Amundsen vs Secoda.
Amundsen and Secoda both anchor in catalog & discovery — 9 dimensions differ, 1 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.
AI-native data catalog, lineage, and observability from Toronto — acquired by Atlassian in December 2025 to power Rovo AI.
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.
Mid-market and scaleup data teams that want one AI-native tool covering catalog, search, lineage, documentation, and basic observability rather than running separate catalog, lineage, and monitoring tools — especially teams that value a natural-language assistant for self-serve data questions and broad business-user adoption.
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.
Acquired by Atlassian; announced via Secoda's blog (Dec 4, 2025) and reported by TechTarget (Dec 5, 2025). Terms undisclosed. Atlassian plans to fold Secoda's semantic cataloging into its Teamwork Graph / Rovo AI and migrate it onto the Atlassian Cloud Platform over time. As of mid-2026 Secoda still operates under its own brand with the founding team aboard; near-term customer experience is said to be unchanged. Founded 2021 in Toronto (Y Combinator); ~USD 14M Series A in 2023.
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.
Secoda's page doesn't directly mention Amundsen. See the Secoda detail page.
Each quote is pulled from the named tool's own "Where it fits" write-up.
Spec sheet diff.
| Amundsen | Secoda | |
|---|---|---|
| Vendor | LF AI & Data Foundation | Secoda (Atlassian) |
| Deployment | Self-hosted only | SaaS · Self-hosted |
| License | Open source | Proprietary |
| Pricing | OSS · paid tiers | Contact sales |
| Free tier | Yes | No |
| OSS self-host | Yes | No |
| dbt integration | Plugin | Native |
| OpenLineage | Consumer | None |
| Founded | 2019 | 2021 |
| HQ | — | Toronto, Ontario, Canada |
| Status | ● active | ○ acquired |
Full Amundsen pricing → Full Secoda pricing →
Both share Primary cluster: Catalog & discovery
Each tool's center of gravity.
| Cluster | Amundsen | Secoda |
|---|---|---|
| Lineage & metadata | 1/3 | 3/3 |
| Quality & testing | 0/3 | 0/3 |
| 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.
5 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 | Secoda |
|---|---|---|
| Business Glossary Catalog & discovery | ||
| PII Auto-Classification Catalog & discovery | ||
| Column-Level Lineage Lineage & metadata | ||
| Reverse Impact Analysis Lineage & metadata | ||
| Transformation Lineage Lineage & metadata | ||
| Table-Level Lineage Lineage & metadata |
Where they disagree.
Catalog & discovery
7 of 9 differ| Amundsen | Secoda | |
|---|---|---|
| Business glossary | ||
| NL search | ||
| Governance flows | ||
| Access requests | ||
| PII auto-classify | ||
| Tag propagation | ||
| Free self-host |
Lineage & metadata
3 of 7 differ| Amundsen | Secoda | |
|---|---|---|
| Column-level | ||
| Cross-system | ||
| Reverse impact |
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.
Mid-market and scaleup data teams that want one AI-native tool covering catalog, search, lineage, documentation, and basic observability rather than running separate catalog, lineage, and monitoring tools — especially teams that value a natural-language assistant for self-serve data questions and broad business-user adoption. A strong fit for organisations on Snowflake, BigQuery, or Databricks plus dbt and a modern BI tool who want fast time-to-value and lighter governance overhead than enterprise suites like Atlan or Collibra.
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
Secoda stands out for
- AI-native search and assistant as the primary interface — natural-language data questions across the catalog, plus purpose-built agents for search, documentation, observability, and governance
- Consolidated — catalog, data dictionary/glossary, column- and table-level lineage, governance, and no-code monitoring in one workspace
- Strong automated lineage including column-level, BI-tool coverage, impact analysis, and downstream/upstream owner notifications
- Fast time-to-value and broad business-user adoption relative to heavyweight enterprise catalogs, with 50+ no-code connectors
Tools both also compete with.
All Amundsen alternatives, scored →All Secoda alternatives, scored →
A note on this comparison.
Every capability value above traces to Amundsen or Secoda's own structured spec, which links back to its source — nothing here is averaged or smoothed across the two.
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