Doram vs Honeydew
Honeydew is a semantic layer your data team writes. Doram learns the layer from your company and asks your team to approve it.
DoramChecked on 17 October 2026. Every fact about Honeydew comes from its public website and docs. The links are at the end.
The short answer
Honeydew is a semantic layer that your data team writes. Once it is written, Honeydew serves it to almost every tool you can name, and a compiler, not the AI, writes the SQL.
Doram also compiles the SQL. The difference is who writes the model. In Honeydew, your data team does, in YAML or in its Studio. In Doram, the model is learned from your data, documents and tools, and your team approves what matters.
At a glance
| Question | Honeydew | Doram |
|---|---|---|
| What it is | A semantic layer for AI and BI | A context layer with its own AI analyst |
| Who builds the model | Your data team, in YAML or Studio. Coding agents can open pull requests. | Doram drafts it. Your team approves what matters. |
| What it reads | Schema, keys, comments, and models imported from BI tools. Looks up wiki pages over MCP. | Schema, query history, dashboards, docs, Slack, Jira, CRM |
| How the AI gets the number | The AI writes a semantic query. A compiler writes the SQL. | The AI picks approved facts. A semantic compiler writes the SQL. |
| Access rules | Can run as the real user, so warehouse rules apply | Reads your warehouse grants at every sync |
| Other AI tools | MCP, SQL over JDBC, GraphQL, Excel, many BI tools | MCP and API |
| Databases | Snowflake, Databricks, BigQuery | Postgres, MySQL, Snowflake, BigQuery, Databricks, Redshift and more |
What Honeydew does well
- The AI never writes SQL. In its words, “The AI decides what to ask. It never decides how.” A compiler writes the query, so the same question gives the same SQL.
- It serves everything. One model reaches MCP clients, BI tools like Power BI, Tableau and Looker, Excel, a SQL interface and a GraphQL API.
- Clear pricing and real user access. Prices are on its website. Queries can run as the real user over OAuth, so your warehouse rules apply.
Where they differ
Who writes the model
Honeydew expects your data team to write the model. It helps: it imports models from LookML, Tableau, Power BI and dbt, and coding agents like Claude Code can build parts of it and open a pull request. But the model is still something a person writes and keeps up.
Doram writes the first draft for you. It reads your query history, dashboards, documents, Slack, Jira and the CRM, finds the rules your company already follows, and keeps the evidence for each one. People step in where the evidence disagrees or a business call is needed.
What happens to your documents
Honeydew can look up pages in Notion, Confluence, Atlan or DataHub while it answers. The page is read, but it does not become part of the model.
Doram turns what it reads into rules your team approves. The rule, and the reason behind it, then sit in the model, and every answer that uses the rule shows it.
How changes are approved
Honeydew publishes through Git. You can merge at once or open a pull request. Its docs note that you cannot yet set merge rights per user or per branch, and that required reviewers are not enforced on Bitbucket.
Doram routes each change by risk. Small changes go live on their own, and a change to a number your team reports on waits for its owner.
Databases
Honeydew works with Snowflake, Databricks and BigQuery. Doram also reads Postgres, MySQL, Redshift, ClickHouse, SQL Server, Oracle and MongoDB.
Who should pick which
Pick Honeydew if you run on Snowflake, Databricks or BigQuery, your data team wants to write the model in Git, and you need one model to feed many BI tools.
Pick Doram if nobody has time to write the whole model, your rules live in queries, documents and tools, or your data sits in other databases.