[Learning Center](/learning-center)[Data Catalog](/learning-center/topic/data-catalog)

# OpenMetadata vs DataHub: When Is OpenMetadata a Better Fit?

A buyer weighs what they get in the open-source product, what it costs to run, and what happens when they need enterprise controls.

On all three, OpenMetadata is the stronger answer, and DataHub's own documentation makes the case better than any vendor pitch could. DataHub publishes a line-by-line list of Cloud-only features withheld from open source, the best evidence available here.

## Key Takeaways

-   DataHub's own comparison pages mark quality monitoring, governance workflows, and fine-grained access control as Cloud-only. OpenMetadata ships all of that in open source today.
-   OpenMetadata runs on a simpler stack: Postgres or MySQL plus a search index, with no Kafka to operate. That difference shows up directly in setup time and ongoing cost.
-   The controls essential for enterprise procurement–SCIM (System for Cross-domain Identity Management), OIDC (OpenID Connect) role sync, approval workflows, and standards-based metadata–are already open-source features in OpenMetadata, not upsells.
-   Our community innovation and release velocity have led us to now pass DataHub in GitHub stars.
-   DataHub still makes sense for a team that specifically wants a streaming metadata bus or needs every component under Apache 2.0.

## At a glance

| | **OpenMetadata** | **DataHub** | |---|---|---| | **First released** | August 2021 | November 2015 (as WhereHows lineage) | | **Core license** | Apache 2.0 — with exceptions, [see below](#licensing) | Apache 2.0 throughout | | **Storage** | PostgreSQL/MySQL + Elasticsearch/OpenSearch | Relational store + Elasticsearch + graph database (JanusGraph/Neo4j) + Kafka | | **Ingestion model** | Pull-based; built-in Kubernetes Orchestrator, external Airflow supported | Stream-first, event-driven over Kafka | | **Data quality** | Built into the platform | Native assertions + external integrations | | **MCP server** | Yes - since v1.8.0 (2025) | Yes - since v0.3.13 (2025) | | **Commercial offering** | Collate | DataHub Cloud (Acryl Data) | | **Positioning** | "The Open Context Platform for Data and AI" | "The Context Platform for AI Agents" |

## The Open-Source Product Is the Product

[DataHub's Core vs Cloud comparison](https://docs.datahub.com/docs/managed-datahub/managed-datahub-overview) marks a long list of Cloud-only features: AI anomaly detection, freshness and volume and schema and column monitoring, custom SQL checks, the data health dashboard, assertion notifications, secure in-VPC quality validation, pipeline circuit breakers, bi-directional metadata sync, compliance forms, the workflow engine, metadata tests, change proposals, access request workflows, fine-grained access control, AWS PrivateLink, IP restrictions, the in-VPC remote ingestion agent, the hosted MCP (Model Context Protocol) server, scoped MCP servers, the Ask DataHub AI agent, AI documentation generation, and multi-channel notifications to Email, Slack, and Teams. [An archived version of the same page](https://archive.docs.datahub.com/docs/1.3.0/managed-datahub/managed-datahub-overview) made the same split explicit for compliance forms, certification, and approval workflows, all marked as Cloud add-ons rather than Core features.

We ship the equivalents in OpenMetadata's open-source build. Tests, profiling, freshness, volume, null, uniqueness, and distribution checks run alongside alerts, incidents, and root-cause workflows, with event-based alerts over Webhooks, Slack, Microsoft Teams, or Google Chat. RBAC (role-based access control) policies cover metadata operations and extend to search scoped by role. ODCS (Open Data Contract Standard) 3.1 data contracts support import and export, schema expectations, SLAs, and terms of service. From the 2.0 line, teams use custom intake forms for asset certification, ownership assignment, and data classification, feeding into the same task and workflow system, and OWL import lets a team load an existing ontology instead of re-keying it. The MCP server ships on by default, so it starts ready on upgrade with no manual activation.

DataHub's open-source edition reads like a funnel into Cloud, while OpenMetadata's open-source edition is a complete platform, and we at Collate add managed operations and AI agents on top of it rather than gating the basics behind a contract.

## Lower Operational Cost, Same Production Scale

DataHub requires MySQL, Elasticsearch, and Kafka for primary storage and indexing, built around a streaming-first design with real-time updates pushed through Kafka. OpenMetadata runs on MySQL 8.0.42 or newer, or Postgres 15 or newer, plus Elasticsearch 9.x or OpenSearch 3.x, with no Kafka and no separate graph database.

The practical difference shows up fast. One engineer who self-hosted both platforms, [writing for Fastero](https://fastero.com/blog/datahub-vs-openmetadata-open-source-data-catalogs), reported a three-day setup time for DataHub before trusting it enough for real ingestion, against an afternoon for OpenMetadata. Building a custom connector took about half a day on OpenMetadata against a full day on DataHub. Even that same reviewer, who prefers DataHub's governance model, concedes that OpenMetadata's simpler, Postgres-backed operating model beats DataHub's event-streaming design, and that DataHub's Kafka-based pipelines add infrastructure complexity OpenMetadata avoids entirely.

