Head-to-head · updated 13 September 2026
DataShield vs Telmai: is the table behaving, or is the data safe to hand an agent?
Telmai watches data as it lands, closely. Point it at a lake or lakehouse. It builds ML baselines per column. Then it flags anomalies. Schema changes too. And drift. Before the dashboard breaks, ideally. On Google Cloud, it runs inside your own account. It reads the same Iceberg catalog as BigQuery, too. No copies leave. Their own post cites ZoomInfo. Over a billion data points a day. No sampling. GigaOm made them a Leader. In 2023 and again in 2024. They hold SOC 2 Type 2. We do not. Right here, we say so.
We do another job, a different one. Your pipelines do not get monitored, not at all. We profile and score the datasets an agent is about to read. We name what sits in each column. Then we keep proof. Proof of what the agent did next. Every dataset gets a 20-section profile. Full stop. Plus a weighted trust score per domain. Our classification is repeatable. Same data, same config, same verdict. Stamped, so you can re-run it and diff it. We tokenize datasets at ingest, always. Agents query tokenized data over MCP. Turning a token back is a privileged step. It gets audited too. Here is the honest split. Rows Telmai wins are in it, too.
The short version
Pick DataShield when
- Someone will ask you to prove an agent's access log was not edited. Could be an auditor. Could be a regulator. Could be Article 12 of the EU AI Act. Our chain answers with math, not a policy PDF. Run the verifier.
- You need to pull an agent's authority mid-session. The very next tool call should fail. Not the next token refresh. The next call.
- You need to know which columns hold PII or PHI. You want a verdict you can re-derive next quarter. Same answer, every time.
- The data plane, the policy engine, and the evidence all need to run on your own hardware, on keys you hold. And you want a price before the call.
Pick Telmai when
- The pain is a broken pipeline, not a nosy agent. You want to hear about a bad load at 3am. We send nobody a page at 3am.
- Your data lives in a lakehouse on Iceberg or Delta. At a scale where sampling is a lie. That is the ground they built on.
- You want incident triage, routing, and plain-language diagnosis in the product. Run by their named agent suite.
- Procurement wants SOC 2 Type 2 on the vendor list this quarter. They have it. We are not there yet.
Bottom line: Telmai tells you the table is behaving. We tell you what is in the table. We score it. We prove what the agent did with it. Most teams who buy us already own a watch tool. We would rather they kept it.
Feature by feature
The Telmai cells describe what their site and blog say. As of the date above. If we got something wrong, email support@myorg.ai. We will fix it, with credit.
| What matters | DataShield | Telmai | Edge |
|---|---|---|---|
| How data quality gets scored | A 20-section profile per dataset. It covers completeness, field stats, patterns, and column meaning. Plus a relationship graph and quality metrics. Business rules and lineage too. Compliance governance as well. On top sits a weighted trust score. Per entity type, domain, and estate. Each score gets a per-axis breakdown. Plus a trend, recomputed hourly. It is repeatable. Two runs on the same data agree. | "ML-driven and rule-based checks" over live tables. It learns baselines per column, with user-defined expectations on top. Strong for drift. Harder to re-derive a score six months later. | ◈ |
| Monitoring, incidents and source coverage | We have none of the watch half. No anomaly detector. No alerts on warehouse tables. Nor freshness SLAs. No incident queue. We do ship change and drift detection. Across dataset versions. A diff engine, a change classifier. Schema evolution and a CDC log too. On coverage we scan, profile, and classify a live PostgreSQL source. In place. No rows leave it. Plus file ingest and storage watching. PostgreSQL today, not your whole estate. | The core of the product, and the best part. Anomaly detection, consistency checks, incident management, routing. Plus a named agent suite. It diagnoses in plain language. Lake and lakehouse first: Iceberg, Delta, flat files. Through the Iceberg REST catalog. Plus OneLake. Plus Google Cloud. | ◇ |
