Atlan vs DataHub
Choose the Metadata Platform That Operationalizes Context Across the Enterprise
Atlan is the context layer for data and AI for top enterprises, built on an Iceberg-native lakehouse with a developer-first app exchange and rich data products that future-proof data and AI capabilities to drive innovation.
Trusted by companies
with more than $10T in enterprise value
Affirm chose Atlan as a partner to power an extensible governance platform
"We looked for a partner whose vision, development, and engineering culture matched ours. We are an API-first company and needed APIs to extend the catalog into a platform that could integrate across systems."
Ankit Singh
Tech Leadership
"Being a fintech company, we wanted to focus on what matters for our customers — not maintaining infrastructure and becoming an infrastructure company. For us, that's undifferentiated work."
Ankit Singh
Tech Leadership
"People behind the solution matters. We looked at their ability to solve problems when things got difficult. This gives us confidence we have a partnership, not just buying the current software & tomorrow roadmap"
Ankit Singh
Tech Leadership
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Atlan vs DataHub:
Key differences that deliver better outcomes
| What Teams Need |  | DataHub |
|---|---|---|
| Metadata lakehouse (core differentiator) | Iceberg-Native Lakehouse - Separated storage/compute. Optimized for multi-cloud/AI workloads | OSS Platform - Built on GraphQL/OpenLineage. Scale and infra are self-managed |
| App framework (core differentiator) | App Exchange - Developer first tools for AI apps/connectors. 20+ external partners already building | No Native Framework - Relies on OSS scripts. Extensions require manual engg/PRs |
| MetaModel | Flexible, No-Code - UI-interface for admins to define custom metadata attributes | Structured, Schema-First - Requires developers to define new "Aspects" using PDL files |
| Event driven architecture | CDC and webhooks with Kafka for dynamic metadata capture | Runs on a Kafka-based Metadata Change Log (MCL) |
| What Teams Need | | DataHub |
|---|---|---|
| AI governance (core differentiator) | Unified governance for data and AI assets, including model lineage, explainability, and compliance | No AI governance or model lineage capabilities |
| Data product marketplace (core differentiator) | Curated data product marketplace with usage scoring, access controls | Basic "data product" tagging. Lacks marketplace and scoring |
| Lineage & impact analysis | Column-level lineage for 30+ sources with proactive impact analysis (Github CI/CD) | Column-level lineage for 20+ sources with simple impact analysis |
| Workflows and Policy Manager (core differentiator) | Native Policy Manager (cross-system) & Advanced DIY Governance Workflow Builder | Limited policies (Datahub-only, not data sources). Basic governance workflows |
| Tag management | 2-Way Tag Sync. Bulk tagging via playbook. Upstream & Downstream lineage propagation | 1-Way Tag Sync. Bulk tagging via metadata tests. Downstream only lineage propagation |
| What Teams Need | | DataHub |
|---|---|---|
| Personas served | Supports All Personas - Data Governance, Data Producers and Data Consumers | Supports primarily technical personas |
| Collaboration | Chrome extension for major BI tools. Integration with Slack, MS Teams, ServiceNow, JIRA, MS excel and Google Sheets | Basic Chrome extension & integration with Slack |
| UI/UX | Modern, intuitive, personalised for all personas for enterprise adoption | User-friendly experience for technical users, not non-technical |
| What Teams Need | | DataHub |
|---|---|---|
| System coverage | 100+ Native/API-First Connectors. Breadth across modern/legacy (e.g.SAP ECC) & depth in MDS (e.g. Snowflake) | ~60 OSS connectors. Community maintained |
| Post-Sales & Support Model | Partnership-Focused - Dedicated CSM, 24/7 SLAs, & CX adoption services | Focus on Stability/Expert Help. Provides tiered technical SLAs |
| Pricing model | Starts at $100K/year. Aligned with asset volume and users. | Varies by setup. Total cost includes infra and DevOps |
Why data and AI-forward enterprises choose Atlan over DataHub
Data leaders chose Atlan for tangible business outcomes and superior product fit.
01
Open & extensible platform
Future proof platform with Iceberg-native Metadata Lakehouse and App Framework, Atlan evolves with new tools, AI use cases, and enterprise-scale needs.
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02
Robust end to end lineage
Complete asset and business lineage with proactive impact analysis ensures visibility, reliability, and control.
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Partner not vendor
A collaborative solution-driven culture with adoption accelerator services helps enterprises deploy and scale faster.
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Lower TCO
Managed SaaS and reduced engineering overhead deliver faster time to value without maintenance complexity.
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Adoption by all personas
A personalized, non-technical interface and a ready Data Products Marketplace make collaboration effortless across teams.
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Advanced governance
Policy Manager, no-code workflow builder, and AI Governance bring automation, consistency, and compliance to every governance process.
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Build or buy? With Atlan, you don't have to choose.
Open-source tools like DataHub offer flexibility but require heavy engineering resources to secure, maintain, and scale.
Managed platforms provide reliability but limit customization and control.
Atlan brings the best of both worlds
- The hybrid advantage: Atlan's platform is open by design - with robust APIs/ developer SDK, iceberg-native metadata lakehouse and developer first app exchange for easy extensibility.
- Focus on outcomes: Atlan manages the burden of infrastructure, upgrades, and uptime. This frees your team to concentrate on more data projects and AI innovation.
