Amundsen vs OpenMetadata.
Amundsen and OpenMetadata both anchor in catalog & discovery — 6 dimensions differ, 4 hold. Below: posture, coverage diff, and capability matrix.
The Lyft-born OSS catalog that invented search-first discovery — historically important, but development has largely stalled since 2024.
Apache-2.0 unified metadata platform with a deliberately simple stack — discovery, lineage, quality, and contracts in one project.
Teams that already run Amundsen and need to understand what they have, or teams with a genuinely minimal requirement — usage-ranked table search and ownership tracking, nothing more — who are comfortable owning a codebase that is no longer moving.
Teams that want an OSS catalog without the operational weight of DataHub's Kafka and graph-DB architecture.
What each is betting on.
Honest read: maintenance mode. Created at Lyft, open-sourced October 2019, joined LF AI & Data as an incubation project in August 2020. Development has slowed to near-zero — the last release was databuilder 7.5.1 (August 2024) and the last commit to the monorepo (April 2025) moved a maintainer to emeritus status. Stemma, the managed-Amundsen company founded by an Amundsen co-creator, was acquired by Teradata in 2023 and discontinued as a standalone product. The repo is not archived, but treat this as software that is no longer evolving.
Collate founded 2021 by Suresh Srinivas (ex-Hortonworks co-founder, Hadoop committer) and Sriharsha Chintalapani (Apache Kafka and Storm PMC, ex-Uber). The OpenMetadata project was launched alongside the company. Series A $10M July 2025. Differentiator vs DataHub: deliberately simpler architecture (Postgres or MySQL + Elasticsearch — no Kafka, no graph DB) and faster shipping cadence on governance features through 2024–2025 (Multi-Domain, Data Contracts GA in 1.9, Data Quality as Code).
Each tool's current strategic narrative, verbatim from its profile.
How each tool describes the other.
Against datahub and openmetadata, the comparison is mostly historical: both cover Amundsen's discovery core, then add business glossary, governance workflows, column-level lineage, and data quality — with active communities shipping monthly. Amundsen's remaining edge is smallness: if your entire requirement is usage-ranked table search with owners and column stats, Amundsen does that with less conceptual overhead than either successor. Against apache-atlas, Amundsen is the friendlier UX but the less-maintained codebase; Atlas persists inside Hadoop-legacy estates Amundsen never targeted. Against select-star or secoda, the trade is self-hosted OSS versus paying for a maintained SaaS that does the same search-first job.
OpenMetadata's page doesn't directly mention Amundsen. See the OpenMetadata detail page.
Each quote is pulled from the named tool's own "Where it fits" write-up.
Spec sheet diff.
| Amundsen | OpenMetadata | |
|---|---|---|
| Vendor | LF AI & Data Foundation | Collate |
| Deployment | Self-hosted only | SaaS · Self-hosted |
| Pricing | OSS · paid tiers | OSS · free |
| dbt integration | Plugin | Native |
| OpenLineage | Consumer | None |
| Founded | 2019 | 2021 |
| HQ | — | Saratoga, CA |
Full Amundsen pricing → Full OpenMetadata pricing →
Both share Primary cluster: Catalog & discovery · License: Open source · Free tier: Yes · OSS self-host: Yes · Status: ● active
Each tool's center of gravity.
| Cluster | Amundsen | OpenMetadata |
|---|---|---|
| Quality & testing | 0/3 | 2/3 |
| Lineage & metadata | 1/3 | 3/3 |
| Catalog & discovery | 3/3primary | 3/3primary |
Scored 0–3 per cluster on the same rubric across all tools. A 0 means the cluster isn't the tool's focus, not that the feature is absent. See the methodology.
Where they cover different ground.
The declared feature set.
6 of 6 declared features differ — listed first.
These are each tool's self-declared key_features; a blank dot means
undeclared, not impossible.
| Feature | Amundsen | OpenMetadata |
|---|---|---|
| Data Contracts Quality & testing | ||
| Schema Change Detection Quality & testing | ||
| Business Glossary Catalog & discovery | ||
| PII Auto-Classification Catalog & discovery | ||
| Column-Level Lineage Lineage & metadata | ||
| Table-Level Lineage Lineage & metadata |
Where they disagree.
Catalog & discovery
7 of 9 differ| Amundsen | OpenMetadata | |
|---|---|---|
| Business glossary | ||
| NL search | ||
| Data contracts | ||
| Governance flows | ||
| Access requests | ||
| PII auto-classify | ||
| Tag propagation |
Lineage & metadata
4 of 7 differ| Amundsen | OpenMetadata | |
|---|---|---|
| Column-level | ||
| Cross-system | ||
| Reverse impact | ||
| Historical |
When to pick each.
Teams that already run Amundsen and need to understand what they have, or teams with a genuinely minimal requirement — usage-ranked table search and ownership tracking, nothing more — who are comfortable owning a codebase that is no longer moving. The core idea still holds up: index tables, dashboards, and people into Elasticsearch, rank results by query-log usage so the tables analysts actually trust float to the top, and keep the UX focused on the single question "which table should I use?" Databuilder's pull model is plain Python, so extending it from an existing Airflow deployment is straightforward for a data platform team.
Teams that want an OSS catalog without the operational weight of DataHub's Kafka and graph-DB architecture. OpenMetadata's simpler stack — Postgres or MySQL plus Elasticsearch, no graph DB, no Kafka — makes it materially easier to stand up and keep alive. Particularly strong for shops that want one tool to cover discovery, governance, lineage, profiling, and quality together rather than glue several together. Connector breadth (120+) is the highest of the OSS catalogs, and the cadence of governance features in 2024–2025 (Multi-Domain, Data Contracts GA in 1.9, Data Quality as Code) has been faster than the competition.
What each does best.
Amundsen stands out for
- Pioneered usage-ranked, search-first discovery — PageRank-style ranking from query logs remains a genuinely good idea that successors copied
- Deliberately small surface area — analysts get table search, owners, column stats, and previews without a governance platform's learning curve
- Apache-2.0 under neutral LF AI & Data governance, with nothing held back for a paid tier
- Databuilder is plain-Python ETL — custom extractors are easy to write and schedule from an existing Airflow deployment
OpenMetadata stands out for
- Highest connector count in the OSS catalog space (120+) — particularly strong on dashboards, ML, and pipeline systems
- Deliberately simple architecture (no Kafka, no graph DB) makes self-hosting realistic for smaller platform teams
- Unified scope — discovery, lineage, governance, quality, contracts, and collaboration in one project, not a constellation of subsystems
- Faster shipping cadence on governance features through 2024–2025 (Multi-Domain, Data Contracts GA, Data Quality as Code, Auto-Tune)
Tools both also compete with.
All Amundsen alternatives, scored →All OpenMetadata alternatives, scored →
A note on this comparison.
Every capability value above traces to Amundsen or OpenMetadata's own structured spec, which links back to its source — nothing here is averaged or smoothed across the two.
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