Integrius vs Atlan: Active Metadata vs Active Enforcement

What Atlan gets right

Atlan is the catalogue that data teams choose for themselves, and that fact alone tells you something. Legacy governance tools were bought by compliance departments and tolerated by engineers. Atlan flipped that: it is collaborative, quick to deploy, pleasant to use, and built around the tools modern data teams already live in.

The integrations are the heart of it. Atlan plugs into the modern data stack, Snowflake, dbt, Looker, Slack and the rest, and pulls metadata together into a shared workspace. dbt models show up with their documentation and lineage. Slack threads about a dataset can live next to the dataset. Column-level lineage traces a metric back through the transformation layer. Context that used to be scattered across five tools and three people's heads gets a home.

Atlan's pitch is "active metadata": metadata that does not just sit in a catalogue but flows back into the tools where work happens, triggering notifications, annotating BI dashboards, flagging downstream impact when a dbt model changes. Compared with legacy catalogues that demanded months of implementation before anyone saw value, Atlan's time to value is genuinely fast, and its user experience is genuinely good.

If the job is "help a fast-moving data team find, understand, and trust their data," Atlan does that job well. We are not going to pretend otherwise, because the comparison only matters if it is honest. The question is what happens when the job changes from understanding data to controlling it.

Active metadata still is not enforcement

Here is the structural point, and it applies to Atlan just as it applies to Alation, Collibra, and every other catalogue: metadata about your data is not control over your data.

Atlan can tell you a column contains PII. It can notify the owner in Slack when someone requests access. It can show you every dashboard that depends on a table before you change it. All of that is useful. None of it sits between a consumer and the data. When an analyst with a sufficiently broad Snowflake role queries the customer table, Atlan is not in that path. The query runs. The catalogue may know the policy; the warehouse executes the query; and the connection between the two is whatever glue your platform team has built and maintains by hand.

This is governance as advice. Good advice, delivered in the right channel at the right moment, but advice. The enforcement still lives in warehouse grants, masking policies, and access tooling that are configured separately and drift separately. The catalogue and the controls are two systems, and every gap between them is a finding waiting for an auditor.

There is a second structural issue: Atlan is SaaS. Your metadata, which is a detailed map of your data estate, its owners, its sensitive columns, and its lineage, lives in a vendor's cloud. For most companies that is an acceptable trade for the convenience. For organisations in pharma, defence, banking, or anywhere with hard data residency rules, it can be disqualifying on its own, before the architecture discussion even starts. We have written about why self-hosted data governance is a hard requirement in those environments rather than a preference.

Describing data vs serving it

The deeper difference between Integrius and Atlan is not a feature gap. It is what kind of thing each one is.

Atlan is a description layer. It observes your stack, harvests metadata, and makes that metadata useful. Its accuracy depends on crawlers and integrations keeping up with a moving estate, and its influence on behaviour depends on humans reading what it shows them.

Integrius is a runtime. It does not describe a serving layer that exists elsewhere; it is the serving layer. Data products in Integrius are not catalogue entries pointing at warehouse tables. They are live, governed endpoints, and the metadata you see, the schema, the owner, the access rules, the lineage, is the configuration of the thing doing the serving. It cannot drift from reality, because it is the reality.

That distinction sounds abstract until you put concrete questions against it:

• "Can this contractor see customer emails?" In Atlan: check the classification, then check the warehouse grants, then hope they agree. In Integrius: the access rule on the data product is the answer, because the API enforces it.
• "Prove nobody outside finance accessed revenue data this quarter." In Atlan: assemble warehouse query logs and cross-reference roles. In Integrius: query the tamper-evident audit chain, which recorded every access, append-only and hash-chained.
• "What breaks if we drop this field?" Atlan's lineage will show you affected assets, and that is genuinely useful. Integrius's blast radius analysis shows affected downstream products and consumers too, with one difference: the consumers it lists are integrating against a versioned API contract Integrius itself serves, so the answer is complete by construction.

How Integrius works

Integrius is a self-hosted data product platform. It deploys entirely inside your infrastructure, air-gap capable, with zero SaaS dependencies and no phone-home. It connects to your sources through 16 connector types, PostgreSQL, MySQL, MSSQL, Snowflake, BigQuery, Redshift, MongoDB, Salesforce, Kafka, S3, REST, GraphQL, CSV, Excel, JSON and event logs, and turns fragmented data into governed data products.