For a platform team, the choice is standing up one more Postgres instance versus running a Kafka cluster where consumer-lag debugging becomes a permanent line item. Security has to review every Kafka topic and Elasticsearch reindex path, and someone on call owns every Rest.li service.

## Enterprise Fit, Argument by Argument

Identity and provisioning live in the open-source build. In OpenMetadata 2.0, we moved SCIM provisioning into Settings, under Team and Users, and in 2.0.2 we fixed SSO role sync so changes made at the identity provider now flow through at the OIDC callback. SCIM, OIDC role sync, and search-scoped RBAC are all in OSS; [DataHub's own matrix](https://docs.datahub.com/docs/managed-datahub/managed-datahub-overview) lists fine-grained access control as Cloud-only.

The governance workflow engine is in OSS too. [DataHub puts compliance forms, change proposals, access request workflows](https://docs.datahub.com/docs/managed-datahub/managed-datahub-overview), and action workflows behind Cloud, while our intake forms, tasks, and announcements stay open-source, with the 2.0 release confirming automatic carryover on upgrade. For a regulated industry, approval workflows and certification are the governance product, not an add-on fee. At Collate, we package the same capability through [governance workflows and approval gates](https://www.getcollate.io/data-governance) for teams that want it managed.

Standards-based metadata makes audits and interoperability easier. We publish more than 700 JSON Schemas covering entities, APIs, configurations, events, and relationships, alongside RDF and OWL ontologies, SHACL (Shapes Constraint Language) shapes, and JSON-LD contexts, aligned with DCAT (Data Catalog Vocabulary), DPROD (Data Product Vocabulary), PROV-O (Provenance Ontology), OpenLineage, and ODCS 3.1. OWL import lets a team load an existing taxonomy instead of rebuilding it by hand. DataHub's model is PDL, a schema language built at LinkedIn, a smaller pool to hire against, and a harder handoff to a governance team already working in W3C standards. The same lineage model also carries [column-level lineage across sources](https://www.getcollate.io/data-lineage) through OpenLineage, so audit trails hold up across the stack.

Data contracts are a standard entity with OpenMetadata. ODCS 3.1 import and export moves contracts between OpenMetadata, producer repositories, and other ODCS-aware tools. DataHub Core has data contracts too, but enforcement, assertion monitoring, circuit breakers, and notifications sit behind Cloud.

Data quality runs natively, without a second vendor. DataHub Core imports dbt and Great Expectations results, but execution and monitoring stay in Cloud. We run our own [native profiler, tests, and incident management](https://www.getcollate.io/data-quality) instead. Choose OpenMetadata when quality checks need to run inside the catalog itself; DataHub Core still works when execution is meant to stay external. The 2.0 profiler also defaults to dynamic sampling instead of scanning every row, cutting query cost on large tables.

Kubernetes-native operations remove a standing objection. We ship a Kubernetes operator, and Airflow is being retired as the internal orchestration system in version 2.1, replaced by [the Kubernetes Orchestrator](https://docs.open-metadata.org/v2.0.x/deployment/upgrade/breaking-changes/overview) that shipped in 1.12. External Airflow installations are unaffected, closing the common complaint that OpenMetadata forces Airflow on anyone scheduling ingestion.

Patches ship often, with named fixes. [The 2.0 release notes](https://docs.open-metadata.org/v2.0.x/releases/2.0-release) show release 2.0.7 alone patched the AWS SDK, updated Jackson Core, and closed Brotli and TLS vulnerabilities in the Node.js build. Seven point releases in nine weeks, each with named CVE (Common Vulnerabilities and Exposures) fixes, make patching routine.

AI access stays governed, not walled off. Our MCP server ships in the open-source build, on by default, exposing semantic search, lineage traversal, glossary lookups, and classification as tools any LLM can call under existing roles and policies. At Collate, we build on the same foundation with an [enterprise MCP server that enforces permissions on every call](https://www.getcollate.io/collate-ai), and provider configuration spans OpenAI, Azure OpenAI, Bedrock, Google, and Anthropic, so bringing your own model is a simple configuration change, nothing to discuss or negotiate. DataHub's hosted and scoped MCP servers stay behind Cloud.

Business users adopt it faster. Governance programs fail when stewards avoid the tool. OpenMetadata fits analytics and governance-led teams that want fast adoption with low overhead, and [one hands-on comparison](https://fastero.com/blog/datahub-vs-openmetadata-open-source-data-catalogs) found DataHub's interface less intuitive for casual business users, a point DataHub's own comparison table confirms by marking business-user experience as a paid feature.

