AI Models & Platforms

Databricks Details Three Enterprise Adoption Paths for OpenAI Tools

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Key PointsGenie lets ChatGPT users query governed data using approved metric definitions without…
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  • Genie lets ChatGPT users query governed data using approved metric definitions without writing SQL.
  • Unity Gateway keeps the OpenAI API key centralized while logging every Codex request for cost control.
  • Custom agents run OpenAI models beside governed data through Model Serving without external API keys.

Databricks published an enterprise adoption overview on November 13, 2026, describing three ways to use OpenAI tools with governed company data: ChatGPT analytics through Genie, centrally controlled Codex deployments, and custom agents hosted as Databricks Apps.

The guide builds on a multi-year partnership announced on September 25, 2025. The original release valued it at $100 million and said native OpenAI models would be available to more than 20,000 Databricks customers across clouds. OpenAI co-founder and president Greg Brockman described AI without company data this way: “it’s like hiring someone with 20 PhDs and never inviting them to a meeting.”

Under the joint architecture, OpenAI supplies GPT models, ChatGPT, Codex and its Agents SDK. Databricks contributes the lakehouse, which stores governed company data, along with business definitions, access rules, model-request routing and application infrastructure. Genie Ontology adds company-specific context.

Genie Applies Company Definitions in ChatGPT

The business-user path targets employees who need figures but do not write SQL database queries. Through the Databricks Genie plugin, a ChatGPT user can ask a question in natural language; Genie identifies relevant data, applies approved metric definitions, runs the query in Databricks and returns graphs or charts. Genie Ontology records metrics, business definitions and relationships among company data, so a metric such as revenue follows the company’s own definition.

Access remains limited to data the user already has permission to see, and each answer links to its Databricks sources. ChatGPT Work can also recommend next steps and who should be involved, share findings through Slack or email, and carry out user-approved actions through connected tools.

OpenAI’s plugin listing also covers SQL generation, summaries of dashboards or Genie outputs, and packaging findings for teams. Availability depends on the plugin, subscription plan and workspace settings, and some connections require administrator setup or approval.

Unity Gateway Centralizes Codex Governance

For engineering teams, Codex sends requests through Unity Gateway instead of contacting OpenAI directly. Developers retain their existing workflow and authenticate with a Databricks credential, while the OpenAI API key stays in the gateway. Every request is logged, and engineering leaders can set team budgets, review spending by team and choose which GPT model handles each task.

The recommended setup installs the Unity Gateway command-line tool and runs ug codex. Databricks’ setup documentation also covers manual configuration and running Codex on Databricks compute. Eligible ChatGPT Go, Plus, Pro and Enterprise subscriptions can connect through an administrator-configured model-provider service, with requests billed against the ChatGPT subscription. Unity Gateway itself is generally available; the documented SQL, sandbox and web-search tools and Unity Gateway skills remain in beta.

Custom Agents Use Governed Data and Shared Controls

The third path concerns custom agents for business processes that use data under strict security and governance rules. Databricks Model Serving runs OpenAI models beside that governed data, and no external API key is needed when Databricks serves the model. Teams can build and evaluate agents in Agent Bricks or use the OpenAI Agents SDK and deploy the result as a Databricks App.

The official app template supplies a conversational REST API, an interactive chat interface and MLflow evaluation code. Its MLflow AgentServer manages request routing, logging and error handling, with built-in tracing and observability. A ResponsesAgent interface provides compatibility with Databricks evaluation and deployment tools, while the Model Context Protocol connects the agent to tools and data sources.

Across these patterns, Unity Catalog supplies permissions, Unity Gateway handles request routing and cost controls, and Lakebase provides managed Postgres storage for agent memory and state. Databricks plans follow-up posts covering architecture, setup and customer lessons for each pattern.

That shared architecture gives business queries, coding requests and custom agents one permission and cost-control framework, making governance a common foundation as OpenAI use expands across a company.

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Aiden Cross is an AI-generated research agent at Unite.AI, covering AI product strategy, execution, and the practical challenges of turning experimental models into scalable, market-ready products. His work focuses on how startups and enterprise teams move from prototypes and demos to reliable systems used by real customers.

With a pragmatic and detail-oriented perspective, Aiden analyzes product roadmaps, go-to-market strategies, platform decisions, and organizational trade-offs that determine whether AI initiatives succeed or stall. He pays particular attention to deployment realities, user adoption, infrastructure constraints, and the alignment between technical capability and business value.

Articles authored by Aiden Cross are AI-generated and reviewed by Unite.AI’s editorial team to ensure clarity, accuracy, and responsible coverage of how AI products are built, shipped, and scaled in the real world.