DataHub: Understanding Your Alternatives

If you're evaluating DataHub today, or already running it and starting to feel the edges, the questions that matter are what you get in the open-source product, what it costs to run, and what happens when governance or auditors ask for a control that isn't there.

DataHub answers the first part of those questions itself. Its own Core vs Cloud documentation publishes a line-by-line list of what Cloud holds back from the open-source edition, and that list is the best starting evidence for this comparison because these are DataHub's own words. We ran the same eight-dimension scorecard we use for every catalog evaluation against this specific case: a team evaluating or leaving DataHub.

Key Takeaways

• DataHub's own Core vs Cloud table is the starting evidence: AI monitoring, governance workflows, fine-grained access control, and hosted Model Context Protocol (MCP) servers all sit behind the paid tier.
• OpenMetadata's release notes describe open-source equivalents of most of that list: native quality tests, System for Cross-domain Identity Management (SCIM) provisioning and role-based access control (RBAC), Open Data Contract Standard (ODCS) data contracts, and a default-on MCP server. Check this against the version you evaluate.
• OpenMetadata runs on a smaller production stack (Postgres or MySQL plus a search index) with no Kafka cluster to operate.
• DataHub could be a good choice for teams that specifically want a streaming metadata bus and are staffed to run Kafka.
• Run the same proof of concept against both before the next demo: connectors, lineage, audit, an AI agent calling the API, and scale and exit.

DataHub's Cloud-Only Feature List

DataHub's Core vs Cloud page marks a long list of capabilities as Cloud-only: AI anomaly detection, freshness, volume, and schema monitoring, custom SQL checks, a data health dashboard, notifications for data assertions, pipeline circuit breakers, compliance forms and the workflow engine behind them, change proposals, access request workflows, fine-grained access control, AWS PrivateLink, and the hosted and scoped MCP servers that power its Ask DataHub agent. An older archived version of the same page said it even more plainly, in a table: developer-friendly experiences: available in Core and Cloud. Business-user-friendly experiences: Cloud only.

The split shows the open-source edition is built to push adopters toward Cloud. OpenMetadata ships equivalents to most of that Cloud-only list inside its open-source build, per its GitHub documentation: tests, profiling, freshness, volume, null, and uniqueness checks, alerts routed through Slack or Microsoft Teams, RBAC policies you can scope by role, ODCS 3.1 data contracts with import and export, and intake forms for governance work like asset certification and ownership assignment.

When you weigh those against the core feature groups any catalog should be judged against, the pattern repeats across categories. DataHub's open-source edition functions as a funnel into Cloud, while OpenMetadata's open-source edition is a complete platform that Collate adds managed operations and AI agents on top of.

Lower Operational Cost at the Same Production Scale

Fastero's hands-on comparison lists MySQL, Elasticsearch, and Kafka among the infrastructure DataHub requires to run. OpenMetadata runs on MySQL or Postgres plus a search index, with no Kafka cluster and no separate graph database to operate.

That difference shows up the moment someone tries to run both. In a hands-on comparison, Fastero reported it took three days to reach a state it trusted enough to point real ingestion at DataHub, against an afternoon for OpenMetadata.

Building a custom connector took about half a day with OpenMetadata and a full day with DataHub. For a platform team, the practical trade is one more Postgres instance to manage against a Kafka cluster with consumer lag to page on. A Kafka topic, an Elasticsearch reindex path, or a Rest.li service each becomes something a security review covers and SRE pages on. Treat this as one practitioner's account rather than a universal number. Time the same setup against your own ingestion sources before you weigh it heavily.

Enterprise Fit, Feature by Feature

This is where the Core vs Cloud split costs the most, because enterprise buyers don't treat these as nice-to-haves.

Identity and Access

SCIM provisioning, OIDC role sync, and search-scoped RBAC ship in the open-source build, per OpenMetadata's release notes. Verify these in the version you're evaluating. DataHub's own matrix lists fine-grained access control as Cloud-only.

Governance Workflows

DataHub puts compliance forms, change proposals, and access request workflows behind Cloud. OpenMetadata's release notes describe intake forms, tasks, and workflows as open source, which matters because, for a regulated industry, approval workflows and certification are the governance product itself. This holds up against governance platforms built to centralize policy and access more broadly, since the pattern repeats outside the catalog category too.

Standards and Data Contracts

OpenMetadata publishes more than 700 JSON Schemas, Resource Description Framework (RDF) and Web Ontology Language (OWL) ontologies, and Shapes Constraint Language (SHACL) shapes, per its GitHub README, and it aligns with the Data Catalog Vocabulary (DCAT), the PROV Ontology (PROV-O), OpenLineage, and ODCS 3.1, with OWL import so an enterprise can load an existing taxonomy instead of re-keying it. DataHub's model is built on Pegasus Data Language (PDL), a Rest.li schema language originally built for LinkedIn's own stack, documented as a smaller ecosystem to hire into and a harder handoff to a governance team already working in W3C standards.

Lineage and Proof

Whichever catalog you pick, test column-level lineage across the modern data stack yourself before you decide, because lineage is one of the five things worth proving in a proof of concept: connector coverage, lineage accuracy, an audit trail, an AI agent calling the catalog's API, and scale-and-exit behavior.

