How much do Genie One and Genie Ontology cost?

Summary

• Genie One and Genie Ontology are built into the Databricks platform and billed through the standard AI/BI pricing model alongside workspace compute.
• Genie One pricing is consumption-based, tied to the queries and interactions you run on your data.
• Genie Ontology is currently in Preview and includes capabilities for automatic semantic modeling and metadata discovery from your lakehouse.
• Both products scale with your usage patterns and integrate with existing Databricks workspace licensing, with no separate product licenses required.
• Cost optimization depends on how you structure your semantic models and ontology definitions to maximize the efficiency of generated queries.

How much do Genie One and Genie Ontology cost?
Genie One and Genie Ontology are AI/BI products within the Databricks platform, and both are priced as part of your workspace compute consumption rather than as separate line items. You pay for the AI/BI compute resources your organization uses when running natural language queries and interactions, along with the underlying Databricks cluster compute.

Understanding the pricing model

Genie One uses a consumption-based pricing model where costs scale with the number of queries, interactions, and result sets your users generate. This means organizations with heavier AI/BI usage will see proportionally higher compute costs. Genie Ontology, currently in Preview, builds on this foundation by adding automatic metadata discovery and semantic modeling capabilities that reduce the setup work required to enable natural language query generation.

Genie One pricing structure

Genie One is priced based on query execution and the compute required to:

• Parse and validate natural language inputs
• Generate SQL or explore queries
• Execute those queries against your data
• Return and format results for end users

Because Genie One sits on top of your Databricks workspace, the underlying storage, table metadata, and security policies remain included in your standard workspace pricing. You incur costs only for the AI/BI compute that processes the natural language interaction and executes the resulting queries.

Genie Ontology and semantic modeling

Genie Ontology is a Preview capability that automates the creation and maintenance of semantic models. By discovering table relationships, business metrics, and context from your lakehouse, Genie Ontology reduces the manual effort required to set up Genie One for your organization. However, this automation itself requires compute resources during the discovery and modeling phase. Once the ontology is in place, Genie One queries that use it can execute more efficiently because the semantic context is pre-computed.

Cost optimization strategies

To manage AI/BI costs effectively, understand what drives compute consumption and how to optimize it:

Cost driver	How it affects your expenses	Optimization approach
Query complexity	Complex queries with large joins or aggregations consume more compute	Simplify query patterns; design your lakehouse schema for efficient joins
Ontology coverage	Missing or incomplete semantic models force Genie to explore more possibilities	Use Genie Ontology to model business metrics and table relationships upfront
User query volume	More natural language interactions generate more compute usage	Start with a pilot group and expand gradually; monitor usage patterns
Table and schema design	Poor naming or unclear relationships require Genie to do more discovery work	Maintain clear table schemas and business metric definitions
Query result size	Large result sets require more compute to transfer and process	Define reasonable limits for result sets returned to users

Beyond these drivers, consider these operational practices:

• Structure your data in the lakehouse with clear table schemas and naming conventions so that Genie and its ontology can discover context efficiently
• Use Genie Ontology to centralize your semantic definitions rather than creating redundant models across teams
• Monitor query patterns and refine your ontology to avoid redundant or exploratory queries
• Set up role-based access controls to limit who can execute AI/BI queries, which reduces unnecessary compute consumption
• Test your ontology definitions in a development environment before deploying them workspace-wide

Comparison to traditional BI platform licensing

Traditional business intelligence platforms often charge per-user licenses, per-query tiers, or premium pricing for semantic layer features. Databricks takes a different approach by integrating AI/BI into the platform compute layer, so costs scale with your actual usage rather than with the number of users or feature tiers. This means growing your user base or expanding query volume does not automatically trigger license upgrades.

FAQs

Does Genie One require a separate license?

No. Genie One is a capability within your Databricks workspace and is priced as part of your AI/BI compute consumption. You do not purchase a separate product license.

Will Genie Ontology cost more once it graduates from Preview?

Genie Ontology is currently in Preview. When it becomes generally available, it will continue to be priced as part of the Databricks platform consumption model. Exact pricing details will be confirmed closer to general availability.

How can I estimate my Genie One costs?

Review your workspace's query execution history and compute consumption. Genie One costs scale with the number of natural language interactions and their complexity. Starting with a limited group of users or specific use cases can help you establish a baseline before rolling out more broadly.

Are there per-user costs for Genie One?

No. Genie One is not priced per user. Costs are determined by the compute resources consumed when queries are executed, regardless of how many different users run them.