Data Catalog Alternatives: An Evaluation Guide

A shortlist often comes from an analyst quadrant, a few peer recommendations, and three vendor demos, and those demos all ran on the vendor's sample data. Now you have to pick one. The demos told you almost nothing, though, about whether the catalog will ingest your oldest Oracle instance, or trace a column through your dbt models. They also said little about whether it can show an auditor who approved access to a PII table, or give an AI agent something better than a table name, when it calls the API. This guide covers how to weigh the options, how to test them on your own sources, and which one wins in which situation. We publish it, and we have a bias toward multi-cloud estates that have real governance work, and AI on the roadmap.

Key Takeaways

• Fix scorecard weights before the first demo, and run a proof of concept on your own sources, so the evaluation reflects your estate, rather than the vendor's demo data.
• Score eight dimensions across every vendor. Those are connector fit, governance and audit, AI context, lineage, scale and operations, openness and lock-in, cost, and quality. The weights in this guide are a starting suggestion, so adjust them to your risk.
• If an estate is mostly Microsoft, or mostly Google Cloud, start with Microsoft Purview or Google Dataplex / Knowledge Catalog. Alation, Atlan, DataHub, and Secoda each fit a specific situation, and those situations are described below.
• We fit multi-cloud estates that need governance, quality, and column-level lineage in one platform, and we also fit estates where AI agents reach metadata through MCP, on open standards and schemas.
• Export your metadata in a documented format, and check what an agent's API call returns, beyond schema.

Weigh Eight Dimensions Before the Next Demo

A feature checklist tends to favor the vendor with the longest feature list. A scorecard built on six areas to check in an enterprise catalog keeps the scoring on outcomes. The eight dimensions below adapt those six areas. Lineage and quality are split, security is folded into governance and audit, and connector fit and cost are added. These weights are a starting suggestion, and they are not an industry standard. Move them to match your risk:

• Connector fit: 20
• Governance and audit: 15
• AI context: 15
• Lineage: 12
• Scale and operations: 10
• Openness and lock-in: 10
• Cost: 10
• Quality: 8 A regulated bank should increase the weight on governance and audit, and it should reduce the weight on AI context. If a team's main driver is an AI initiative, it should increase the AI-context weight, and it should reduce the weight on governance and audit. Fix the weights before the first demo, so the demo does not change them.

Test on Your Own Sources

Run a proof of concept on your own metadata. Our guidance on whether to pick an open source or enterprise catalog puts a proof of concept at two to four weeks. Run the same five tests against every finalist. Connectors. Pick your three hardest sources. Those might be the legacy database, a BI tool with unusual authentication, and the internal pipeline. Ingest each one, and then record what arrives, what is missing, and who had to write code. Ask whether ingestion is pull-based on a schedule, or event-driven, because that decides how stale the catalog gets between runs. Lineage. Choose one dashboard column that finance quotes in a board deck. Trace it back to source through your real SQL, your dbt models, and your orchestration. Check whether the trace is at column level, or whether it stops at table level. Also check whether column-level lineage is in the tier you were quoted, or whether it sits in a higher one. Audit. Ask the vendor to show a denied access request, and then ask for the history of who changed a glossary term, and when. Check that roles map to your identity provider, and ask about SCIM provisioning. Auditors usually ask for both, but a vendor demo environment often does not include them. An AI agent calling the API. Give an agent a scoped service account, and ask it a question where two tables look right, but only one is certified. See what the API returns. That might be the owner, the glossary definition, lineage, quality status, or only the schema. Check that the agent's access follows the same policies as a human's. Scale and exit. Load your production asset count, rather than a sample dataset. Our 2 million assets benchmark is our own testing, so ask every other vendor for its own numbers, and for its method. Then export your metadata in a documented format, and see what you can still use outside the product. The Apache 2.0 OpenMetadata project publishes its specifications in JSON Schema, and a proprietary store should be tested against that same export requirement. Check which open standards the export and import paths use, such as JSON Schema for entities and OpenLineage for lineage events.

Where Each Option Wins

The table is a snapshot of fit, based on how each product is positioned. It is a starting map, and your proof of concept should confirm every row, or revise it.

If your data lives almost entirely in Microsoft, or in Google Cloud, start with Purview or Dataplex / Knowledge Catalog, before you look at us. A cloud-native catalog fits that estate well, and it bills on consumption. G2 reviews, retrieved July 2, 2026, report that Purview is weaker on non-Microsoft sources, and that Dataplex / Knowledge Catalog is weaker on third-party and multi-cloud sources. It gets harder when a second cloud appears, or when on-prem sources appear, and standalone catalogs become the better fit there. Put a cloud-native option, and the standalone finalists, through the same five tests. Most vendors quote pricing on request, so compare total cost with named assumptions. Write down the number of data engineers, end users, and sources, along with a three-year horizon, and who runs upgrades. When you self-host an open-source catalog, cost moves from the license to engineering time, and that time should be included in the model.

