |
◆ DataShield leads◇ Monte Carlo leads◈ comparable
We draw Monte Carlo claims from three sources. Those are montecarlo.ai, docs.getmontecarlo.com and their own blog. We last checked on 13 September 2026, and link them below. We do not work from memory.
Three things you get here that you won't get from a data observability platform
Proof that survives an audit
A log an admin can quietly edit proves nothing. Ours is a hash chain instead. It carries signed checkpoints. The check tells you what broke. It does not just say something broke. EU AI Act Article 12 reviewers care about that. HIPAA §164.312(b) reviewers care about it too. See the mechanism.
Authority that can change mid-flight
An analyst leaves on a Friday. Their agent is 20 minutes into a 40-minute job. DataShield re-checks the next controlled tool call. It checks that call against current access. It fails closed right away. A monitoring layer writes you a tidy incident about it. That report lands on Monday. How Auth does it.
The sensitive columns found and handled
A healthy table can still be full of PHI. We label columns against 129 classes, and tokenize them at ingest. We generalize the quasi-identifiers too. So an agent joins on a token. It never joins on a patient number. What the catalog does.
Where Monte Carlo is genuinely stronger
Start with the obvious. They have been at this since 2019, and have raised over $236M. Target, Disney, Nasdaq and Highmark all get named on their front page. Their models learn what normal looks like on a table. No one has to write tests first. That is the thing every rules-based rival has to talk around. Their connector matrix runs across warehouses, lakes, BI and ETL. On the Enterprise tier, it adds Oracle and SAP HANA. We scan PostgreSQL. That is not the same sport. They also moved fast into the agent layer. That means evaluation, metric, trajectory and validation monitors. It means an Operations Agent too. It also means an MCP server. You can ask it about data health in plain English.
Their September 2026 circuit-breaker post deserves credit. It comes closer to our argument than anything else in the category. "Before the tool reads the serving table, it checks the rule status. If it's green, proceed. If it's breached, the agent declines and says why." That is a real pre-execution gate. It is not just an alert. Here is our push-back, though. That gate only asks if the data is healthy. It does not ask who this agent is. It does not ask whose access it borrowed. It does not ask if someone pulled that access four minutes ago. It does not ask if this column is PHI. When the gate fires, you get a decision. You do not get proof an examiner can check a year later. You would still be taking someone's word for it. Healthy and allowed are different words. We do the second one.
Questions worth asking both of us
These are the questions we would ask. We would ask them if we were buying. Ask them on every call. Ask us these too.
Can you cryptographically prove an audit log entry wasn't deleted?
DataShield: yes. Each record commits to the one before it. We sign each checkpoint. We chain the checkpoints too. The check tells deletion apart from truncation. It tells both apart from tampering. Watch it run yourself at /verify. Try it against a sample chain. Monte Carlo lists audit logging as an Enterprise-tier feature. We found no public tamper-check system. We found no verifier either. Ask them to show you one.
What happens to a revoked agent mid-session?
DataShield re-checks access on each controlled tool call. Revocation lands on the very next call. It does not wait for a token refresh. Monte Carlo's agent monitors watch behaviour. They watch tool-call order too. They watch output quality as well. Their circuit breakers can stop an agent. That happens when a data-quality rule breaks. We could not find an identity revocation path. Their public docs do not show one. Ask how long a compromised agent keeps working. Ask what happens after you pull its access.
How does GDPR erasure interact with the audit trail?
DataShield crypto-shreds the per-subject key material. We issue an ISO 27560 consent receipt too. We HMAC-commit actor identities in the chain. So the proof still checks out. It checks out even once the subject is gone. Monte Carlo keeps agent trace content in your own setup. That helps. But their docs describe no erasure method. Ask what happens to a year of prompts. Ask what happens to the completions too. What happens when a subject invokes the erasure right?
Is DataShield a Monte Carlo alternative, or do we run both?
Run both, in most cases. We have no anomaly detection, and no monitors on your warehouse tables either. There are no freshness SLAs, and no incident queue. Is a stale table your pain? Then we are the wrong purchase. Buy us for the datasets your agents read. There, the job is authorization and proof, not detection. One case is different, though. A self-hosted buyer may replace a chunk of their scope with us. They mainly want profiling, classification and agent oversight. They want it all in one stack.
Monte Carlo has an MCP server too. What's different?
Monte Carlo announced theirs in October 2025. They called it a private preview. It lets an agent ask about alerts. It also covers lineage and data health. It answers in natural language. Their post is clear about one thing. It serves observability state, not the datasets. Agents query the controlled data itself here. We cover more than 200 tools. The tool token carries a scope ceiling. DataShield authorizes the call before it runs. It seals the decision into the chain too. Want an agent to ask why a dashboard broke? Use theirs. Want an agent to read regulated data under a policy you can prove later? Use ours.
Does DataShield have SOC 2?
Not yet. We will not imply otherwise. Auth is live with a public threat model. It ships a verifier you can run yourself. Guardian and Lighthouse have been in production since April 2026. Design-partner terms include source escrow. So a small vendor is not a single point of failure. See details on the security page.
- Monte Carlo's current positioning: "Trust your agents in production" and "the only platform that monitors, troubleshoots, and optimizes your agents and their underlying data at scale". - montecarlo.ai, 13 Sep 2026
- Circuit breakers for agents: "Before the tool reads the serving table, it checks the rule status. If it's green, proceed. If it's breached, the agent declines and says why". - Monte Carlo blog, 1 Sep 2026
- Monte Carlo announced its MCP server. They called it a private preview. It lets agents query alerts. It also covers lineage and data health. It answers in natural language. - Monte Carlo blog, 21 Oct 2025
- Agent Observability ships four monitor types. They span context, performance, behaviour and output. They are evaluation, metric, trajectory and validation. - docs.getmontecarlo.com, 13 Sep 2026
- Pricing runs per table. It spans Start, Scale and Enterprise tiers. They publish no dollar figures. Monte Carlo says: "pay-as-you-go for full flexibility, or enjoy discounts and predictability by committing to usage". - Monte Carlo pricing page, 13 Sep 2026
- Their Series E raised $135M. It valued them at roughly $1.6B. The frame was expansion into AI observability. - TechTarget, Oct 2025
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