DataHub vs Atlan:
The Enterprise Metadata Platform Comparison Guide

Selecting the right metadata platform is critical for organizations operating at scale. This comprehensive guide compares DataHub vs Atlan across the dimensions that matter most to enterprise teams—scalability, extensibility, governance, AI readiness, and total cost of ownership—so you can make an informed, future-ready choice.

What is DataHub?

DataHub is an AI & Data Context Management Platform purpose-built for the demands of complex, high-scale, and AI-enabled enterprise data environments.

The origins of DataHub are rooted in solving real-world enterprise metadata challenges, namely:

• LinkedIn’s GDPR crisis, where co-founder Shirshanka Das led the development of a new generation metadata platform to address regulatory compliance.
• Airbnb’s IPO preparation, where co-founder Swaroop Jagadish led his team to build several data-informed decision-making tools including DataPortal for improved resource search and discovery.

Backed by a thriving open-source community of over 13,000 contributors across 3,000+ organizations, DataHub combines extensibility, scalability, and innovation—all driven by real-world production use cases.

Core capabilities include:

• Unified discovery, observability, and governance
• Real-time metadata sync and lineage tracking
• API-first extensibility and native support for AI workflows

DataHub Cloud, the enterprise SaaS offering, delivers:

• Dedicated enterprise support
• Improved performance and availability
• Secure, VPC-deployable infrastructure
• Premium features

Trusted by industry leaders like Netflix, Visa, Apple, and Slack, DataHub empowers organizations to scale metadata management and unlock AI-ready context across the data ecosystem.

What is Atlan?

Atlan is a data catalog and collaboration platform designed to help data teams discover, understand, trust, and use data more effectively. 

Atlan’s roots lie in Apache Atlas, an open-source framework originally developed by HortonWorks to govern Hadoop-based data lakes. While Atlan has since evolved into a proprietary metadata catalog, it retains a narrower focus on basic data catalog functionality.

Atlan has gained traction with select enterprise customers such as Autodesk, Nasdaq, and Unilever. However, the majority of its customer base skews toward early-stage data teams still building foundational data practices.

DataHub vs Atlan feature comparison

Feature	DataHub	Atlan	Impact
Deployment Options	Multi-cloud + VPC	AWS-only, no VPC	Deploy securely, where you need
Architecture	Real-time, event-driven	Batch-based	Accurate metadata at scale
Performance	Proven at scale (significantly faster ingestion)	Slow with large schemas	Faster time-to-value
Extensibility	API-first, UI & no-code customization	Rigid schema, limited APIs	Adapt to evolving needs
Observability	Native, real-time alerts & insights unified with discovery and governance	Requires third-party tools	Unified experience vs tool sprawl
Open Source	Apache 2.0, active community of 13,000+ members	Proprietary	Transparency, flexibility, innovation

Customers

DataHub

DataHub is the platform of choice for enterprises with complex, high-scale data environments.

With a thriving open-source community of over 13,000 members and 3,000+ active open-source deployments, DataHub is the largest open-source offering in the category, and used across some of the world’s most recognized data teams.

Enterprise adopters using DataHub include:

• Tech and digital leaders: Apple, Netflix, Notion, Slack, Foursquare
• Financial services: Visa, Chime, Block
• Telecom and media: Deutsche Telekom, Airtel
• Healthcare and pharma: Optum

These organizations turn to DataHub for scalable metadata management, AI-ready infrastructure, and governance at scale—proving its readiness for complex, enterprise-grade environments.

Atlan

While Atlan has some enterprise customers, it is more common among early-stage data teams with less complex metadata needs.

Core product features

Scalability

DataHub is engineered for high-scale metadata operations, supporting:

• Cross-platform visibility across hundreds of data sources
• An embeddable connector framework for easy integration
• High-throughput ingestion for large volumes and high-velocity metadata

DataHub’s performance is battle-tested in production at leading enterprises like Netflix, Apple, and Visa, where metadata complexity and scale push platform limits.