What We Do

We are a platform for discovery, lineage, quality, and governance, and we are built on OpenMetadata's open context layer. We cover 130+ connectors. We also keep those functions in one architecture, so that a quality failure shows up on the lineage graph, next to the asset it affects. The pieces map to the scorecard:

• Lineage. We extract column-level lineage from query logs and pipelines, and we can import OpenLineage events.
• Quality. We run quality tests and incident management inside the catalog, and we integrate with dbt and Great Expectations.
• Governance. Our glossary and governance workflows include certification, role-based access control, and data contracts.
• AI context. In Collate 2.0, we add a semantic context graph for AI agents. OpenMetadata also ships a native MCP server. The metadata is reachable by MCP on open schemas, which keeps it out of a proprietary store.
• Operations. We run as SaaS, and we also deploy single-tenant, hybrid, or in your own cloud account. Our homepage lists SCIM and a 99.9% SLA at 2M+ assets, along with SOC 2 Type II. We add these on the same Apache 2.0 core, and that is the substance behind the claim that OpenMetadata is enterprise-ready. Self-hosted OpenMetadata has no SLA, unless you use us.
• Cost. Our free, Premium, and Enterprise tiers are published, and the pricing page lists the free tier's limits. Confirm the lineage rows per tier there. We price Collate AI through sales. Openness. We support open standards at the points where metadata moves in and out of the platform. The OpenMetadata specifications are published in JSON Schema, lineage events come in through OpenLineage, data contracts follow ODCS, and agents reach the metadata through MCP. The graph underneath is built on RDF and OWL, with open vocabularies such as DCAT, DPROD, SKOS, and PROV-O on top, so your definitions are not tied to our schema. Apache 2.0 covers the open source core, and the standards cover the formats around it. Test this the same way as the exit test above: export your metadata, and load it somewhere else. We fit less well if your estate sits entirely in one cloud vendor's stack, or if no one on your side owns governance. We ingest on a pull basis, as OpenMetadata does, but we do not use a real-time event bus. DataHub is event-driven, so weigh that in the connector test, if freshness between runs matters to you.

Pick by Situation

• If the estate is Microsoft-only, or Google Cloud-only, start with the cloud-native catalog.
• If the estate is multi-cloud, and it has governance and audit pressure, and an AI roadmap, we are the first option to evaluate, and the proof of concept should confirm it.
• A working Alation or Atlan install is reasonable to keep, unless a test shows a specific failure, or the renewal price is too high.
• If a team has strong platform engineering, and it prefers to operate the catalog, it can evaluate DataHub, or self-hosted OpenMetadata.
• If a small team needs a catalog in place quickly, it can look at Secoda, but it should check the connector list first. Write the weights down, and run the five tests against your three hardest sources.

FAQs

What Is the Best Alternative to Alation or Atlan?

A working Alation or Atlan install is worth keeping, unless a test exposes a problem you can name. If the estate is multi-cloud, and it has governance and audit pressure, and an AI roadmap, we are the first option to evaluate, though we publish this guide, and we have a bias. Platform-engineering teams may prefer DataHub, or self-hosted OpenMetadata, and small teams may prefer Secoda.

Is OpenMetadata a Realistic Alternative to DataHub?

For teams with strong platform engineering that want to run the catalog themselves, both DataHub and self-hosted OpenMetadata fit. The Apache 2.0 OpenMetadata project publishes its specifications in JSON Schema, and it ships a native MCP server. Whichever you choose, check your own staffing for upgrades, scaling, and support, because self-hosting moves cost from the license to engineering time. OpenMetadata also works with OpenLineage for lineage events and ODCS for data contracts, which keeps the metadata usable outside the product.

Should We Include Cloud-Native Catalogs in an RFP With Standalone Catalogs?

Run them through the same five tests, which are connectors, lineage, audit, an AI agent calling the API, and scale and exit. If your estate is mostly Microsoft, or mostly Google Cloud, you should start with Microsoft Purview, or with Google Dataplex / Knowledge Catalog. Coverage of sources outside that cloud is the primary check. G2 reviews, retrieved July 2, 2026, report that Purview is weaker on non-Microsoft sources, and that Dataplex / Knowledge Catalog is weaker on third-party and multi-cloud sources. It gets harder when a second cloud appears, or when on-prem sources appear.

How Do We Compare Pricing When Most Vendors Are Quote-Only?

Compare total cost with named assumptions. Write down the number of data engineers, end users, and sources, and write down a three-year horizon, and who runs upgrades. Include engineering time for any self-hosted option, and confirm which tier includes the lineage features you need.

How Should I Weight a Data Catalog Scorecard?

Treat the weights as a starting point, and adjust them to your risk. Our suggestion is connector fit 20, governance and audit 15, AI context 15, lineage 12, scale and operations 10, openness and lock-in 10, cost 10, and quality 8. A regulated bank should increase the governance weight, and it should reduce the AI-context weight. An AI-led team should do the opposite. In both cases, the weights should be fixed before the first demo.

Which Open Standards Does Collate Support?

We support JSON Schema for the OpenMetadata specifications, OpenLineage for lineage events, ODCS for data contracts, and MCP for agent access. The graph is built on RDF and OWL, with open vocabularies such as DCAT, DPROD, SKOS, and PROV-O. The core is Apache 2.0, so your metadata stays usable outside the product.