Head-to-head · updated 13 September 2026
DataShield vs Great Expectations: the tests passed, so who let the agent read the table?
Great Expectations wrote the language. Expectations, Expectation Suites, Checkpoints, Data Docs. Have you ever asserted a column is never null? Did the pipeline stop when it was? Then you've used the idea GX made normal. GX Core is free. Apache-2.0. It runs where your data already runs. Their own hero calls it "the most popular data quality framework in the world". More than 11,000 users back that up. We won't pick a fight with a good tool.
We do not write Expectations. DataShield is not a rules engine. Keep GX Core if rules are what you need. We work one layer over: the dataset itself. Each dataset gets a 20-section profile. Plus a trust score. We classify sensitive fields against 129 field classes. We tokenize datasets at ingest. Agents query tokenized data over MCP. Detokenization needs a privileged step. We audit it, always. We seal each checked tool call into a hash chain. You can verify it without trusting us. Below is the honest split. It includes the rows GX wins.
The short version
Pick DataShield when
- You want to know what is in a dataset, not just whether it passes. Profiling, 129-class PII and PHI classification, and a trust score per domain. No assertion needed. How Ontology does it.
- Agents read your data, and somebody will eventually ask you to prove an access log was not edited. Our chain answers with math. Run the verifier.
- You need to pull an agent's access mid-session. The very next tool call should fail. Not the next token refresh. How Auth does it.
- You liked GX Cloud, but you want the whole thing on your own infrastructure, run by a vendor with a public price.
Pick Great Expectations when
- You want declarative tests in your pipeline code. That's the job GX Core was built for. We do not do it at all.
- Your checks have to run where your data runs: pandas, Spark, SQL, wired into Airflow next to the rest of your DAG.
- Free matters. GX Core is Apache-2.0 with no feature limits. Fivetran says it stays that way.
- Your team already speaks Expectations. A shared language across eleven thousand users is worth real money.
Bottom line: GX Core tests the shape of your data. DataShield governs the data itself, and keeps proof of what agents did with it. Run both, if you like. But were you a GX Cloud customer? The SaaS went away on 1 June 2026. The roadmap now lives at two other companies.
Feature by feature
Competitor cells describe what Great Expectations, FICO and Fivetran have posted. We use the date above. Did we get something wrong? Email support@myorg.ai and we'll correct it, credited.
| What matters | DataShield | Great Expectations | Edge |
|---|---|---|---|
| Data quality rules, tests and alerts | None. We do not author Expectations. No rules DSL. No anomaly detection. Warehouse tables get no monitors. Freshness SLAs are absent. Incident management is not part of it. Want tests? Keep GX Core. | The whole product, and it's good. Declarative Expectations in Python or YAML. Grouped into Suites. Run as Checkpoints. Rendered as Data Docs. Multi-engine: pandas, Spark, SQL. Email alerts on failures shipped in March 2026. | ◇ |
| Dataset profiling and a trust score | Each dataset gets a 20-section analysis profile. Completeness. Field statistics. Patterns. Column semantics. Relationship graph. Quality metrics. Compliance governance. Transformation lineage. Business rules. A source fingerprint. A weighted composite trust score rolls up per entity type, domain and estate. We recompute it hourly, with per-axis breakdown and trend. | Profiling exists to help draft Expectations. ExpectAI generates them from profiling plus business context. The output is a test suite. Not a standing profile with a score you can trend. | ◆ |
| PII and PHI classification | 129 field classes cover PII, PHI, financial data and secrets. Verdicts stay deterministic and reproducible. All 18 HIPAA Safe Harbor identifiers are discrete classes. Each carries a regulation reference. We also fingerprint the source system behind a dataset, from 95 built-ins. | Not a feature. Expectations test shape, range, uniqueness and completeness. Nothing in their public docs classifies a column as sensitive. | ◆ |
| Tokenization and generalization | 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 gets a measured cardinality-reduction score. Both are switches you turn on, not defaults. | Not part of the product. | ◆ |
| Audit evidence | SHA-256 hash chain with Ed25519-signed checkpoints, themselves chained. The verdict is three-valued: clean, attested damage, tampered. Verification names the failure: tampering, insertion, deletion or truncation. Try the verifier. | Data Docs is an HTML report. GX added Expectation and Data Asset history in April 2026. Useful for debugging, sure. We found no cryptographic tamper evidence, though. Not in their public docs. | ◆ |
