Atlan
Revolutionize data management: discover, govern, and collaborate effortlessly.. [Contact for Pricing]
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What is Atlan?
Atlan is an active metadata platform and third-generation data catalog designed for modern data teams. It serves as the context layer for enterprise AI, helping organizations discover, understand, trust, and collaborate on data assets across their entire data stack. The platform unifies metadata from 80+ native connectors including data warehouses (Snowflake, BigQuery, Redshift, Databricks), databases (PostgreSQL, MySQL, Oracle), BI tools (Tableau, Power BI, Looker, Sigma), ETL pipelines (dbt, Fivetran), and orchestrators (Airflow, Dagster).
Key features include automated metadata discovery that continuously scans data sources without manual effort, end-to-end column-level data lineage showing dependencies from source to dashboard, intelligent natural language search with faceted filters, automated PII classification and governance controls, a business glossary for consistent definitions, embedded collaboration in tools like Slack and Teams, and AI-powered Context Agents that auto-generate asset descriptions, metrics, and business ontologies. The Atlan App Framework enables custom connector development using an open-source Python SDK.
Atlan is designed for data analysts who need self-service discovery without engineering support, data engineers who require impact analysis and pipeline debugging, governance teams enforcing compliance and access policies, data scientists finding quality training datasets, and AI teams deploying agents that need enterprise context. Enterprises like General Motors, Workday, Nasdaq, Mastercard, and Virgin Media O2 use Atlan to reduce discovery time by 95%, accelerate impact analysis from 6 weeks to 30 minutes, and achieve 53% reduction in engineering workload.
Atlan pricing
Pricing model: Freemium
Atlan offers three primary pricing tiers: Starter, Premier, and Enterprise. Specific pricing details are not publicly disclosed on the website. A free trial is available for Starter and Premier tiers. Customers must contact Atlan directly for customized pricing proposals. Estimated costs range from $6,000+ annually for 1 user, $20,000-$50,000+ for 10 users, and $50,000-$150,000+ for 100 users. Pricing is based on number of users, data volume/assets cataloged, required capabilities, and deployment type. Enterprise plan includes SCIM provisioning and advanced governance features.
Atlan pros
- 80+ native connectors for comprehensive data stack integration
- Automated metadata discovery with real-time updates
- End-to-end column-level lineage for precise impact analysis
- AI-powered Context Agents auto-generate 87% human-quality descriptions
- Intelligent natural language search with usage-based recommendations
- Automated PII classification reduces tagging from 50 days to 5 hours
- Embedded collaboration in Slack, Teams, Tableau, and SQL editors
- Leader in both 2025 Gartner Metadata Management and 2026 Data & Analytics Governance Magic Quadrants
- Customer Favorite in Forrester Wave for Data Governance Solutions Q3 2025
- Iceberg-native Metadata Lakehouse with knowledge graph purpose-built for AI
- MCP Server enables AI agents to access enterprise context at runtime
- 95% of G2 userssee Atlan as a true partner
- Free trial available for Starter and Premier tiers
- Open APIs and portable context prevent vendor lock-in
- 24x7 availability with real-time system status page
Atlan cons
- No publicly disclosed pricing - requires sales contact for quotes
- SCIM provisioning only available on Enterprise plan
- R does not support querying - only Python available
- Browser emoji rendering varies across different operating systems
- No pricing transparency makes budget planning difficult
- Implementation can take 2-12 months depending on complexity
- Manual documentation approach does not scale beyond ~100 assets
- Requires executive sponsorship and dedicated data stewards for success
Frequently asked questions about Atlan
What is Atlan?
Atlan is the context layer for enterprise AI. It sits between business systems and AI agents, connecting lineage from data pipelines, business definitions from BI tools and SQL logic, knowledge from SOPs, quality scores, and access policies into a unified context store. Every agent and analyst queries that context store directly with no manual context-building per use case. Gartner named Atlan a Leader in the 2025 Metadata Management and 2026 Data and Analytics Governance Magic Quadrants. Forrester did the same in its 2024 Enterprise Data Catalogs and 2025 Data Governance Solutions Waves. The only platform recognized across all four.
