Atlan vs DataHub.
Atlan and DataHub both anchor in catalog & discovery — 5 dimensions differ, 2 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.
DataHub originated at LinkedIn (open-sourced February 2020); Acryl Data was founded 2021 by ex-LinkedIn engineers to build the managed product. Series A $21M (2022, 8VC); Series B $35M (2024, Bessemer). 2024–2025 rebrand consolidated the OSS and managed offerings under a single 'DataHub' brand, with 'DataHub Cloud' replacing the older 'Acryl Cloud' name.
Each tool's current strategic narrative, verbatim from its profile.
How each tool describes the other.
The natural comparison is to datahub and openmetadata. Both are Apache-2.0 with credible managed counterparts; Atlan is fully proprietary. The decision usually comes down to two questions: how mature is the buyer's governance programme (Atlan's UX is built around stewards and certifications in a way the OSS catalogs aren't yet), and is OSS portability a hard requirement (in which case the OSS catalogs win by default).
Against atlan, DataHub is the OSS counterpoint. Atlan has the more polished governance UX and the bigger enterprise GTM; DataHub has the open license, the stronger lineage parser, and a credible OSS-to-managed graduation path. Buyers with a hard OSS requirement skip Atlan; buyers with a hard "no platform team" requirement skip OSS DataHub.
Each quote is pulled from the named tool's own "Where it fits" write-up.
Spec sheet diff.
| Atlan | DataHub | |
|---|---|---|
| Vendor | Atlan | Acryl Data |
| Deployment | Hybrid | SaaS · Self-hosted |
| License | Proprietary | Open source |
| Pricing | Contact sales | OSS · free |
| Free tier | No | Yes |
| OSS self-host | No | Yes |
| Founded | 2019 | 2021 |
| HQ | Singapore | Palo Alto, CA |
Both share Primary cluster: Catalog & discovery · dbt integration: Native · OpenLineage: Consumer · Status: ● active
Each tool's center of gravity.
| Cluster | Atlan | DataHub |
|---|---|---|
| Quality & testing | 0/3 | 2/3 |
| 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.
3 of 7 declared features differ — listed first.
These are each tool's self-declared key_features; a blank dot means
undeclared, not impossible.
| Feature | Atlan | DataHub |
|---|---|---|
| Schema Change Detection Quality & testing | ||
| PII Auto-Classification Catalog & discovery | ||
| Table-Level Lineage Lineage & metadata | ||
| Data Contracts Quality & testing | ||
| Business Glossary Catalog & discovery | ||
| Column-Level Lineage Lineage & metadata | ||
| OpenLineage-Native Lineage & metadata |
Where they disagree.
Catalog & discovery
1 of 9 differ| Atlan | DataHub | |
|---|---|---|
| Free self-host |
Lineage & metadata
0 of 7 differNo disagreement on any of the 7 capabilities in this cluster — they match across the board.
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.
Engineering-led data platforms that want an open, extensible metadata layer they can shape to their stack — with a credible managed escape hatch (DataHub Cloud) when self-hosting Kafka, Elasticsearch, and the graph store stops being fun. Particularly strong for organisations that already think in events: DataHub's Kafka-based Metadata Change Log makes it a natural fit for shops that want metadata to flow the same way data does. The SQL parser is genuinely best-in-class in the OSS catalog space, with SQLGlot-based column-level lineage benchmarked at 97–99% accuracy on standard corpora — materially better than competing parsers. A good fit also for teams wiring DataHub into AI agents via the native MCP server.
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
DataHub stands out for
- Best-in-class column-level SQL lineage parser (SQLGlot-based, benchmarked at 97–99% accuracy on standard corpora)
- Event-driven Kafka MCL architecture — metadata changes are a stream, not a snapshot, which composes well with downstream consumers
- Native OpenLineage consumer endpoint plus dedicated Spark and Airflow plugins
- Open-core model with a credible managed product (DataHub Cloud) means buyers can start free and graduate without a re-platforming
Tools both also compete with.
A note on this comparison.
Every capability value above traces to Atlan or DataHub's own structured spec, which links back to its source — nothing here is averaged or smoothed across the two.
Notice something inaccurate? Send a correction.