Critical Data Manager gives every critical data element one audit-ready view: lineage, quality on each physical instance, ownership, and the status of each required control [6] , [46] . Under BCBS 239, that view is the evidence itself, assembled continuously, not reconstructed under deadline [47] . Brambles uses it across 10 data domains and 60 countries at any given time. When its ERP brought in 300,000 objects, agents linked critical data elements across the catalog, saving hundreds of hours [16] . The difference: Atlan's BCBS 239 explainer maps general catalog features to the principles [29] , but its public documentation shows no critical-data-element capability or BCBS 239 control tracking [34] .

When "revenue" means one thing in Snowflake and another in Databricks, the agent isn't wrong; the enterprise is, and no auditor will accept two answers. Alation brings in the semantic models your BI tools and data platforms already use, keeps one approved definition of each term, and syncs it back to Snowflake and Databricks, so an agent returns the same number wherever it asks [1] , [18] . Every answer names the definition it used, so the number arrives with its explanation attached. [7] The difference: Atlan scopes each Context Repo to a domain or use case, so Finance’s “revenue” and Sales’ “revenue” can both be correct [30] . Alation masters one approved definition per term and syncs it back to Snowflake and Databricks, so every downstream agent runs on that definition [18] .

Governing the data is half the job. AI adds a second thing to govern: the models and agents acting on it. Alation AI Governance registers every model and agent, wherever it was built, governs each by use case against frameworks such as the EU AI Act, NIST AI RMF, and ISO/IEC 42001, and gates approval on verified evidence. Live compliance posture is reported to executives and auditors, so answering a regulator becomes a report you pull, not a project you staff [5] . The difference: Atlan provides an AI asset registry, Jira and ServiceNow approvals, and a Policy Center for risks such as model drift [31] , [35] . Alation ties each approval to a use case, a named regulation, and its evidence [5] .

What is the main difference between Alation and Atlan?

The main difference is scope. Atlan is a context layer that unifies metadata and delivers business context to AI agents, while AIOS governs the data underneath, the context in the middle, and the agents on top as one system. [1] , [27] , [28]

Is Alation or Atlan better?

It depends on how much you need governed. Atlan is a context layer for data engineers and AI platform teams, and governs that layer. [27] , [28] Alation governs data, context, and agents together as one system [1] . It is built for everyone who touches data: engineers and analysts work in their own tools, and stewards, compliance, and executives work without writing code [4] . If you have to prove to a regulator that your data and your AI are under control, that is the scope Alation covers [4] , [5] .

Is Alation just a data catalog?

No. The catalog is one layer of AIOS [14] , which also governs business context, data quality, data products, and the AI agents acting on all of it as a single system [1] . Alation has run governance workflows, policy management, and audit trails in production since 2012 [3] , and AIOS extends that to registering and governing AI models and agents as assets in their own right [5] .

How has Alation changed in the last two years?

Substantially. If you last evaluated Alation before 2025, re-evaluate it. Alation Cloud Service runs as cloud-native SaaS with continuous upgrades [1] . Agents now draft documentation for stewards to approve [17] , Agent Studio builds governed agents with no-code and SDK paths [7] , and AIOS runs data, context, and agents as one system [1] . Connectors keep moving too, such as incremental metadata extraction for Snowflake [26] . Recent reviewers say Alation's AI features have made curation much faster. [69]

Which platform is better for banks and regulated industries?

Alation, if you have to prove compliance rather than assert it. Critical Data Manager gives every critical data element one audit-ready view, covering lineage, quality on each physical instance, documented ownership, and control status, which is the evidence BCBS 239 requires [6] , [47] . Atlan publishes a BCBS 239 explainer mapping general catalog capabilities to the principles [29] ; we found no first-class critical-data-element capability or BCBS 239 control tracking in its public product documentation [34] . Commonwealth Bank of Australia uses Alation as its governance intelligence layer, connecting quality metrics, compliance indicators, critical data elements, and lineage to certify data products at scale across its cloud data ecosystem. [21]

How do Alation and Atlan compare on data lineage?

Both platforms provide end-to-end data lineage [11] , [27] . The difference in Alation is what the lineage is attached to: lineage from source to consumption is tied to each critical data element alongside quality, ownership, and control status, so it functions as audit evidence rather than a diagram [6] . Alation also traces lineage across cloud platforms, on-premises systems, and legacy estates in one view, which matters when the number a regulator asks about originates outside the cloud [11] .

Which platform is better for business users and stewards?