## Velocity and Commitment to Open Source

We ship faster into the open-source line. Multi-domain support, data contracts reaching general availability, and data quality as code all landed through 2024 and 2025 without waiting on a paid tier. OpenMetadata, [our open-source project](https://open-metadata.org/), now counts more GitHub stars than DataHub.

## Licensing

**DataHub Core is Apache 2.0.**

**OpenMetadata is mixed:**

| **Component** | **License** | |---|---| | Server, core APIs, schemas (repository root) | **Apache 2.0** | | Ingestion framework (`ingestion/`) | **Collate Community License v1.0** | | Web UI (`openmetadata-ui/`) | **Collate Community License v1.0** | | MCP server (`openmetadata-mcp/`) | **Collate Community License v1.0** |

The Collate Community License grants you a royalty-free, worldwide right to use the software, modify it, create derivative works, distribute it, and reproduce copies. It carries one substantive restriction, an "Excluded Purpose":

making available any software-as-a-service, platform-as-a-service, infrastructure-as-a-service, or other similar online service that competes with Collate products or services that provide the Software

**What this means in practice.** If you are self-hosting OpenMetadata for your own organization, which describes the overwhelming majority of deployments, this restriction is one you will never encounter. You can run it, modify it, extend it, and redistribute it. You must mark your modifications and preserve copyright notices.

**When it matters.** If your organization mandates OSI-approved licenses across every component, or you intend to build a commercial hosted service on top of the codebase, the Collate Community License is a constraint, and DataHub Core's uniform Apache 2.0 is the better fit. We would rather you learn that here than three months into a procurement review

## When DataHub Is Still the Right Call

DataHub remains the right choice for a team that specifically wants a streaming metadata bus and is willing to operate Kafka, Elasticsearch, and MySQL to get it, or that needs every component under an Apache 2.0 license with no managed tier involved at all. That is a real requirement for some platform teams.

## Summary

OpenMetadata is the better fit for most enterprise evaluations because the open-source edition already contains the capabilities DataHub reserves for Cloud: native quality monitoring, alerting to Slack and Teams, governance workflows with intake forms, fine-grained and search-scoped RBAC, SCIM and SSO role sync, ODCS data contracts, OWL ontology import, and an MCP server that ships on with authentication built in. The platform runs on a smaller stack: Postgres or MySQL plus a search index, with no Kafka to deal with. Monthly releases carry named security fixes, and the whole system is built on JSON Schema and W3C standards that enterprise governance teams already use. DataHub remains the right pick for teams that want a streaming metadata bus and plan to operate it themselves.

## FAQs

**Is OpenMetadata's open-source edition feature-complete, or does it funnel users into our paid product the way DataHub Core funnels into Cloud?** No, and that is the core difference this piece makes. OpenMetadata's open-source build already ships profiling, alerting, RBAC, data contracts, and a default-on MCP server, the same categories DataHub reserves for Cloud. Our [architecture and feature comparison](https://www.getcollate.io/competitive/collate-vs-datahub) lays out where the two products diverge in more detail, and we add managed operations and AI agents on top, not the basics.

**Does OpenMetadata require Kafka or a graph database to run in production?** No. OpenMetadata runs on MySQL 8.0.42 or newer, or Postgres 15 or newer, plus Elasticsearch 9.x or OpenSearch 3.x, with no Kafka and no separate graph database. DataHub, by contrast, requires Kafka for its streaming architecture, which adds a cluster and a consumer-lag problem that someone on a platform team has to own.

**Can OpenMetadata handle SSO, SCIM, and fine-grained RBAC without a paid tier?** Yes. SCIM provisioning, OIDC role sync at the identity provider, and search-scoped RBAC all ship in the open-source build, and OpenMetadata 2.0.2 fixed a bug so SSO role changes now sync automatically. DataHub's own feature matrix lists fine-grained access control as a Cloud-only capability.

**How do OpenMetadata and DataHub differ on data contracts and governance workflows in the open-source edition?** We ship ODCS 3.1 data contracts with import and export, plus custom intake forms, tasks, and workflows for certification and ownership assignment, all in OSS. DataHub Core includes data contracts, but enforcement, assertion monitoring, circuit breakers, notifications, compliance forms, and change proposals stay locked behind Cloud.

**When does DataHub remain the better choice over OpenMetadata?** DataHub keeps the edge when a team specifically needs a Kafka-based streaming metadata bus and is willing to run it, or when every component must carry an Apache 2.0 license with no managed tier anywhere in the stack. Some platform teams genuinely have that requirement.

## Get Enterprise Controls Without the Upsell

See how OpenMetadata's open-source foundation and our managed layer at Collate work together for your team. [Talk to us](https://www.getcollate.io/contact-sales).

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