| PII and PHI classification | 129 field classes cover PII, PHI, financial data, and secrets. Each comes with checksum validators and anti-pattern suppression. Plus per-class confidence. Every verdict carries a config digest and a rule version. You can re-run it and diff the result. The catalog also does source-system fingerprinting. Across 95 built-ins. | We could not find sensitive-data classification in their public docs. Their quality signals are about shape and behavior. Not about what the value means under HIPAA. | ◆ |
| Audit evidence | A SHA-256 hash chain, with Ed25519-signed checkpoints. We chain those checkpoints too. Verification names the failure. Tampering, insertion, deletion, or truncation. Try the verifier. | Data health KPIs published into the catalog, plus quality history. We found no cryptographic tamper proof over the log of who did what. | ◆ |
| Agent authorization | Every checked tool call passes a scope ceiling. Plus an authority tier. Plus a revocation re-check. That happens before dispatch. The call fails closed. | Not their layer. Their agents read and explain data health. We found no per-call decision point on data access. | ◆ |
| Break-glass | Scoped, time-boxed emergency access for agents. It revokes itself. It cannot be quietly deleted from the log. | Not described in their public docs. | ◆ |
| GDPR erasure | Crypto-shred of per-subject key material, plus ISO 27560 consent receipts. The audit chain still verifies after the subject is gone. | Not described in their public docs. Fair enough. A watch tool is rarely the system of record for a subject. | ◆ |
| Tokenization and masking | Deterministic, join-preserving, vault-reversible tokens. Applied at ingest. Plus 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 a measured cardinality-reduction score. Masking and generalization are switches. You turn them on, not defaults. | Not part of the product. They validate data. They do not de-identify it. | ◆ |
| MCP and agents | More than 200 MCP tools. Across Ontology, Auth, Corpus, and Lighthouse. Tool tokens carry a scope ceiling. We authorize calls before dispatch. Per-call metering ties back to the agent. | An MCP-compliant server. It lets agents such as Claude, Bedrock, or Vertex query validated data. Plus quality metadata. Announced October 2025. A waitlist CTA still sits by the agent suite. Right on their home page. | ◆ |
| Deployment | Self-hosted, in your own cloud or data center. Or a dedicated single-tenant server we operate. Docker images for Auth, Ontology, Corpus, and Lighthouse. Ed25519 audit-signing keys stay on your instance. 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. Not a shipped integration. HMAC tokenization keys sit in your setup today. Not in a KMS. We will not pretend otherwise. | SaaS, plus a customer-account model on Google Cloud. Their own post calls it zero-copy. It runs inside your own cloud account. It reads your Iceberg catalog. No copies leave your setup. Better than the SaaS-only story we expected. | ◈ |
| Maturity and certification | Live in production, with Guardian and Lighthouse there since April 2026. SOC 2 not yet certified. Design-partner terms include source escrow. | Founded 2021. SOC 2 Type 2, GigaOm Leader in 2023 and 2024, G2 badges in 2025. Named customers include Bill.com, PropertyGuru Group, and Zip Co. Funding is not public. | ◇ |
| Pricing | Published model, scoped instant quote, no sales wall. | Quote only. We found no pricing page at all on 13 September 2026. | ◆ |
◆ DataShield leads◇ Telmai leads◈ comparable
We draw Telmai claims from telm.ai and the Telmai blog. Last checked 13 September 2026. We link the posts below rather than 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 that can be quietly edited proves nothing. Ours is a hash chain with signed checkpoints. The verifier tells you what broke. Not just that something did. Reviewers ask about that property. EU AI Act Article 12. HIPAA §164.312(b) too. Try it in your browser, no signup.
Authority that can change mid-flight
An analyst leaves on a Friday. Their agent is 20 minutes into a 40-minute job. We re-check the next checked tool call. It fails closed. A watch layer would file you an incident. On Monday, not now. How Auth does it.
A verdict you can re-run
Ask a model why it scored a column. You get a story. Ask our classifier and you get a config digest. Plus a rule version. Run it again next quarter. The answer matches. Or the diff tells you which rule changed. See the catalog.