Alation. Stewardship, policy management, approvals, and curation are built for people who don't write code [4] . Atlan positions itself for data engineers and AI platform teams, with approval chains routed through Jira and ServiceNow [27] , [28] , [32] , and G2 reviewers list a steep learning curve for business users among its most common drawbacks [68] . A platform your stewards can't operate themselves becomes a queue in front of your data engineers.

Which platform is easier for a business team to adopt?

It depends on what people find the first time they log in. Catalog programs rarely stall at setup. They stall at filling the catalog: when users find nothing useful or out-of-date content, they stop coming back [48] , [49] . Alation removes that cold start. Agents draft descriptions, classifications, and ownership, and stewards approve or refine them, compressing roughly six weeks of manual curation into 15 minutes [17] . In practice: at Ciena, a small governance team curated about 6,000 assets in roughly two weeks, work it had scoped at 1.5 to 2 years by hand, and catalog users tripled to more than 1,100 [20] . As Ciena's Matt Law put it: "People don't come back to a site they don't use and they're coming back." [20]

Which platform is better for AI agents?

Alation covers all three parts of the agent lifecycle: data, context and AI. Atlan's scope is the middle one. Building agents, feeding them context, and governing what they do are separate problems. Alation ships pre-built agents for documentation, data quality, critical-data identification, and data product creation, plus Agent Studio with no-code paths for business users and an SDK for engineers, and every agent inherits platform access controls and policies by default [7] , [27] . Agents built outside Alation are governed the same way, without being rebuilt: build anywhere, govern here [5] , [7] . Georgia-Pacific deployed eleven AI agents last year on a governed knowledge layer, with more in development [19] .

How should we compare total cost of ownership?

Compare the shape of the cost, because neither vendor publishes list pricing. Ask how many separate tools each approach needs to cover data quality, policy, critical data, and AI governance; which of those are add-ons; which implementation services are priced separately [50] ; and what changes when scope grows from one team to the enterprise. A platform that covers fewer layers is not cheaper if you buy and integrate the rest.

How long does implementation take, and how should we evaluate time to value?

Ask each vendor for two dates: when a team's assets are catalogued and searchable, and when governance is running in production with policy enforced and evidence an auditor will accept. Then ask for a customer who hit both dates at your scale. G2 lists Atlan's typical time to implement as three months [70] . Then ask what the plan is measured against. Alation's outcome-based governance starts from the result that has to be right, whether that's an executive report, a compliance filing, or an agent's answer, and governs everything powering it first, so value shows up where the business is already looking [4] .

Is Alation open, or does it lock you in?

Alation is open by design. More than 120 connectors and open standards, including MCP, OpenLineage, SCIM, SAML, and OAuth, let it read across your stack and push governed context into the tools your teams already use [9] . Through Alation's MCP server, governed context reaches agents on Snowflake Cortex, Databricks Genie, Claude, and other platforms with access controls attached [9] .You can bring any model, including your own, and your data stays where it lives.

Does Alation work with Snowflake and Databricks?

Yes to both. Alation integrates with Snowflake, Databricks, and the modern data stack through more than 120 connectors and open standards including MCP, OpenLineage, OWL, SCIM, SAML, and OAuth [9] . Governed context reaches agents on any platform, including Snowflake Cortex, Databricks Genie, and Claude, with access controls attached. Semantic Model Mastering goes one step further: metric definitions governed in Alation sync back into Snowflake semantic views and Databricks metric views, so Cortex and Genie run on the mastered definition without users ever leaving those platforms [1] . Bring any model, including your own; your data stays where it lives.

Can I migrate from Atlan to Alation?

Yes, and governance assets do not have to be rebuilt from scratch. Alation reads across your existing stack through 120+ connectors and open standards including OpenLineage, OWL, RDF, and SHACL, so assets expressed in those standards, including ontologies and lineage, can be brought across rather than re-authored [9] . Scope platform-specific parts, such as custom workflows and automation written against a vendor API, before you set a cutover date. Alation's Data Catalog Migration Services support moves from other catalog platforms, including Collibra and Informatica.

Evaluating Atlan?

This page covers where the two platforms differ on keeping AI accurate over time, governance depth, and enterprise trust. Statements about Atlan come from Atlan's own public pages and documentation, independent review platforms, and industry sources, all cited and dated in Sources.

