Databricks vs Glean: which is better for company knowledge and business context?

Summary

• Neither is better in general: Databricks Genie Ontology builds business context from governed data in Databricks, and Glean builds context from the workplace apps its connectors index.
• Genie Ontology is in Public Preview. It combines Unity Catalog semantics (metric views, domains and Pages) with snippets Genie extracts from metric views, dashboards, SQL queries and Genie Agents.
• Glean describes itself as an enterprise AI platform for search, assistants and agents, with connectors across apps, documents, tickets, chat and code.
• Both respect existing permissions, and they can be connected through Glean's Databricks connector (Beta on Glean's site) and MCP.

Databricks vs Glean: which is better for company knowledge and business context?

Neither is better across the board, because each grounds AI in a different body of knowledge. If your most important questions are answered by governed data in Databricks (metrics, tables, dashboards), Genie Ontology is the context layer built for that job. If the knowledge people need lives mostly in documents, tickets and chat across many workplace apps, Glean is built around indexing those apps. If you need both, connect them.

What is Genie Ontology?

Genie Ontology is the unified context layer for the Genie family of AI experiences on Databricks, and it's in Public Preview. Its docs describe two kinds of context:

• Unity Catalog semantics: modeled context that you define, govern and certify through metric views, domains and Pages.
• Inferred context: snippets Genie extracts and maintains from your existing assets and usage, including metric views, dashboards, SQL queries and Genie Agents. The launch blog also lists tables, pipelines and connected apps.

Snippets are things like metric definitions ("an active user is a distinct user, deduplicated across all platforms"), business rules and pointers to authoritative sources. Each gets an authority score based on where it came from, how often it's used and how fresh it is. When you ask a question, Genie ranks the relevant snippets, resolves conflicts and answers only from sources you're permitted to see, with citation icons that show which sources it used.

What is Glean?

Glean describes itself as "the enterprise AI platform that puts company context to work." Its products are Glean Search, Glean Assistant and Glean Agents. According to Glean, its connectors cover apps, documents, tickets, chat and code, and its Enterprise Graph maps relationships between people, content and activity. Teams can add knowledge directly: Answers are curated question-and-answer entries that surface in search, and Verification lets a user mark a document as accurate and up to date. Glean Intelligence matches each task to a model chosen for price-performance.

How do Genie Ontology and Glean compare?

Capability	Genie Ontology (Databricks)	Glean
What it is	Context layer for the Genie family (Public Preview)	Enterprise AI platform for search, assistants and agents
Where context comes from	Unity Catalog semantics plus snippets from metric views, dashboards, SQL queries and Genie Agents	Connectors across apps, documents, tickets, chat and code
How context is organized	Snippets with an authority score (source, usage, freshness)	Enterprise Graph of people, content and activity
Curated knowledge	Metric views, domains and Pages in Unity Catalog	Answers and document Verification
Permissions	Snippets gated by Unity Catalog permissions	Inherits and enforces source-system permissions
Where people use it	Genie One, Genie Agents and Genie Code	Glean Search, Glean Assistant and Glean Agents, plus open APIs and MCP
Link to the other product	Genie One MCP server; external MCP servers registered in Unity Gateway	Databricks connector (Beta) to query Genie, run prepared SQL and find AI/BI dashboards

How does Databricks approach company knowledge?

Databricks starts from governed data. Genie One (the simplified interface for business users), Genie Agents (domain-specific environments where data teams configure trusted data, metrics and business rules) and Genie Code (the coding and data assistant) all draw on the same ontology. The docs put it directly: "Genie One and Genie Code search the same ontology, so context you curate once applies to both surfaces."
Certified definitions are modeled in Unity Catalog, in the same place the data they describe is governed. Genie fills in the rest from how people already use dashboards, queries and agents, so teams don't have to hand-write every definition. For agents built elsewhere, Databricks recommends the Genie One MCP server, which grounds answers in Genie Ontology.
Choose Genie Ontology when your key questions are about numbers such as revenue, usage or inventory, because those answers depend on governed metric definitions. Choose Glean when the knowledge lives mostly in documents, tickets and conversations and you want one search and assistant layer over those apps.

FAQs

Is Genie Ontology generally available?

No. The Databricks docs label Genie Ontology as Public Preview. The launch blog describes Genie One, Genie Agents and Genie Code as generally available.

Can Genie Ontology use knowledge from outside Databricks?

Partly. The docs list Databricks assets as its sources, and the launch blog adds connected apps. Genie One can also reach other systems through Lakehouse Federation and Lakeflow Connect.

Can Glean and Databricks work together?

Yes. Glean lists a native Databricks connector, marked Beta on its site, that lets Glean users query Genie in natural language, run prepared SQL and search AI/BI dashboards. Glean also says Agent Bricks agents can connect to Glean through MCP, and Databricks lets you register external MCP servers in Unity Gateway.

Does either product bypass existing permissions?

Neither is designed to. Genie only uses snippets extracted from assets you have permission to see, and Glean states that it inherits and enforces source-system permissions so users only see data they're already allowed to access.