| Agent authorization | Each checked tool call passes a scope ceiling. Plus a consented-tool allowlist. An access tier stacks on top. A revocation re-check runs before dispatch. The call fails closed. Delegation is RFC 8693 token exchange with an enforced scope ceiling. | No access control plane. ExpectAI runs in the buyer's own Agent deployment. On the buyer's own OpenAI credentials. Sensible design. But it governs an LLM key, not an agent's access over data. | ◆ |
| Break-glass | Scoped, time-boxed emergency access for agents. It auto-revokes. Step-up and IP gated, too. It can't be quietly deleted from the log. | Not described in their public docs. | ◆ |
| GDPR erasure | Crypto-shred of per-subject key data. Plus ISO 27560 consent receipts. HMAC commitments stand in for actor identities in the chain. So the evidence still checks out after the subject is gone. | Out of scope for a test tool. They do not claim it. | ◆ |
| MCP and agents | More than 200 MCP tools across Ontology, Auth, Corpus and Lighthouse, all our own. Auth issues MCP tool tokens with scope ceilings. Per-call metering ties back to the agent. | No MCP server of their own. gx-mcp-server exposes GX checks as MCP tools. One solo coder maintains it, under MIT. Not GX itself. | ◆ |
| Deployment and hosting | Self-hosted, in your own cloud or data center, your call. Or a dedicated single-tenant server we run. Docker images ship for Auth, Ontology, Corpus and Lighthouse. Guardian checks a signed deploy manifest. 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 environment today. Not in a KMS. | GX Core is a Python library you host yourself. About as portable as it gets. GX Cloud was a SaaS control plane with a self-hosted Agent deployment. It stopped being publicly available on 1 June 2026. | ◈ |
| Maturity and ownership | One company, one roadmap. 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. $65M raised. 11,000-plus users. As of May 2026, GX Cloud belongs to FICO. Fivetran now stewards GX Core, itself mid-merger with dbt Labs. The code is in safe hands. The product line isn't one product line any more. | ◇ |
| Pricing | Published model and a scoped instant quote, no sales wall. | GX Core is free under Apache-2.0, with no feature limits. GX Cloud named Developer, Team and Enterprise tiers. They never posted a dollar figure for the paid ones. Free is hard to beat. We won't pretend otherwise. | ◇ |
◆ DataShield leads◇ Great Expectations leads◈ comparable
Great Expectations claims come from greatexpectations.io. Plus the GX blog and Fivetran's press release. We checked them on 13 September 2026. We link them below, not just a paraphrase from memory.
Three things you get here that you won't get from a test framework
The data typed, not just tested
A passing test tells you a column is well formed. It doesn't tell you the column is a patient MRN. We label columns against 129 field classes. The verdict is deterministic. So two runs always agree. All 18 HIPAA Safe Harbor identifiers are in there. See the catalog.
Proof that survives an audit
An HTML report proves very little. Anyone with write access can edit it. Ours is a hash chain with signed checkpoints. The verifier tells you what broke. Not just that it did. That's the property EU AI Act Article 12 and HIPAA §164.312(b) reviewers ask about. 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. With DataShield, we re-check the next checked tool call. Against current access, each time. It fails closed. A test suite has no opinion about this at all. How Auth does it.
Where Great Expectations is genuinely stronger
Expectations are a good idea, executed well, for nine years. Write expect_column_values_to_not_be_null. Have it fail your Airflow run. You've done more than most governance decks manage. GX Core is free. Apache-2.0. No feature limits. It runs on pandas, Spark and SQL. So your checks live next to your code. Not in somebody's console. Data Docs turns a test run into a report a stakeholder can read. Eleven thousand users share the same words. Your next hire already knows it. We have no equal here. We aren't building one. Is the gap in your stack about checks? Install GX Core this afternoon.
Here is the push-back, though. It's about the last two years. Not the code itself. GX's own 2026 posts moved fast. From "data quality" to "trust" to "Trustworthy AI Agents: The Data Quality Pillar of the AI Context Layer". In about four months. That's the right instinct. A test suite helps decide whether an agent should be trusted with a table. Genuinely. But it's the smallest part. The tool has no way to say which columns hold PHI. No way to stop a specific agent reading them. No artefact an examiner can verify on its own. Then, in May 2026, FICO bought the commercial product. Shut down for public use on 1 June. The open-source project moved to Fivetran.