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Atlan vs. Alation

The 60-second verdict

Choose Atlan if you're equipping a technical team with a context layer that feeds business context to AI agents, with governance focused on that layer.^[27]^,^[28]

Choose Alation if you need the context layer and the system around it: one platform that governs data, context, and agents together. Engineers use it inside their own tools, and stewards, compliance, and executives use it without writing code. And it can prove to a regulator that both your data and your AI are under control.^[4]^,^[5]

AI Accountability

What happens when the AI is wrong?

Standing up context for AI is where most evaluations start. The harder question arrives a few weeks after deployment: an agent gives a confident, incorrect answer. Was it the data, the context, or the agent itself?

Atlan's answer centers on its context pipeline. Atlan's homepage describes it in four stages: unify metadata into an Enterprise Data Graph, enrich it with Context Agents, certify with domain experts, then activate to agents through MCP, SQL, and APIs, with evals and corrections feeding back into context.^[27]

AIOS treats stale context as one of several ways AI fails. Bad data can reach the agent. The agent can misread context because a definition changed. The agent itself can drift as prompts and tools fall out of step. AIOS shows which one failed, routes the fix to the right layer, and keeps the evidence.^[1] Data quality, governed context, agent governance, and audit-ready proof live in one system instead of one layer plus whatever surrounds it.

In Practice

A revenue metric drifts after a schema change. Triage flags it, the correction routes to the definition that caused it, and every agent using that context inherits the fix.^[1] The audit trail writes itself along the way.

Audit-ready by default: evidence that's assembled before the auditor asks

1 —
THE DATA
Critical Data Manager gives every critical data element one audit-ready view: lineage, quality on each physical instance, ownership, and the status of each required control^[6]^,^[46]. Under BCBS 239, that view is the evidence itself, assembled continuously, not reconstructed under deadline^[47]. Brambles uses it across 10 data domains and 60 countries at any given time. When its ERP brought in 300,000 objects, agents linked critical data elements across the catalog, saving hundreds of hours^[16].
The difference: Atlan's BCBS 239 explainer maps general catalog features to the principles^[29], but its public documentation shows no critical-data-element capability or BCBS 239 control tracking^[34].
2 —
THE CONTEXT
When "revenue" means one thing in Snowflake and another in Databricks, the agent isn't wrong; the enterprise is, and no auditor will accept two answers. Alation brings in the semantic models your BI tools and data platforms already use, keeps one approved definition of each term, and syncs it back to Snowflake and Databricks, so an agent returns the same number wherever it asks^[1]^,^[18]. Every answer names the definition it used, so the number arrives with its explanation attached.^[7]

The difference: Atlan scopes each Context Repo to a domain or use case, so Finance’s “revenue” and Sales’ “revenue” can both be correct^[30]. Alation masters one approved definition per term and syncs it back to Snowflake and Databricks, so every downstream agent runs on that definition^[18].
3 —
THE AGENTS
Governing the data is half the job. AI adds a second thing to govern: the models and agents acting on it. Alation AI Governance registers every model and agent, wherever it was built, governs each by use case against frameworks such as the EU AI Act, NIST AI RMF, and ISO/IEC 42001, and gates approval on verified evidence. Live compliance posture is reported to executives and auditors, so answering a regulator becomes a report you pull, not a project you staff^[5].

The difference: Atlan provides an AI asset registry, Jira and ServiceNow approvals, and a Policy Center for risks such as model drift^[31]^,^[35]. Alation ties each approval to a use case, a named regulation, and its evidence^[5].

Product Capabilities

How do Alation and Atlan compare?

