Atlan
Atlan helps enterprises govern data at scale through active metadata and AI-ready context.
Atlan is a metadata platform that transforms data catalogs from static inventories into active governance control planes. It automatically captures column-level lineage, propagates governance context across the modern data stack, and surfaces quality signals inside tools like Snowflake and Databricks. Built for enterprises managing complex data ecosystems, Atlan serves as the missing context layer for enterprise AI, enabling both humans and AI agents to act on trusted data.
Problem solved
Enterprise data teams lack a unified way to track data lineage, propagate governance rules across disparate tools, and provide AI agents with trusted data context, resulting in compliance risks, data quality issues, and slow AI implementation.
Target customer
Mid-market to enterprise organizations (100+ employees) with complex data stacks; data-driven companies in financial services, healthcare, retail, and manufacturing requiring compliance and governance.
Founders
V
Varun Banka
Co-Founder & CEO
Co-founder of SocialCops (built India's National Data Platform); 9+ years in data ecosystem; Bachelor of Engineering from Nanyang Technological University in Computer Engineering.
P
Prukalpa Sankar
Co-Founder & CEO
Engineering degree from Nanyang Technological University; previously held internships at ExxonMobil and Goldman Sachs; met co-founder at NTU.
Funding history
Seed
$2.5M
2019
Led by Unknown
· Unknown
Series A
$16.5M
2019
Led by Unknown
· Unknown
Series B
$50M
2022
Led by Unknown
· Unknown
Series B (Extended)
$27.5M
March 2024
Led by Unknown
· Unknown
Series C
$105M
May 2024
Led by GIC, Meritech
· Insight Partners, Sequoia Capital India, Salesforce Ventures
Total raised:
$206M
Industries
Pricing
Usage-based on active users, data sources connected, and governance features required. Free tier available for small teams and proof-of-concept. Mid-market typically $50K-$150K annually for licensing plus $50K-$100K for first-year implementation. 20-40% cheaper than Alation, 50-70% cheaper than Collibra for comparable deployments.
Notable customers
General Motors, Autodesk, and others (full list not publicly disclosed)
Integrations
Snowflake, Databricks, Salesforce, Tableau, Power BI, dbt, Looker, SQL Server, PostgreSQL, AWS Glue, Apache Spark, modern data stack tools
Tech stack
HSTS (Security)
Google Workspace (Email)
Cloudflare (CDN)
HubSpot (Marketing automation)
Amazon Web Services (PaaS)
Sendgrid (Email)
Website
Competitors
Alation
Alation is more established with broader feature set but typically 20-40% more expensive for mid-market deployments; Atlan bundles more governance features and has lower implementation costs.
Collibra
Collibra offers deeper compliance and privacy modules for highly regulated industries but is 50-70% more expensive; Atlan focuses on active governance and AI readiness with lower cost of ownership.
Apache Atlas
Apache Atlas is open-source and free but requires significant internal engineering resources; Atlan is a managed SaaS platform with automatic lineage capture and governance automation.
Why this matters: Atlan has raised $206M and is backed by top-tier investors (GIC, Sequoia Capital India, Salesforce Ventures) because it tackles a massive enterprise pain point: governance and data trust at scale. As enterprises deploy AI agents, the need for active, automated governance that spans disparate tools becomes critical. Atlan's focus on automation (vs. manual documentation) and AI-readiness positions it well in the evolving data infrastructure landscape.
Best for: Enterprise data teams managing complex multi-tool environments who need automated governance, compliance tracking, and AI-ready data context without manual documentation overhead.
Use cases
Data Lineage & Debugging
A financial services firm traces a reporting discrepancy affecting regulatory submissions. Instead of manually investigating SQL scripts across 20 tools, Atlan's column-level lineage automatically shows that a calculation field in the data warehouse was sourced from an upstream transformation error. The team fixes it and uses Atlan's impact analysis to verify which reports are affected before pushing the fix.
Governance Automation at Scale
A healthcare organization tags sensitive patient data as PHI in Atlan. Those tags automatically propagate across Snowflake, Databricks, and BI tools without manual updates. Access policies sync back, and anyone querying that data sees compliance context in their BI tool, eliminating manual compliance checks.
AI-Ready Data Context
A retail company deploys an AI agent to forecast demand. Instead of the agent hallucinating on bad data, Atlan provides the agent with the full data graph, business logic (how inventory transforms), and governance rules (which datasets are production-ready). The agent makes decisions on trusted data with explainable lineage.
Data Discovery & Time-to-Insight
A product team at a SaaS company wants to analyze user behavior but doesn't know which datasets exist, where they live, or if they're fresh. Atlan surfaces all available tables with business descriptions, update frequency, ownership, and quality scores—reducing data discovery from days to minutes.
Alternatives
Alation
Alation is a mature, more feature-rich data catalog with stronger AI/ML capabilities, but higher cost and more complex implementation for mid-market teams.
Collibra
Collibra excels in regulated industries with deep compliance and privacy modules, but is significantly more expensive and enterprise-focused than Atlan.
Apache Atlas
Apache Atlas is open-source and free but requires substantial engineering resources and lacks the active governance and SaaS automation that Atlan provides.
Informatica Enterprise Knowledge Graph
Informatica offers deeper data integration alongside governance but is more expensive and best for organizations already in the Informatica ecosystem.
FAQ
What does Atlan do? +
Atlan is a metadata platform that automatically catalogs data, tracks column-level lineage across your data stack, and propagates governance rules (tags, access policies, quality metrics) across tools like Snowflake, Databricks, and BI platforms. It transforms static data catalogs into active control planes that serve both humans and AI agents with trusted, contextualized data.
How much does Atlan cost? +
Atlan pricing is not publicly listed and varies by organization size and complexity. Mid-market companies typically pay $50K-$150K annually for licensing, plus $50K-$100K for first-year implementation. A free tier is available for small teams and proof-of-concept use. Contact sales for a custom quote.
What are alternatives to Atlan? +
Top alternatives include Alation (more mature, higher cost), Collibra (stronger compliance modules, enterprise-focused), Apache Atlas (open-source, requires engineering effort), and Informatica Enterprise Knowledge Graph (deeper data integration capabilities). Choice depends on budget, compliance needs, and data stack complexity.
Who uses Atlan? +
Mid-market to enterprise organizations in financial services, healthcare, retail, and manufacturing. Known customers include General Motors and Autodesk. Best suited for data-driven companies with 25+ data sources and complex governance requirements who need compliance tracking and fast AI implementation.
How does Atlan compare to Alation? +
Both are leading data catalogs, but Atlan is typically 20-40% less expensive for mid-market deployments and bundles more governance features with lower implementation costs. Alation is more mature and established with broader AI/ML capabilities. Atlan excels at active governance automation and AI-ready data context, while Alation has stronger metadata enrichment depth.
Does Atlan integrate with my data stack? +
Yes. Atlan integrates with all major data platforms including Snowflake, Databricks, Redshift, BigQuery, and numerous BI tools like Tableau, Power BI, and Looker. It also connects to dbt, SQL Server, PostgreSQL, and AWS Glue for comprehensive modern data stack coverage.
Can Atlan help with AI implementation? +
Yes. Atlan serves as the missing context layer for enterprise AI by providing AI agents with the full data graph, business logic, governance rules, and quality signals. This ensures AI systems operate on trusted, well-understood data with explainable lineage.
Tags
data governance
data catalog
metadata
lineage tracking
data quality
compliance
AI-ready data
enterprise data