How does Atlan help AI agents?
Atlan gives every AI agent the enterprise context it needs: the business definitions behind column names, the lineage behind every output, and the access policies behind every query. Without this, agents hallucinate, misclassify sensitive records, or return answers compliance teams reject. Every AI output is traceable - every answer points back to the data, the definition, and who certified it. AI agents get enterprise context through Atlan's MCP server, SQL interface, and open APIs.
What is an enterprise context layer?
An enterprise context layer sits between business systems and the AI stack. It unifies context from across the business - lineage, semantic definitions, SOPs, access controls, usage patterns - into a single graph that agents and analysts query in real time. Without one, every new agent deployment starts with months of manual context-building. With one, every new agent inherits the organization's full institutional memory on day one.
How does Atlan's context pipeline work?
The context pipeline has four stages: unify, enrich, certify, activate. Atlan unifies metadata from native connectors - data warehouses, BI tools, pipeline orchestrators like dbt and Airflow. Context Agents auto-generate descriptions, metrics, and business ontology across the full data graph. Human experts review and certify - human-on-the-loop, not out of the loop. Certified context activates to every agent and tool via MCP, SQL, and open APIs. Evals and traces feed back in with each cycle, so context quality compounds over time.
What systems does Atlan connect to?
Atlan connects natively to 80+ enterprise systems: Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Tableau, Looker, Power BI, and Postgres, among others. Once connected, lineage, query history, BI semantics, tags, and quality signals flow in automatically through scheduled and event-based workflows - no manual mapping required. Atlan also layers on top of existing catalogs like Microsoft Purview and Snowflake Horizon, pulling their metadata into a unified context layer.
Who uses Atlan?
Atlan is deployed at enterprises including General Motors, Workday, Nasdaq, Mastercard, and Virgin Media O2. AI leaders use it to give agents governed access to enterprise context. Data engineers automate lineage and discovery. Governance teams enforce policies at the asset level. AI platform teams build and deploy agents faster because business logic is already in the context layer - not scattered across prompt files and wikis.
How is Atlan different from other data catalogs?
Atlan is the only platform named a Leader in all four major analyst evaluations for metadata and data governance: Gartner's 2025 Metadata Management Magic Quadrant, Gartner's 2026 Data and Analytics Governance Magic Quadrant, Forrester's 2024 Enterprise Data Catalogs Wave, and Forrester's 2025 Data Governance Solutions Wave. No other platform has been recognized across all four. Atlan is also AI-native with Context Agents that auto-generate 690K+ descriptions across 50+ enterprise customers.
Does Atlan lock you into their platform?
Atlan layers on top of your existing data stack. Many enterprises run Atlan alongside Microsoft Purview or Snowflake Horizon or Databricks Unity Catalog - pulling metadata from all into a unified context layer rather than rebuilding from scratch. Built on open APIs and Iceberg-native formats, context stored in Atlan stays portable: it is not locked to any vendor's proprietary schema. Switch AI frameworks, add new systems, or consolidate tools - the context layer moves with you.
What is context engineering?
Context engineering is the practice of selecting, structuring, and delivering the specific knowledge an AI agent needs at each step of a task. Most teams do this manually for each agent - months of work, duplicated across every use case. Atlan automates it: context from 80+ systems is unified, Context Agents auto-generate descriptions, metrics, and ontology across the full data graph, human experts certify, and certified context activates via MCP, SQL, and APIs. In April 2026, Context Agents generated 690K+ descriptions across 50+ enterprise customers - 87% rated on par or better than human writing.
How do I get started with Atlan?
Start with a Context Workshop: Atlan's team maps your data and AI architecture, designs a context layer for a priority use case, and sets a measurable baseline. From there, a four-week Context Sprint delivers a working agent and accuracy results you can compare directly against your current approach. Most teams see the first value in weeks. Book a demo for a 30-min honest conversation about your AI context gap.