- Proven at scale: Atlan is trusted by enterprises with more than $10 trillion in value, with the likes of Affirm, GM, and Mastercard, proves that openness and enterprise scale can coexist.
- Industry recognition: Validated by leading analysts and partners including Forrester, Gartner, and Snowflake for innovation and customer impact.
Choose the modern way: Build with the freedom of open source and scale with the confidence of a managed platform.
Frequently asked questions: Atlan vs DataHub
Does Atlan support Bring Your Own Cloud (BYOC) or on-prem deployments?
Yes. Atlan supports BYOC, multi-cloud, and hybrid architectures across AWS, Azure, and GCP. Enterprises retain full control of their metadata while Atlan manages performance, uptime, and upgrades.
DataHub requires teams to host and maintain their own infrastructure or rely on managed OSS offerings, which increases operational overhead.
DataHub requires teams to host and maintain their own infrastructure or rely on managed OSS offerings, which increases operational overhead.
How does Atlan's Metadata Lakehouse differ from DataHub's backend architecture?
Atlan's Iceberg-native Metadata Lakehouse separates storage and compute for petabyte-scale performance. It enables real-time querying, versioning, and time travel—capabilities required for AI and large-scale workloads.
DataHub operates as an OSS metadata platform built on open standards like GraphQL and OpenLineage; scale and extensions are self-managed rather than lakehouse-based.
DataHub operates as an OSS metadata platform built on open standards like GraphQL and OpenLineage; scale and extensions are self-managed rather than lakehouse-based.
Can Atlan integrate directly with Databricks, Snowflake, and dbt?
Yes. Atlan offers native connectors for 100+ systems like Databricks, Snowflake, BigQuery, Redshift, dbt Core and Cloud, as well as streaming systems like Kafka and governance tools such as Immuta. This enables unified, near-real-time lineage across the assets in the entire data estate.
How extensible is Atlan's API and SDK framework?
Atlan is built for programmatic metadata use cases—with open APIs, SDKs for CI/CD pipelines, and webhooks for event-driven orchestration. E.g., Top 3 banks in LatAm execute 4.4B API calls/mo, 400M+ asset updates done in Atlan are automated via SDKs.
Does Atlan provide developer tooling similar to OSS frameworks?
Yes. The Atlan App Framework lets developers build, run, and share metadata-driven apps with prebuilt APIs, runtime environments, and reusable components.
How does Atlan handle automation and lineage at scale?
Atlan delivers automated end-to-end lineage for 30+ systems—from pipelines to dashboards—with impact analysis embedded directly in GitHub CI/CD. Atlan Playbooks automate tagging, quality rules, and compliance workflows, cutting documentation time.
How is Atlan optimized for AI and LLM use cases?
Atlan's metadata lakehouse powers real-time context management for AI agents and copilots. It acts as a context database for models (used by Fox, Workday), enabling semantic search, lineage-based explainability, and compliance tracking. DataHub does not offer AI-native lineage or orchestration.
Can Atlan manage unstructured data or logs?
Yes. Atlan integrates with object stores, logs, and unstructured sources through partner-built connectors like the Anomalo Unstructured Data Quality Connector. The App Framework ensures metadata from files, models, and APIs is orchestrated in one control plane.
How does Atlan support metadata versioning and change tracking?
Every metadata action—from enrichment to policy change—is logged and versioned. The metadata lakehouse provides immutable audit trails, rollback, and time-travel queries for compliance and reproducibility.
What kind of scalability can Atlan handle?
Atlan is trusted by enterprises with more than $10 trillion in value, with the likes GM, and Mastercard. Consequently, Atlan powers some of the largest metadata volumes in the world. E.g. A FMCG co has 72M+ assets in Atlan, A Top 3 LatAm bank has 9.5B API calls/month
Does Atlan offer support for multi-tenant or domain-first data architectures?
Yes. Atlan's Data Product Marketplace enables federated, domain-first governance with curated access, usage scoring, and cross-domain lineage—key to enterprise scale. DataHub supports only basic domain tagging and manual federation.
Is Atlan secure enough for regulated industries?
Atlan is used by Fortune 50 banks, fintechs, and healthcare companies with strict compliance needs. It supports the basic security features (e.g. SAML SSO, SCIM identity management, 2FA (TOTP)) and advanced security features (PrivateLink, Site-to-Site VPN, secure offline agents). Atlan meets SOC 2 Type 2, ISO 27001, GDPR, and HIPAA standards, with deployment options across AWS, GCP, and Azure in any preferred region.
DataHub's OSS model requires manual setup for comparable controls.
DataHub's OSS model requires manual setup for comparable controls.
What's the learning curve for implementation?
Atlan is designed for fast adoption. Our philosophy is software in 2025 shouldn't need training, and should be easy to adopt. Most enterprise customers go live in under 90 days using in-app guides and community playbooks. For customers who need help, Atlan offers adoption CX accelerator services.
How does Atlan price compare to DataHub?
Atlan's pricing starts at $100K/year, aligned with users and data estate size—not connector counts or guest users. This ensures predictable scaling and lower TCO compared to OSS setups that require ongoing engineering support.
Can Atlan coexist with OSS metadata tools during migration?
Yes. Atlan ingests metadata from OSS catalogs such as DataHub and OpenMetadata into the metadata lakehouse, enabling unified governance during transitions.