Atlan

In side-by-side evaluations of DataHub vs Atlan, enterprises have reported performance issues with Atlan’s ingestion, particularly:

• Slow metadata ingestion, especially for schemas containing large numbers of tables or columns
• Lagging search performance and irrelevant search results at scale
• Limited ingestion throughput when compared directly to DataHub

These limitations make Atlan less suitable for high-volume, fast-changing data environments, especially those requiring rapid metadata updates and reliable search relevance.

Extensibility

DataHub

DataHub’s extensible metadata model allows organizations to adapt the platform to fit their unique data landscape, including custom entities, relationships, ownership models, and descriptors.

Key extensibility features include:

• UI-based configuration for rapid, low-friction customization
• API-first metadata model, designed for easy programmatic extension
• No-code extensibility, enabling user-defined entities and attributes
• Native AI/ML support for modern data ecosystems and workflows
• Comprehensive API backbone to support flexible integrations and automation

These capabilities make DataHub ideal for enterprises with complex, evolving metadata needs. Whether you’re enabling AI agents, building internal tools, or unifying siloed data systems.

Atlan

While Atlan offers baseline metadata cataloging capabilities, its extensibility is limited, making it difficult for enterprises to adapt the platform to their specific needs.

Reported limitations include:

• Limited API support for custom structures and event-driven metadata updates
• Restricted metadata model modifications and rigid schemas
• Poor integration capabilities for exporting metadata via APIs
• Limited AI ecosystem integration, especially with platforms like SageMaker
• Underdeveloped event-based extensibility for integration with other products

These restrictions pose challenges for enterprises looking to build custom workflows, integrate with modern AI/ML pipelines, or extend metadata management across a diverse ecosystem.

Observability

DataHub

DataHub provides a complete platform offering data observability unified with discovery and governance capabilities. 

Key features include:

• Customizable quality checks for data validation
• Incident management for data quality issues
• Comprehensive reporting on data health and usage
• Real-time monitoring of data pipelines and assets
• Automated alerting for data anomalies

Atlan

Atlan lacks native observability capabilities, requiring third-party integrations that increase operational complexity.

Security and deployment

DataHub

DataHub is built for secure, compliant enterprise deployment.

Key capabilities include:

• VPC deployment capabilities for enhanced security
• Remote ingestion to maintain data sovereignty
• Compliance with industry standards for regulated environments
• Custom deployment configurations to meet unique enterprise security needs

With these security features and deployment options, DataHub empowers enterprises to maintain control, meet compliance, and deploy securely.

Atlan

Atlan’s architecture is more rigid, presenting a critical security limitation for enterprises with sensitive or regulated data.

Key concerns include:

• No support for VPC deployment, meaning metadata must be processed outside the customer’s network
• No remote ingestion option for metadata behind firewalls
• Barriers for organizations with strict data sovereignty or compliance requirements

AI-readiness and future-proof architecture

DataHub

DataHub is engineered for the future of AI-enabled data operations—supporting real-time intelligence, model governance, and AI agent workflows at scale.

Key capabilities include:

• Native AI/ML asset support for robust model governance
• DataHub MCP Server enables AI agents to tap into the rich metadata from across your data ecosystem without custom connectors
• Real-time metadata architecture with even-driven updates, essential for AI systems needing live context
• Extensible metadata model to support emerging AI technologies and use cases
• Community-driven innovation, rapidly integrating advancements in AI tooling
• Scalable architecture to support AI workload demands

With DataHub, enterprises can confidently support today’s metadata needs and adapt seamlessly to tomorrow’s AI landscape.

Atlan

Atlan uses a batch-based metadata ingestion approach, which limits its effectiveness in dynamic data environments and its ability to support modern AI requirements.

Challenges include:

• Batch-based metadata processing incompatible with real-time AI applications
• No native support for AI/ML metadata, requiring external integrations
• Limited extensibility, making it hard to adapt to fast-evolving AI metadata standards
• Closed development model limiting innovation speed
• Rigid architecture hindering AI system integration

These limitations make Atlan less suited for organizations investing in AI agents, real-time analytics, or model-centric governance frameworks.

Total cost of ownership (TCO)

DataHub

DataHub delivers long-term cost efficiency by consolidating metadata capabilities into a single, extensible platform.

