Atlan vs Secoda.
Atlan and Secoda both anchor in catalog & discovery — 3 dimensions differ, 3 hold. Below: posture, coverage diff, and capability matrix.
What each is betting on.
Series C ($105M, May 2024) led by GIC and Meritech at a ~$750M valuation. Through 2025–2026 repositioned around 'The Context Layer for AI' — Iceberg-native metadata lakehouse, MCP server for AI agents, Context Engineering Studio. Named a Gartner MQ and Forrester Wave leader 2025. Heavy enterprise positioning; no self-serve free tier.
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.
Atlan's page doesn't directly mention Secoda. See the Atlan detail page.
Secoda cross-shops most directly with atlan, datahub, and openmetadata as a catalog/discovery plane, and with unity-catalog for teams already on Databricks. Against Atlan it positions as faster-to-value, lighter-weight, and more self-serve for mid-market teams; against the OSS catalogs it trades open-source portability for a polished AI-first UX and bundled observability. It is not a dedicated data-quality engine like monte-carlo, anomalo, or soda — its monitoring is consolidated and metadata-driven rather than deep test authoring, which is why we score the quality-testing cluster zero.
Each quote is pulled from the named tool's own "Where it fits" write-up.
Spec sheet diff.
| Atlan | Secoda | |
|---|---|---|
| Vendor | Atlan | Secoda (Atlassian) |
| Deployment | Hybrid | SaaS · Self-hosted |
| OpenLineage | Consumer | None |
| Founded | 2019 | 2021 |
| HQ | Singapore | Toronto, Ontario, Canada |
| Status | ● active | ○ acquired |
Both share Primary cluster: Catalog & discovery · License: Proprietary · Pricing: Contact sales · Free tier: No · OSS self-host: No · dbt integration: Native
Each tool's center of gravity.
| Cluster | Atlan | Secoda |
|---|---|---|
| Quality & testing | 0/3 | 0/3 |
| Catalog & discovery | 3/3primary | 3/3primary |
| Lineage & metadata | 3/3 | 3/3 |
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 8 declared features differ — listed first.
These are each tool's self-declared key_features; a blank dot means
undeclared, not impossible.
| Feature | Atlan | Secoda |
|---|---|---|
| Data Contracts Quality & testing | ||
| OpenLineage-Native Lineage & metadata | ||
| Reverse Impact Analysis Lineage & metadata | ||
| Table-Level Lineage Lineage & metadata | ||
| Transformation Lineage Lineage & metadata | ||
| Business Glossary Catalog & discovery | ||
| PII Auto-Classification Catalog & discovery | ||
| Column-Level Lineage Lineage & metadata |
Where they disagree.
Catalog & discovery
1 of 9 differ| Atlan | Secoda | |
|---|---|---|
| Data contracts |
Lineage & metadata
1 of 7 differ| Atlan | Secoda | |
|---|---|---|
| Historical |
When to pick each.
Mid-market and enterprise organisations with a real data-governance function — a CDO, stewards, a defined glossary programme — who need a polished, integration-rich catalog with strong column-level lineage and an opinionated view of how AI agents should consume metadata. Particularly strong for teams already on a modern stack (Snowflake or Databricks plus dbt plus Looker or Tableau) where Atlan's SQL parser and OpenLineage ingestion can light up lineage with relatively little manual work. The 2025 MCP-server pitch lands well for organisations actively wiring up Claude, Cursor, or internal agents and wanting a single governed surface those agents query for context.
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.
Atlan stands out for
- Polished UX and onboarding — consistently scores top in analyst rankings on time-to-value relative to peers
- Lineage built from four signal sources (SQL parsing, native APIs, OpenLineage events, manual) gives broad coverage without forcing one approach
- Iceberg-native 'Metadata Lakehouse' architecture (rolled out in 2025) decouples metadata storage from compute and supports versioned/time-travel views
- First-class MCP server and AI-agent context surface — the 2025 repositioning is real product, not just marketing
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.
A note on this comparison.
Every capability value above traces to Atlan 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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