Doram vs Atlan
Atlan is a catalog that feeds context to other AI engines. Doram learns the context and also answers the question.
DoramChecked on 17 October 2026. Every fact about Atlan comes from its public website, docs and GitHub. The links are at the end.
The short answer
Atlan is a data catalog that now calls itself “The Context Layer for AI”. It knows what data you have, who owns it and how it flows, and it feeds that context to other AI tools. In its own words, “Atlan does not generate SQL.”
Doram is a context layer too, and it also answers the question. It learns your business rules, a compiler turns them into SQL, and its own analyst returns the number. Other AI tools can read the same rules.
At a glance
| Question | Atlan | Doram |
|---|---|---|
| What it is | A data catalog and context layer for other AI tools | A context layer with its own AI analyst |
| Who builds the model | AI agents draft it. People review and switch it on. | Doram drafts it. Your team approves what matters. |
| What it reads | 108 connectors, query history, lineage, BI tools | Schema, query history, dashboards, docs, Slack, Jira, CRM |
| How the AI gets the number | Another engine answers, like Snowflake Cortex or Databricks Genie | The AI picks approved facts. A semantic compiler writes the SQL. |
| Access rules | Column masking. No row rules of its own. | Reads your warehouse grants at every sync |
| Other AI tools | MCP with 39 tools, SQL, REST and graph APIs | MCP and API |
| Where it runs | Atlan cloud, single tenant, with a connector in your network | Doram Cloud, or your own cloud account |
What Atlan does well
- It knows your whole data estate. 108 connectors, lineage worked out from SQL, pipelines and BI, and AI agents that write descriptions from real query history.
- One context, many engines. It turns one model into the format each engine needs: Snowflake Cortex Analyst, Databricks Genie, dbt and Hex. Its MCP server works with Claude, ChatGPT, Cursor and more.
- Mature governance. Approval workflows, Verified and Deprecated badges, masking and audit logs. Gartner named it a Leader for data governance in 2026.
Where they differ
Who answers the question
Atlan sits upstream. It prepares the context, and another engine, like Cortex Analyst, Genie or Claude, writes the SQL and returns the number. Atlan’s own chat answers from metadata, like descriptions, owners and lineage.
Doram does both jobs. It holds the rules, and its semantic compiler writes the SQL from approved facts. The same question gives the same answer, and any past answer can be replayed on the version that made it.
Where the rules come from
Atlan reads far more systems than Doram: more than a hundred connectors across warehouses, BI, pipelines and data quality. Its product page also lists Confluence, Notion and Slack. As far as its docs show, there is no ticket or CRM source for business rules.
Doram reads fewer systems, but it reads the tools where decisions are made, like Slack, Jira, the CRM and billing. That is often where the reason behind a rule lives. Doram keeps that reason with the rule.
How changes are approved
Atlan can send changes to descriptions, badges, owners and terms through an approval workflow. A model starts as a draft and someone switches it on. Its marketing also says that high-confidence output applies on its own.
Doram routes each change by risk. Small changes go live on their own, one in ten medium changes is spot-checked, and a change to a number your team reports on waits for its owner.
Access
Atlan masks columns by rewriting the SQL, so unmasked data never reaches it. We found no row rules of its own. It suggests that each user queries with their own warehouse login.
Doram reads your warehouse grants at every sync, and applies row rules under every total. When two rules disagree, the stricter one wins.
Who should pick which
Pick Atlan if you need a full data catalog with lineage across many tools, you already use Cortex Analyst, Genie or Hex to answer questions, and governance across the whole estate is the goal.
Pick Doram if you want one product that learns the rules and answers with them, with SQL compiled from approved facts and access that follows your warehouse grants.