Criteria	Alation	Atlan
Who operates governance day to day
Primary audience	Everyone who touches data. Engineers and analysts work in their own tools; stewards, compliance, and executives work in no-code workflows^[4]	Data engineers and AI platform teams, per Atlan's own positioning^[27]^,^[28]. G2 reviewers cite a steep learning curve for business users among Atlan's most common drawbacks^[68]
Governance workflowS	No-code stewardship, policy, approvals, and curation for business users, all automatable by engineers through the Python SDK, REST APIs, and Alation's MCP Server^[4]	Approval chains routed through Jira and ServiceNow^[32]^,^[34]
Curation at scale	AI-powered curation across the full data estate: agents draft, stewards approve or refine.^[8]^,^[17] Pointed at a raw SAP source, it turns tens of thousands of cryptic, abbreviated names into plain-language descriptions^[8]	Context Agents auto-generate documentation^[27]; business-context curation is user-driven^[34]
Openness and developer access	120+ connectors; open standards including MCP, OSI, OpenLineage, OWL, RDF, SHACL, SCIM, SAML, OAuth, OAS, and ODPS; a developer platform with REST APIs, Python SDK, AI Agent SDK, Data Quality SDK, and MCP Server. Any model, including your own^[9]^,^[23]	API-first platform; context delivered to agents through MCP, SQL, and APIs^[27]
Governing the Data
Critical data elements	Critical Data Manager: agent-assisted identification, automated mapping, continuous quality monitoring, end-to-end lineage, and audit-ready evidence^[6]	No named, first-class Critical Data Element capability found in Atlan's public product documentation^[34]
Data quality	Native checks pushed down into ten platforms, including Snowflake, Databricks, BigQuery, Redshift, Oracle, and SAP HANA, with ML anomaly detection on row count, freshness, and schema drift^[10]^,^[24]^,^[25]	Data Quality Studio covers BigQuery, Databricks, and Snowflake; ML anomaly detection is Snowflake-only and limited to row count and freshness^[38]^,^[39]
Lineage	End-to-end lineage from source to consumption, spanning cloud, on-premises, and legacy systems in one view^[11]	Data lineage across the connected estate^[27]
Semantics	Brings in semantic models from your BI tools and data platforms, governs each definition once, and syncs it back to Snowflake and Databricks^[1]^,^[18]	Context Engineering Studio scopes each Context Repo to a domain, so Finance's "revenue" and Sales' "revenue" can both be correct.^[30]
Ontologies	Drafted by agents from your policies, bound to your tables as a knowledge graph, with rules that can drive data quality checks. Open standards, shared over MCP^[18]	Generated by Context Agents. Scoped to individual domains through Context Repos. Extracting ontologies from documents is described as the intended direction^[44]
Documents and unstructured knowledge	Governed Collections bring live documents in SharePoint, Confluence, and S3 under governance without copying them, with the same trust signals as structured data^[18]	Admins upload documents into Atlan as a curated layer^[44]^,^[45]
Data products	A certified unit of AI context: governed data with business definitions, quality, ownership, and contracts, published once for people in the Marketplace and agents over MCP; nothing is listed until it clears an approval^[12]^,^[13]	Data products built in the products module and published to the Data Marketplace; scored on fixed criteria customers can't configure, and data contracts are in private preview^[32]^,^[41]^,^[42]^,^[43]
Policy management	Policies attached to the data they cover, enforced whether a person or an agent is asking. AI suggests which policies apply where, no coding needed^[4]	Policy Center offers six policy types and requires the governance workflows and inbox module as a prerequisite^[40]
Governing AI and agents
AI and agent governance	Every agent and model registered as a governed asset, governed by use case, mapped to citable regulations, and gated on verified evidence. Live compliance posture reported to executives and auditors^[5]	An AI asset registry with workflows via Jira and ServiceNow, a Policy Center for risks such as model drift and ethical AI, and a compliance-readiness capability^[31]^,^[35]
AI agents in the product	Pre-built, custom, and flow-orchestrated agents for documentation, data quality, critical-data identification, and data product creation, plus Agent Studio. Every agent inherits platform access controls and policies^[7]	Context Agents generate and deliver context to AI tools^[27]
Keeping answers right over time	Corrections route back to the data, context, or agent that caused the error, and the fix improves every app sharing that context. Built-in evaluations flag drift before users see it^[1]	Automated context evals and feedback-driven context updates; correction routing is scoped to context for AI answers^[30]^,^[33]
Security and deployment
Deployment models	Cloud-native SaaS on Alation Cloud Service, with continuous upgrades; hybrid with a customer-hosted agent; and a fully customer-managed option for strict sovereignty requirements^[1]	Cloud SaaS and hybrid with a customer-hosted agent^[36]
Security and compliance	SOC 2 Type 2 (including HIPAA and HITECH), ISO 27001, ISO 27701, and the cloud standards ISO 27017 and ISO 27018^[15]	SOC 2 Type II, ISO 27001, ISO 27701, and ISO/IEC 42001; HIPAA-aligned^[37]

The Analyst View

Analyst Recognition

Both Alation and Atlan are named Leaders. Analyst evaluations offer a snapshot of current capabilities, but it's also important to consider what each is building toward.