Where Telmai is genuinely stronger
Let us be plain. Telmai does a job we do not do at all. They sit where data lands. And watch it there. That happens at volumes where sampling would be dishonest. They do it on open formats. Through the Iceberg catalog your query engines already read. Their Google Cloud work runs in the customer's own account. No copies leave. That beats most watch-tool vendors' story. They plug into Atlan, OneLake, and Dataplex. No rip-and-replace required. GigaOm rated them a Leader two years running. They carry SOC 2 Type 2. We carry neither. Fair enough. If a bad load keeps you up, buy them.
Here is the push-back, though. Their agent suite reads quality signals. And explains them. It does not decide what an agent may do with the data. Health KPIs in a catalog are not proof. Not really. Quality and sensitivity are two separate questions, too. A column can pass every freshness and drift check. It can still hold plaintext medical record numbers. A baseline will never tell you that. Nothing about the shape of the data looks off. One more thing worth asking, on the call. An ML baseline is a moving judgment. Say a regulator asks why a dataset was cleared for use in March. A learned threshold from March is hard to reproduce. Ours is a config digest you can replay.
Questions worth asking both of us
These are the questions we would want answered if we were buying. Ask them on every call, ours included.
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. Verification tells deletion apart from truncation. And from tampering. Run it on a sample chain at /verify. Telmai: their docs cover data health KPIs and quality history. Not tamper proof over an access log. Ask them to show one, if it exists.
What happens to a revoked agent mid-session?
We re-check authority on every checked tool call. Revocation lands on the next call. Not the next refresh. Telmai's agents are about data health. We found no access-revocation feature in their public docs. Ask how long a compromised agent keeps reading. Even after you pull its access.
How does GDPR erasure interact with the audit trail?
We crypto-shred per-subject key material. Then we issue an ISO 27560 consent receipt. We HMAC-commit actor identities in the chain. The evidence still verifies once the subject is gone. Telmai does not describe an erasure step. Reasonable, for a watch tool. Ask both of us how it works. Not just the workflow.
Does DataShield replace Telmai? Do we drop our data observability platform?
No. We have no anomaly detection. No watchers on warehouse tables. No freshness SLAs. There is no incident queue either. If that is your problem, keep the tool that solves it. Run us on the checked datasets agents actually read. Our job there is profiling, classification, authorization, and proof. Plenty of teams will sensibly run both.
ML baselines or deterministic checks: which is better?
It depends on the question. For "did this table just get weird", a learned baseline wins. That is Telmai's ground. For "is this column a medical record number", a learned score is the wrong tool. Same for "can you show the same answer in six months". Ours is rule-based, with checksum checks. It carries a config digest. Plus a rule version. There is no model in the path. Nothing to drift.
Does DataShield have SOC 2?
Not yet, and we will not imply otherwise. Telmai has SOC 2 Type 2 and we do not. What we offer instead: a published threat model. Plus a verifier anyone can run. Plus source escrow in design-partner terms. That way a small vendor is not a single point of failure. Details on the security page.
- Telmai's current hero: "Autonomous ready data for Agentic AI". It also says "continuous data validation and quality signal within your data lake and lakehouse". They cite SOC 2 Type 2, and GigaOm Leader badges for 2023 and 2024. - telm.ai, 13 Sep 2026
- Data Reliability Agents launch: seven named agents. Plus an "MCP-compliant server, which enables LLM-powered agents" such as Claude, Bedrock or Vertex. It lets them query validated data. Plus quality metadata. - Telmai blog, 27 Oct 2025
- Atlan partnership: "ML-driven and rule-based checks" that "automatically detect anomalies, schema changes, and data drift". Embedded through Atlan's App Framework. - Telmai blog, 3 Oct 2025
- Google Cloud Lakehouse: Telmai "runs inside the customer's own Google Cloud account". It reads "the same Apache Iceberg REST catalog as BigQuery and Spark". They add: "no copies leaving your environment". They cite ZoomInfo at over a billion data points a day. No sampling, they say. - Telmai blog, 25 Jun 2026
- Microsoft OneLake collaboration brings constant validation to Fabric estates. - Telmai blog, 14 Nov 2025
- iLink Digital partnership targets financial services and insurance buyers on Microsoft Fabric with implementation services. - Telmai blog, 16 Apr 2026
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