Benefits of this unified approach include:

• One platform for discovery, governance, and observability
• Reduced integration overhead—no need to stitch together and maintain separate tools
• Lower training and onboarding costs thanks to an intuitive user experience
• Faster time-to-value through comprehensive capabilities
• Community-driven innovation that lowers internal development effort

DataHub’s architecture enables teams to scale metadata practices without incurring tool sprawl or unexpected complexity.

Atlan

Atlan’s limited out-of-the-box capabilities often lead to higher total cost of ownership.

Reported cost drivers include:

• Required integration with third-party tools like Monte Carlo for observability 
• Managing multiple vendor relationships and SLAs
• Ongoing integration overhead and maintenance across disconnected systems
• Longer implementation timelines due to fragmented architecture
• Higher training costs across multiple platforms and interfaces

These hidden costs can erode initial savings and introduce friction as organizations scale.

Ease of use and adoption

DataHub

DataHub offers a user-friendly experience backed by a vibrant open-source community and flexible enterprise deployment options.

Key strengths include:

• Community of 13,000+ active practitioners across 3,000+ organizations, driving peer learning and rapid innovation
• Intuitive interface designed for multiple user personas, including analysts, data scientists, engineers, and developers
• Multi-cloud compatibility with support for AWS, Azure, and Google Cloud 

This combination of ease of use, community-driven adoption, and deployment flexibility ensures faster time-to-value and long-term maintainability across complex enterprise environments.

Atlan

While Atlan offers a sleek interface for basic data discovery, technical users—especially data engineers and platform teams—often find it limiting for infrastructure-related workflows.

Reported adoption challenges include:

• Partial developer-friendly features and flexibility for data infrastructure management 
• No community ecosystem of open-source engagement
• AWS-only deployment, with no support for VPC or other cloud providers

These constraints increase friction for adoption among technical stakeholders and complicate deployment in organizations with diverse or high-security infrastructure needs.

Common misconceptions about DataHub

As an open-source project with enterprise capabilities, DataHub is sometimes misunderstood by new evaluators. Let’s clear up a few common myths.

Myth: “DataHub is only available as a self-hosted open-source tool.”

Reality: DataHub offers both options.

Organizations can choose between:

• DataHub Core: the open-source project you can deploy and manage in your environment
• DataHub Cloud: a fully managed enterprise-grade service handled entirely by the DataHub team

Organizations can choose between self-hosted open-source deployment or fully managed cloud service based on their needs.

Myth: “DataHub is hard to set up and maintain.”

Reality: DataHub Cloud eliminates complexity.

With the enterprise SaaS offering, DataHub Cloud, the DataHub team fully manages setup, optimization, and scaling of your deployment, ensuring:

• High availability and enterprise-grade performance
• Secure deployment configurations tailored to your needs
• Dedicated implementation support from the customer success team

This makes it easy for organizations to adopt DataHub quickly and confidently, without requiring in-house metadata platform expertise.

Final verdict: DataHub vs Atlan

When evaluating DataHub vs Atlan, the comparison is clear: DataHub is the enterprise-ready context platform built for scale, flexibility, and the future of AI.

Here’s why it stands apart:

• Proven at scale
  Battle-tested at LinkedIn, and trusted by industry-leading enterprises.
  
• Unified platform
  Combines data discovery, governance, and observability into a single metadata graph—eliminating the need for fragmented tools.
  
• AI-ready architecture
  Real-time, event-driven design powers modern AI agents and high-velocity data pipelines.
  
• Enterprise-grade extensibility
  API-first, highly configurable metadata model adapts to your evolving organizational needs.
  
• Robust open source community
  Backed by a global community of 13,000+ contributors driving continuous innovation and peer support.
  
• Flexible, secure deployment
  Supports VPC deployment and multi-cloud environments (AWS, Azure, GCP) to meet enterprise security and compliance standards.
  
• Performance leadership
  Industry-leading metadata ingestion, search performance, and system responsiveness—proven across large-scale deployments.

Atlan may suit early-stage data teams, but its limitations in extensibility, security, and AI support make it less suitable for enterprises looking to future-proof their data operations.

Get started with DataHub

Join the thousands of data-driven organizations that trust DataHub.

Whether you’re looking for a fully managed enterprise solution or prefer to start with open-source exploration, DataHub offers flexible onboarding paths tailored to your needs.