Recognition	Alation	Atlan
Gartner® Magic Quadrant™ for Metadata Management Solutions	Leader 5x: 2017, 2018, 2019, 2020, 2025^[51]^,^[52]^,^[53]^,^[54]	Leader 1x: 2025^[54]^,^[55]
Gartner® Magic Quadrant™ for Data & Analytics Governance Platforms, published 6 January 2026	Leader^[56]	Leader^[57]
The Forrester Wave™	Leader 5x: Machine Learning Data Catalogs Q2 2018^[58]; Machine Learning Data Catalogs Q4 2020^[59]; Data Governance Solutions Q3 2021^[60]; Q3 2023^[61]; Q3 2025^[62]	Leader 3x: Enterprise Data Catalogs for DataOps Q2 2022^[63]; Enterprise Data Catalogs Q3 2024^[64]; Data Governance Solutions Q3 2025^[65]

Customer Results

Stage	Industry		Result		What it proves
Georgia-Pacific	Manufacturing	→	About $25 million in intercompany transfers in one year, enabled by a trusted data platform that includes Alation as a critical component^[19]	→	Business outcomes from trusted data
Ciena	Technology	→	About 6,000 assets curated in roughly two weeks with Curation Automation, and catalog users up 3x^[20]	→	Curation at scale; adoption
Brambles	Supply chain	→	Critical data governed with Critical Data Manager across 10 domains and 60 countries^[16]	→	Critical data governance at global scale
Commonwealth Bank of Australia	Banking	→	Alation is the bank's governance intelligence layer, used to certify data products at scale^[21]	→	Regulated governance; data products

What customers say

Alation held a 4.7 out of 5 rating on Gartner Peer Insights in Metadata Management Solutions based on 44 verified reviews, more reviews than any other vendor in the market as of 19 November 2025^[51]. Alation has been named a Gartner Peer Insights Customers' Choice three times, most recently in both Metadata Management Solutions and Data & Analytics Governance Platforms in 2026^[66]^,^[67].

