Atlan began as a data catalog and now positions itself as "the context layer for AI": an enterprise data graph of metadata, lineage, business glossary and policies, built from 80+ connectors, that AI agents can query through an MCP server.
Product Overview
Its framing matches the RevOps trust problem: enterprise AI fails because of missing context, not the model. Atlan pulls metadata from warehouses, BI tools and business systems including Salesforce and HubSpot, uses context agents to draft descriptions, metrics and a business ontology, and stores it in an Iceberg-native context lakehouse. For revenue teams, it's where an agreed definition of churn or ARR can be owned, linked to its source tables and inherited by any agent. It's a data-team platform; RevOps typically contributes definitions rather than running it.
RevOps Jobs-to-Be-Done
- Publish one glossary of revenue terms — Define ARR, churn and pipeline once, link each term to the tables and dashboards that implement it, and name an owner. KPI: Agents and analysts use the same definition of each metric.
- Trace a board number to source — Column-level lineage follows a figure from the dashboard back through dbt models to the CRM field. KPI: Faster answers when a number is challenged.
- Ground enterprise agents — Serve definitions, lineage and access policies to Claude, ChatGPT or internal agents over MCP. KPI: Agents inherit governance along with the data.
Key Features
- Enterprise data graph: Unified metadata from 80+ connectors across data, BI and business systems.
- Lineage and catalog: End-to-end lineage and asset discovery across the stack.
- Context agents: AI that drafts descriptions, metrics and business ontology for humans to approve.
- Context Engineering Studio: Bootstrap, test and deploy business context for AI use cases.
- MCP server and APIs: Exposes context to Claude, ChatGPT, Slack and Teams.
How It Fits Your Stack
Primary system of record: Data warehouse (Snowflake, Databricks, BigQuery) plus business systems
Key integrations: Snowflake, Databricks, dbt, Tableau, Looker, Power BI, Salesforce, HubSpot
Data flows: Reads metadata and lineage from connected systems into the context store; agents and users query it; it does not move business data.
Security & Compliance
- Certifications: SOC 2, ISO 27001, ISO 27701, HIPAA, GDPR
Implementation & Ownership
- Time to first value: Weeks for core connectors; months for enterprise-wide adoption
- Implementation complexity: High
- Typical owners: Data platform team, Data governance, Analytics engineering
The catalog fills itself; the business glossary doesn't. RevOps needs a seat at the table to own GTM definitions.
Pricing & Contracts
- Pricing model: Enterprise subscription
- Indicative range: Contact sales
- Free tier: No
Who It's Best For
Large companies with a central data team, a cloud warehouse and many BI tools that want one governed context for all AI.
Good fit if:
- Revenue metrics are computed in the warehouse, not only in the CRM
- Governance and lineage are audit requirements
- Several teams are building agents on shared data
Probably not ideal if:
- Your revenue data lives only in HubSpot or Salesforce
- You lack a data team to run it
Proof & Buyer Signals
Ratings: Gartner Magic Quadrant Leader for Metadata Management (2025) and Data & Analytics Governance (2026); Forrester Wave Leader (as displayed by the vendor)
Pros
- Broad connector coverage including CRMs
- Strong analyst recognition and enterprise customer base
- Governance travels with the context into agents via MCP
Cons
- Enterprise price and implementation effort
- Owned by data teams, so RevOps influence depends on internal politics
- Overkill for CRM-centric revenue stacks
Often Compared With
- Secoda — Secoda covers catalog, observability and governance for mid-sized data teams; Atlan targets the large enterprise.
- dbt — dbt defines the models and metrics in code; Atlan catalogs, governs and serves them.
- Vasco — Choose Vasco for a RevOps-owned GTM context layer; Atlan for an enterprise-wide one run by the data team.
Frequently Asked Questions
What is Atlan used for?
Atlan began as a data catalog and now positions itself as "the context layer for AI": an enterprise data graph of metadata, lineage, business glossary and policies, built from 80+ connectors, that AI agents can query through an MCP server.
How much does Atlan cost?
Atlan pricing: Contact sales There is no free tier.
What does Atlan integrate with?
Atlan integrates with Snowflake, Databricks, dbt, Tableau, Looker, Power BI, Salesforce, HubSpot.
Is Atlan SOC 2 compliant?
Atlan holds: SOC 2, ISO 27001, ISO 27701, HIPAA, GDPR.
Who is Atlan best for?
Large companies with a central data team, a cloud warehouse and many BI tools that want one governed context for all AI.
What are the best Atlan alternatives?
The tools most often compared with Atlan are Secoda, dbt, Vasco.
