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Head-to-head · updated 13 September 2026

DataShield vs Datafold: the diff passed CI, but who let the agent read the table?

Datafold is good at a hard thing. Data Diff compares two datasets value by value, across databases. It tells you the exact rows and columns that moved. That is why it ended up inside the Datafold Migration Agent. Freeze the source data. Run the old code and the translated code. Diff the results. Ship when they match. Informatica, Talend, DataStage, SSIS and Alteryx into Snowflake or dbt. Parity you can show your steering committee. If you are moving a warehouse, buy it.

We do a different job on the same tables. DataShield profiles and classifies the datasets agents read. Then it governs what happens next. We tokenize datasets at ingest. Agents query tokenized data over MCP. Detokenization is a gated run. We audit it. Each checked tool call checks the agent's current rights first. We seal the choice into a chain. You can check it yourself. Datafold asks whether the values are right. We ask who had permission to touch them. And can you prove it in March?

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DataShield vs Datafold at a glanceEight questions data leads ask us. Scored from each vendor's public material. DataShield vs Datafold at a glance Eight questions data leads ask us. Scored from each vendor's public material. DataShield Datafold Tamper-evident audit chain you can verify Authority re-checked on every governed tool call PII and PHI classification on the columns GDPR erasure that keeps the chain valid Pricing you can see before a call Value-level data diff across two databases AI agent for legacy ETL migrations SOC 2, HIPAA and GDPR attestations shipped partial / roadmap not offered Sources at the bottom of this page.

The short version

Pick DataShield when

  • Agents already query your data. Someone will ask you to prove the access log was not edited. 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 want the columns classified before anyone builds on them. 129 field classes for PII, PHI, financial data and secrets, with reproducible verdicts.
  • You want the whole thing on your own infrastructure, with a price before the call.

Pick Datafold when

  • You are migrating off Informatica, Talend, DataStage or SSIS. Their Migration Agent translates the code and builds the test harness that proves parity. We do not do that at all.
  • You want value-level diffs in CI. A pull request that shifts a join or drops nulls should fail before it merges. Data Diff is the best-known tool for this. It works across databases.
  • You need anomaly monitors on warehouse tables, with lineage into Looker or Tableau. We have no anomaly detection and no alerting. We will not pretend otherwise.
  • Procurement wants SOC 2, HIPAA and GDPR attestations on the paperwork today.

Bottom line: Datafold proves the pipeline still produces the right values. DataShield proves the agent reading those values was allowed to. Buy Datafold for the migration. Buy us for the audit that lands eighteen months later.

Feature by feature

Competitor cells describe Datafold's public site and blog. As of the date above. If we mischaracterised something, email support@myorg.ai. We will correct it, credited.