Were you buying GX Cloud to solve agent trust? The thing you were buying is now two things. Owned by two other companies. Neither one is a data governance vendor.
Questions worth asking both of us
These are the questions we would want answered if we were the ones buying. Ask them on every call, ours included.
Can you cryptographically prove an audit log entry wasn't deleted?
DataShield: yes, it can. Each record commits to the one before it. Checkpoints are Ed25519-signed and chained. The verdict separates deletion from truncation and from tampering. Run it against a sample chain at /verify. Great Expectations: Data Docs is a static HTML report. The newer history view is a UI over run records. We found no tamper-evidence mechanism, though. Not in their public docs. Ask them to show one.
What happens to a revoked agent mid-session?
DataShield re-checks access on each checked tool call. Revocation lands on the next call, not the next token refresh. Great Expectations has no access control plane. So there's nothing to revoke. That's not a criticism of a test tool. It's a reason not to buy one for this job.
How does GDPR erasure interact with the audit trail?
DataShield crypto-shreds the per-subject key data. Then it issues an ISO 27560 consent receipt. HMAC commitments stand in for actor identities in the chain. So the evidence still checks out once the subject is gone. GX doesn't claim an erasure mechanism. A validation run that captured example failing values is worth checking, though. Ask where those sample values sit. And for how long.
GX Cloud shut down on 1 June 2026. What are my options?
Three, honestly. Keep GX Core. It's free, and now stewarded by Fivetran. Run it yourself. Talk to FICO. They bought GX Cloud to fold into their Decision Intelligence platform. Or move the checked-data job to a stack you host. We are the third option. We're only right if your real problem is classification, agent access and evidence. Not checks. Is it checks? Option one is free, and we'll say so on the call.
Is DataShield a Great Expectations alternative for data quality testing?
Not for testing, no. We have no rules engine. No anomaly detection. No alerting. We are an alternative for a different part of the job. The part GX Cloud was starting to reach for. Profiling a dataset. Scoring its trust. Labelling its sensitive fields. Deciding what an agent may do with it. Most teams that buy us for that keep their test tool, too. Ours is the layer under it.
Great Expectations has an MCP server. Don't you both do agents?
Theirs is gx-mcp-server, an MIT project by one solo coder. Not a GX product. It lets an agent run validations, which is useful. Ours queries the checked data itself. The tool token carries a scope ceiling. We authorize the call before dispatch. We seal the decision into the chain. Different jobs. Ask GX whether their MCP story is their own, and supported. Today, it is not.
Does DataShield have SOC 2?
Not yet. We won't imply otherwise, ever. Auth ships with a public threat model and a verifier anyone can run. Guardian and Lighthouse have run since April 2026. Design-partner terms include source escrow. That way a small vendor isn't a single point of failure. Details on the security page.
- GX's hero: "A powerful, flexible data quality solution… the most popular data quality framework in the world." Community: "more than 11,000 data practitioners." GX Core ships under Apache-2.0. - greatexpectations.io, 13 Sep 2026
- "Great Expectations (GX) and Fair Isaac Corporation (FICO) have completed a definitive agreement to acquire GX Cloud", and "GX Cloud will no longer be publicly available beginning June 1." - Great Expectations blog, 6 May 2026
- "GX Core will continue as an open source, community-driven project. Fivetran intends to support ongoing project maintenance, ecosystem integrations, and community engagement." - Fivetran press release, 13 May 2026
- ExpectAI runs in a self-hosted Agent deployment. On the customer's own OpenAI credentials. Per GX: "GX Cloud still cannot connect to or extract your underlying data." - Great Expectations blog, 24 Feb 2026
- GX Cloud listed Developer, Team and Enterprise tiers with no dollar figures published for the paid options. - greatexpectations.io/pricing, 13 Sep 2026
- gx-mcp-server "exposes Great Expectations data-quality checks as MCP tools for LLM agents." It's an MIT-licensed project. Maintained by an independent developer, not an official GX repository. - GitHub, 13 Sep 2026
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