Frequently asked questions

Q1 —
What is the main difference between Alation and Atlan?
The main difference is scope. Atlan is a context layer that unifies metadata and delivers business context to AI agents, while AIOS governs the data underneath, the context in the middle, and the agents on top as one system.^[1]^,^[27]^,^[28]
Q2 —
Is Alation or Atlan better?
It depends on how much you need governed. Atlan is a context layer for data engineers and AI platform teams, and governs that layer.^[27]^,^[28] Alation governs data, context, and agents together as one system^[1]. It is built for everyone who touches data: engineers and analysts work in their own tools, and stewards, compliance, and executives work without writing code^[4]. If you have to prove to a regulator that your data and your AI are under control, that is the scope Alation covers^[4]^,^[5].
Q3 —
Is Alation just a data catalog?
No. The catalog is one layer of AIOS^[14], which also governs business context, data quality, data products, and the AI agents acting on all of it as a single system^[1]. Alation has run governance workflows, policy management, and audit trails in production since 2012^[3], and AIOS extends that to registering and governing AI models and agents as assets in their own right^[5].
Q4 —
How has Alation changed in the last two years?
Substantially. If you last evaluated Alation before 2025, re-evaluate it. Alation Cloud Service runs as cloud-native SaaS with continuous upgrades^[1]. Agents now draft documentation for stewards to approve^[17], Agent Studio builds governed agents with no-code and SDK paths^[7], and AIOS runs data, context, and agents as one system^[1]. Connectors keep moving too, such as incremental metadata extraction for Snowflake^[26]. Recent reviewers say Alation's AI features have made curation much faster.^[69]
Q5 —
Which platform is better for banks and regulated industries?
Alation, if you have to prove compliance rather than assert it. Critical Data Manager gives every critical data element one audit-ready view, covering lineage, quality on each physical instance, documented ownership, and control status, which is the evidence BCBS 239 requires^[6]^,^[47]. Atlan publishes a BCBS 239 explainer mapping general catalog capabilities to the principles^[29]; we found no first-class critical-data-element capability or BCBS 239 control tracking in its public product documentation^[34]. Commonwealth Bank of Australia uses Alation as its governance intelligence layer, connecting quality metrics, compliance indicators, critical data elements, and lineage to certify data products at scale across its cloud data ecosystem.^[21]
Q6 —
How do Alation and Atlan compare on data lineage?
Both platforms provide end-to-end data lineage^[11]^,^[27]. The difference in Alation is what the lineage is attached to: lineage from source to consumption is tied to each critical data element alongside quality, ownership, and control status, so it functions as audit evidence rather than a diagram^[6]. Alation also traces lineage across cloud platforms, on-premises systems, and legacy estates in one view, which matters when the number a regulator asks about originates outside the cloud^[11].
Q7 —
Which platform is better for business users and stewards?
Alation. Stewardship, policy management, approvals, and curation are built for people who don't write code^[4]. Atlan positions itself for data engineers and AI platform teams, with approval chains routed through Jira and ServiceNow^[27]^,^[28]^,^[32], and G2 reviewers list a steep learning curve for business users among its most common drawbacks^[68]. A platform your stewards can't operate themselves becomes a queue in front of your data engineers.
Q8 —
Which platform is easier for a business team to adopt?
It depends on what people find the first time they log in. Catalog programs rarely stall at setup. They stall at filling the catalog: when users find nothing useful or out-of-date content, they stop coming back^[48]^,^[49]. Alation removes that cold start. Agents draft descriptions, classifications, and ownership, and stewards approve or refine them, compressing roughly six weeks of manual curation into 15 minutes^[17]. In practice: at Ciena, a small governance team curated about 6,000 assets in roughly two weeks, work it had scoped at 1.5 to 2 years by hand, and catalog users tripled to more than 1,100^[20]. As Ciena's Matt Law put it: "People don't come back to a site they don't use and they're coming back."^[20]
Q9 —
Which platform is better for AI agents?
Alation covers all three parts of the agent lifecycle: data, context and AI. Atlan's scope is the middle one. Building agents, feeding them context, and governing what they do are separate problems. Alation ships pre-built agents for documentation, data quality, critical-data identification, and data product creation, plus Agent Studio with no-code paths for business users and an SDK for engineers, and every agent inherits platform access controls and policies by default^[7]^,^[27]. Agents built outside Alation are governed the same way, without being rebuilt: build anywhere, govern here^[5]^,^[7]. Georgia-Pacific deployed eleven AI agents last year on a governed knowledge layer, with more in development^[19].
Q10 —
How should we compare total cost of ownership?
Compare the shape of the cost, because neither vendor publishes list pricing. Ask how many separate tools each approach needs to cover data quality, policy, critical data, and AI governance; which of those are add-ons; which implementation services are priced separately^[50]; and what changes when scope grows from one team to the enterprise. A platform that covers fewer layers is not cheaper if you buy and integrate the rest.
Q11 —
How long does implementation take, and how should we evaluate time to value?
Ask each vendor for two dates: when a team's assets are catalogued and searchable, and when governance is running in production with policy enforced and evidence an auditor will accept. Then ask for a customer who hit both dates at your scale. G2 lists Atlan's typical time to implement as three months^[70]. Then ask what the plan is measured against. Alation's outcome-based governance starts from the result that has to be right, whether that's an executive report, a compliance filing, or an agent's answer, and governs everything powering it first, so value shows up where the business is already looking^[4].
Q12 —
Is Alation open, or does it lock you in?
Alation is open by design. More than 120 connectors and open standards, including MCP, OpenLineage, SCIM, SAML, and OAuth, let it read across your stack and push governed context into the tools your teams already use^[9]. Through Alation's MCP server, governed context reaches agents on Snowflake Cortex, Databricks Genie, Claude, and other platforms with access controls attached^[9].You can bring any model, including your own, and your data stays where it lives.
Q13 —
Does Alation work with Snowflake and Databricks?
Yes to both. Alation integrates with Snowflake, Databricks, and the modern data stack through more than 120 connectors and open standards including MCP, OpenLineage, OWL, SCIM, SAML, and OAuth^[9]. Governed context reaches agents on any platform, including Snowflake Cortex, Databricks Genie, and Claude, with access controls attached. Semantic Model Mastering goes one step further: metric definitions governed in Alation sync back into Snowflake semantic views and Databricks metric views, so Cortex and Genie run on the mastered definition without users ever leaving those platforms^[1]. Bring any model, including your own; your data stays where it lives.
Q14 —
Can I migrate from Atlan to Alation?
Yes, and governance assets do not have to be rebuilt from scratch. Alation reads across your existing stack through 120+ connectors and open standards including OpenLineage, OWL, RDF, and SHACL, so assets expressed in those standards, including ontologies and lineage, can be brought across rather than re-authored^[9]. Scope platform-specific parts, such as custom workflows and automation written against a vendor API, before you set a cutover date. Alation's Data Catalog Migration Services support moves from other catalog platforms, including Collibra and Informatica.

Bottom Line

The bottom line

Atlan is a context layer for delivering enterprise context to AI agents^[27]^,^[28]. If that is the whole requirement, that is the scope it covers. Most enterprises need the answer to hold up after the demo, with the business, the board, and the regulator. That takes one system that keeps data, context, and agents in sync, catches the confident wrong answer, and has the evidence ready when someone asks. Alation built AIOS for exactly that.

Let us help you get it right.