Each product gets an accountable owner, one stable versioned API endpoint per business concept, and enforced access control: RBAC with 4 built-in roles and 24 granular permissions, applied at the moment of serving. If access is not approved, the call does not return data. Every access and every change lands in an append-only audit chain, hash-chained with HMAC or Ed25519, so the record is tamper-evident. For regulated work, Integrius supports 21 CFR Part 11 e-signatures, enforces ALCOA+, provides GDPR atomic erasure, and maps controls to HIPAA, SOX, FISMA and NIST 800-53.

Consistency across the organisation comes from Standard Fields, a canonical schema with approval-workflow field mappings, so the same business concept means the same thing everywhere. Products compose into other products, with entity-keyed joins across sources resolved in real time, and materialised snapshots served at sub-50ms p95. The point is to collapse the N x M integration mess into one governed product per business concept, with many consumers on a stable contract.

On top of the serving layer sit Optic, an AI analytics layer that answers plain-English questions with inference running on-prem via Ollama and RBAC enforced upstream of the model, and Search, federated search across your products without a separate index to maintain.

And because every product carries its documentation, schema, owner and lineage as live configuration, Integrius gives you the discovery experience a catalogue promises, generated from the system that serves the data rather than crawled after the fact.

It is worth pausing on what this means for compliance work specifically. In a catalogue-plus-warehouse architecture, an audit is an assembly job: export the catalogue's classifications, export the warehouse grants, export the query history, and build a narrative that convinces an auditor the three artefacts describe the same world. In Integrius, the policy, the enforcement, and the evidence are one system, so the narrative writes itself: here is the rule, here is the endpoint that enforced it, here is the hash-chained record of every call. Teams that have lived through both kinds of audit rarely need the difference explained twice.

Feature comparison

Pricing

Atlan does not publish list pricing. Like most modern SaaS catalogues it is sold on negotiated enterprise contracts, and you should expect pricing conversations to scale with users and the size of your estate. By catalogue standards its time to value is good, which matters because slow catalogue rollouts are where budgets usually die.

Integrius publishes its pricing and prices on one axis: governed data products in production. Not seats, not connectors, not rows.

• Pilot: EUR 5,000 per month, up to 20 products, Optic lite included.
• Enterprise: EUR 18,000 per month, up to 50 products.
• Platform Lite: EUR 22,000 per month, up to 75 products.
• Platform: EUR 320,000 per year for 100+ products, Search and Optic included.

Search and Optic are EUR 100k per year add-ons on the mid tiers, and all 16 connector types are included everywhere. The model is deliberately boring: the thing you pay for is the thing that delivers value, a governed product in production. It also means adding consumers or moving more data through an existing product costs nothing extra, which is the opposite of the consumption treadmill we described in Integrius vs Fivetran.

When to choose Atlan

Plainly, because this comparison is only worth reading if it is fair:

Choose Atlan if your problem is discovery and context, not control. If your team's pain is "we cannot find our data, we do not know what this column means, and tribal knowledge lives in Slack," Atlan attacks exactly that, quickly, with a product people enjoy using. Integrius gives you discovery over the products it serves; it does not try to catalogue every dashboard, every dbt model, and every ad hoc table across your estate. Atlan does.

Choose Atlan if you are deeply invested in the modern data stack and want metadata woven through it. Its dbt, Snowflake and BI integrations are a core strength, and if your governance posture is "informed humans making good decisions," Atlan makes humans better informed with less friction than almost anything else.

Choose Atlan if SaaS is fine for your risk profile and you want value in weeks without owning new infrastructure. Integrius is self-hosted by design; that is a feature for regulated environments and a cost for teams who just want a hosted tool.

And honestly, the two can coexist: Atlan as the estate-wide map and collaboration layer, Integrius as the governed runtime for the data that actually gets served to consumers and auditors. The pattern is the same one we describe in Integrius vs Collibra: the catalogue indexes everything, the runtime governs the subset that carries real regulatory or commercial weight, and the catalogue entries for that subset are the rare ones guaranteed to be accurate, because they describe endpoints whose configuration is the documentation.

Decision framework

The fork in the road is one question: do you need governance that informs people, or governance that stops things?

If a sensible, well-informed team is enough, if your regulators accept documented policies and your security model tolerates broad warehouse roles, then a modern catalogue is the right spend, and Atlan is one of the best. If you need to prove enforcement, to show an auditor a tamper-evident record of every access, to guarantee a contractor cannot see a PII column no matter which role got fat over the years, then metadata is not the layer that can do it. Only the serving path can, and that is the layer Integrius occupies.

Atlan makes your data understood. Integrius makes it governed, served, and provable. Pick based on which failure keeps you up at night: people not knowing, or systems not stopping.

See what enforced governance looks like in practice. Explore Integrius Core or read the technical brief.