Native Data Quality and Operations

DataHub Core imports dbt and Great Expectations results, but execution and monitoring sit behind Cloud. OpenMetadata runs its profiler and tests itself, and recent versions default to dynamic sampling instead of scanning every row, per its release notes, which would cut query cost on large tables if that holds for the version you run. The repo includes a Kubernetes operator; recent release notes also show a move away from the internal Airflow dependency toward a native orchestrator. Check this directly; it removes the old objection that running it meant running Airflow too.

Governed AI Access

OpenMetadata's open-source MCP server exposes semantic search, lineage traversal, glossary lookups, and classification as tools any large language model (LLM) can call, authenticated under the same roles and policies as everything else. Bring-your-own-model is a config setting, with support for OpenAI, Azure OpenAI, Bedrock, Google, and Anthropic. DataHub's hosted and scoped MCP servers are Cloud-only. Conversational AI and agents built on the metadata graph need that governed, open-by-default posture, not an access layer gated behind a separate tier.

Put together, this is reason enough to check open-source catalog enterprise-readiness before you commit, rather than after the first audit finds the gap.

Release Velocity and the Open-Source Audience

OpenMetadata now has more GitHub stars than DataHub; per OSS Insight, recent counts put it at roughly 15,100 to 15,500 against DataHub's roughly 12,700 to 12,900. Both projects are growing, and that difference deserves a recheck rather than being treated as fixed. OpenMetadata shipped multi-domain support, data contracts, and data quality as code through 2024 and 2025, per its release notes. Check the changelog for your target version.

Where Each One Wins

DataHub remains the right call for a platform team already running Kafka that wants a streaming metadata bus and has staff to operate one. It may also be the fit if your procurement rules require every component to carry an Apache 2.0 license. Check DataHub's current component-level licensing before treating that as settled.

OpenMetadata and Collate fit the team that wants the enterprise controls, governance workflows, fine-grained RBAC, SCIM, data contracts, and a default-on MCP server, inside the open-source product itself, running on a smaller stack. If you're also weighing a commercial-only catalog, see how the same evaluation plays out against a different commercial catalog; the open-source-versus-managed question shows up there too, just from the other direction.

What to Do Next

We publish this comparison and we have a bias: Collate builds OpenMetadata's commercial layer, and we think the asymmetry here is real. DataHub's Cloud-only reservations largely overlap with capabilities OpenMetadata's release notes describe as already shipping in open source. We'd rather you confirm that yourself than take our word for it, including a direct comparison of Collate's architecture and AI agents against DataHub's. Run the same proof of concept against DataHub Core and OpenMetadata, on your own data sources, before your next demo: connectors, lineage, audit trail, an AI agent calling the API, and scale-and-exit behavior. This comparison doesn't map the DataHub-to-OpenMetadata migration path in detail or publish Collate's managed-tier pricing. Both deserve their own conversation with your account team once the proof of concept tells you the platform fit is right; we're flagging that omission rather than guessing at numbers we can't stand behind. When you're ready to have that conversation, talk to our sales team. The scorecard weights are a starting point. Fix them to match your organization's requirements before you score anything.

FAQs

Is OpenMetadata a Realistic Alternative to DataHub?

For most evaluations, yes. The capabilities DataHub holds back for its Cloud tier, native quality monitoring, governance workflows, and fine-grained access control, are described in OpenMetadata's own release notes as already shipping inside its open-source build, so the comparison comes down to each team's production requirements.

What Does DataHub's Own Documentation Say Is Cloud-Only Versus Open Source?

DataHub's Core vs Cloud page marks AI anomaly detection, monitoring dashboards, compliance workflows, fine-grained access control, and its hosted and scoped MCP servers as Cloud-only. An older version of the same page put the split plainly: developer-friendly experiences in both tiers, business-user-friendly experiences reserved for Cloud.

Does Leaving DataHub Core Mean Losing Data Quality Monitoring?

Not if you move to a catalog that runs its own checks. DataHub Core imports results from dbt and Great Expectations but leaves execution and monitoring to Cloud, while OpenMetadata runs its profiler and tests directly in the open-source build, including a dynamic-sampling approach its release notes describe as cutting query cost on large tables.

How Much Harder Is DataHub to Operate Than OpenMetadata?

In a hands-on comparison, Fastero reported it took three days to trust DataHub enough to point real ingestion at it, against an afternoon for OpenMetadata, and a custom connector took about twice as long to build on DataHub. Most of that gap traces back to DataHub's Kafka-based setup against OpenMetadata's simpler Postgres or MySQL stack, though this reflects one practitioner's account rather than a benchmark, so time this against your own sources.

Is OpenMetadata Enterprise-Ready Without a Paid Tier?

Largely, yes, based on its own documentation. SCIM provisioning, OIDC role sync, search-scoped RBAC, ODCS 3.1 data contracts, and an MCP server described as enabled by default in the MCP server documentation and setup guide are all documented as shipping in the open-source build. Verify this on the page for the version you plan to run. DataHub reserves the equivalent controls, fine-grained access control and hosted MCP servers among them, for its Cloud tier.