What mattersDataShieldDatafoldEdge
Value-level data diffOntology ships a diff engine, a DuckDB diff path, and auto-diff. Also a change classifier, schema evolution tracking and a CDC log. It runs on checked datasets, to classify what changed and why. It is not a cross-database CI diff. We do not market it as one.The core product, and the reason people know the name. Diffs datasets within or across databases, at value-level precision. Wired into pull requests.◇
Migration validationNot our job. We snapshot, transform and profile datasets. We do not translate stored procedures or build a parity harness.Migration Agent 2.0 maps dependencies, translates code, and refactors. It builds synthetic data. It auto-builds a test harness that diffs both sides. They publish 6x faster timelines and 75% cost savings.◇
Quality scoringEach dataset gets a 20-section profile. Completeness, field statistics, patterns, column semantics. Relationship graph, quality metrics, lineage and compliance governance. A weighted trust KPI rolls up per entity type, domain and estate. It recomputes hourly.ML-driven anomaly monitors across quality checks. Plus alerts and BI links. A different shape of answer. They watch for drift. We score the dataset.◈
Sensitive data classification129 field classes, deterministic, with reproducible verdicts. We scan, profile and classify a live PostgreSQL source in place. No rows leave it. That is PostgreSQL today, not your whole estate.We found nothing about PII or PHI classing in their public docs. Their compliance story rests on certifications, not field-level labelling.◆
Audit evidenceSHA-256 hash chain with Ed25519-signed checkpoints that are themselves chained. Verification names the failure: tamper, insertion, delete or cut-short. Try the checker.Nothing published on crypto tamper proof. Their audit story is the diff artefact. Useful record. Not a sealed one.◆
Agent authorizationEach checked tool call passes a scope ceiling and a consented-tool allowlist. A rights tier and a pull-back re-check come before dispatch. It fails closed.Their agents run inside the client's workflow. Their context layer feeds other coding agents. We found no per-call authorization decision point.◆
Break-glassScoped, time-boxed emergency access for agents. It auto-revokes and cannot be quietly deleted from the log.Not shown in their public docs.◆
GDPR erasureCrypto-shred of per-subject keys, plus ISO 27560 consent receipts. The audit chain still verifies afterwards.They claim GDPR compliance as a certification. They do not describe the erasure method.◆
TokenizationDeterministic, join-preserving, vault-reversible tokens applied at ingest. Plus quasi-ID 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.Not part of their vocabulary. Migration Agent generates synthetic data for test harnesses. A different tool for a different problem.◆
MCP and agentsMore than 200 MCP tools across Ontology, Auth, Corpus and Lighthouse. Auth issues MCP tool tokens with scope ceilings. Per-call metering ties back to the agent.The Data Knowledge Graph serves lineage, business logic, usage and ontology over MCP. Any compatible agent can use it, named for Claude Code, Cursor and Windsurf. Labelled Beta. It is context, not checked data access.◈
DeploymentSelf-hosted, in your own cloud or data center. Or a single-tenant server we run for you. Ed25519 signing keys stay on your instance. They sit in a key folder you control. Key rotation uses kid keys. It fails closed on an unknown key. KMS or HSM custody is a built seam in Auth. Not a shipped link yet. HMAC tokenization keys sit in your setup. Or they come from your machine key today, not a KMS. Embeds run locally. Nothing leaves for that step.SaaS plus single-tenant and VPC setup on AWS, GCP and Azure. Marketplace listings on all three. LLM calls through client-approved endpoints. A strong setup story for their size.◈
Maturity signalsAuth, Guardian and Lighthouse are live in production. Guardian and Lighthouse have run since April 2026. SOC 2 is not yet certified, and we say so.Founded 2020. $20M Series A, led by NEA, in November 2021. SOC 2, HIPAA and GDPR. A Databricks Delivery Provider deal too. Their client list includes Disney, Moody's, AstraZeneca and Perplexity.◇
PricingOpen pricing, scoped instant quote, no sales wall.Quote only. Their pricing page redirects to the contact form. The FAQ bases pricing on the number of users. Also tables monitored and tested. They sell migration work separately.◆

◆ DataShield leads◇ Datafold leads◈ comparable

Datafold claims come from datafold.com and the Datafold blog. We checked last on 13 September 2026. We link them below rather than work from memory.

Three things you get here that you won't get from a data reliability tool

Proof that survives an audit

A diff report tells you two tables matched on Tuesday. It does not tell you who read them. It does not tell you if someone edited that record later. Ours is a hash chain with signed checkpoints. The checker names what broke. That's the property EU AI Act Article 12 reviewers ask about. Also HIPAA §164.312(b). Try it in your browser, no signup.

Authority that can change mid-flight

An analyst leaves on a Friday. Their agent is still 20 minutes into a 40-minute job over your client table. With DataShield, the next checked tool call re-checks against current rights. It fails closed. How Auth does it.

Columns labelled before agents touch them

Classification is not a nice-to-have. Not once an agent can write its own SQL. We label columns against 129 field classes. Verdicts repeat the same way each time. Masking and field-blur are switches you flip per dataset. See the catalog.

Where Datafold is genuinely stronger

Let's be fair about the diff. Comparing two large tables value by value is hard. Across two different database engines. Without dragging both into one place. That's a real engineering problem. Datafold solved it well enough that the phrase "data diff" now belongs to them. They then did the smart commercial thing. They pointed it at migrations. That's where the buyer has budget and fear in equal measure. Migration Agent 2.0 translates the code and refactors it. It builds the test data. It runs both sides. It diffs the output. Their case studies walk through SAP HANA and Talend to Databricks. Also Informatica to dbt Cloud, and Redshift to Snowflake. Ours do not, because we do not do it. They also hold SOC 2, HIPAA and GDPR attestations, which we do not yet. Their VPC setup spans three clouds. It's better packaged than most companies their size can manage.

The push-back is about what happens after the migration lands. Their 2026 pitch is a context layer. It makes coding agents solid, served over MCP. It targets Cursor and Claude Code directly. Search their pages for the words authorization, pull-back, tamper proof or PII. You will come up empty. That is not a gap they failed to fill. It is a job they never took. But it is the job a ruled buyer inherits. The moment those same agents point at production data. A client table sits in it. Gartner expects most not allowed agent transactions through 2028 to be internal rule violations. Not attacks. That is exactly the failure a correctness diff cannot see.

Questions worth asking both of us

These are the questions we would want answered. If we were the ones buying. Ask them on each 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 delete apart from cut-short and from tamper. Run it against a sample chain at /verify. Datafold: we found no open tamper-proof method. Their proof artefact is the diff result. Ask them what stops someone editing the record of who ran it.

What happens to a revoked agent mid-session?

DataShield re-checks rights on each checked call. Pull-back lands on the next call. Not the next token refresh. Datafold's agents run migration and code-review work inside your flow. Their context layer feeds agents you already own. We found no mid-session pull-back method in their public docs. Ask how long a hacked agent keeps working after you pull its access.

How does GDPR erasure interact with the audit trail?

DataShield crypto-shreds per-subject keys and issues an ISO 27560 consent receipt. Actor identities in the chain carry an HMAC commitment. The proof still verifies. Even once the subject is gone. Datafold claims GDPR compliance as a certification. Their public docs do not describe the wipe method. Ask for the method, not the badge.

Is DataShield a data diff or data observability tool? Do we drop Datafold?

No, and no. We have no anomaly detection. No monitors or alerts on warehouse tables. No freshness SLAs. Incident management isn't part of it either. We do ship change and drift detection over checked datasets. It comes with a change classifier and a CDC log. But that is not a cross-database CI diff. If your problem is a pipeline that breaks quietly, buy a reliability tool. Run us where agents read the data. Someone has to answer for it.

Datafold has an MCP server too. What's different?

Theirs, the Data Knowledge Graph, is in Beta. It serves context: lineage, business logic, usage and ontology. A coding agent writing dbt models understands your warehouse because of it. Good design for that job. Ours is where agents query the checked data itself. The tool token carries a scope ceiling. We authorize the call before dispatch. We seal the choice into the chain. Use theirs to help an agent write correct SQL. Use ours to decide if that SQL may run against PHI.

Does DataShield have SOC 2?

Not yet, and we will not imply otherwise. Datafold does, along with HIPAA and GDPR. If that is a procurement gate today, they clear it. We do not. What we offer instead: a public threat model. A checker anyone can run. Design-partner terms include source escrow. That way a small vendor is not a single point of failure. Details on the security page.

Other head-to-heads

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Pipeline incidents, versus proof of what agents did.

 Quality rules

DataShield vs Soda

Checks you write, and the governance underneath them.

 Open source

DataShield vs Great Expectations

Assertions in the pipeline, rights at the tool call.

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Every comparison

One honest scorecard per vendor.

See both mechanisms run in your browser. Break a live audit chain. Revoke an agent mid-session. Then decide what your diff tool still owes you. Demo Center access is free with a work email.

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