AI governance at Data + AI Summit 2026: What’s new with Unity AI Gateway

AI governance at Data + AI Summit 2026: What’s new with Unity AI Gateway | Databricks Blog Skip to main content

Summary

• Optimize AI usage with unified cost management, including spend visibility across providers, granular attribution, hard spend caps, and intelligent routing to balance quality and cost.

• Govern AI assets and interactions in one place by extending Unity Catalog to models, agents, MCP services, and skills, while enforcing runtime controls and guardrails through Unity AI Gateway.

• Monitor and secure AI activity at scale with unified tracing, coding agent observability, Lakewatch investigations, and an open ecosystem of security, identity, and governance partners.

AI is becoming increasingly multi-model, multi-agent, and multi-vendor. Developers are adopting coding agents, while business users are interacting with enterprise data through AI experiences like  Genie . Many enterprises are also launching custom agents to automate critical internal workflows. As organizations scale from individual AI applications to fleets of agents connected to models, MCP services, APIs, and enterprise tools, governance challenges expand beyond model access alone. Organizations need visibility, runtime controls, security guardrails, and cost management across their entire AI estate. Unity AI Gateway is Databricks' governance solution for enterprise AI. Built on the foundation of  Unity Catalog , it extends governance beyond data and AI assets to the runtime interactions between models, agents, MCP services, skills, and enterprise tools. Available across AI providers, coding agents, agent frameworks, enterprise applications, and custom AI systems, Unity AI Gateway delivers centralized governance, security controls, cost management, and agent monitoring for enterprise AI. At Data + AI Summit 2026, we're announcing major new innovations across four areas: Optimize AI usage with cost controls and smart routing: Gain visibility into AI spend across disparate tools and models, enforce hard spend caps, and intelligently route workloads to balance quality and cost. Govern AI assets and interactions in one place: Govern models, MCP services, agents, and skills through Unity Catalog while enforcing contextual policies at runtime through Unity AI Gateway. Monitor and investigate AI activity: Capture end-to-end traces, analyze coding agent activity with Genie, and investigate incidents with Lakewatch. Extend governance through an open ecosystem:  Integrate leading AI security, identity, data protection, and threat detection providers to bring trusted controls into runtime AI workflows.

Optimize AI usage with cost controls and smart routing AI costs are increasingly fragmented, making it difficult to understand where token costs are occurring and how to optimize them. Today, we're introducing new capabilities in Unity AI Gateway that help organizations gain visibility into AI usage, control costs, and optimize spend across their AI estate. Unified AI spend visibility:  Track spend across Databricks-hosted models, frontier model families, coding agents, enterprise AI applications, and custom agents from a single view. Granular cost attribution:  Analyze AI spend and set budgets by user, team, tool, and use case to understand where costs are occurring and where AI is delivering value. Hard spend caps:  Automatically stop requests when budgets are exceeded to prevent runaway costs and enforce spending guardrails. Smart routing: Receive recommendations and intelligently route requests to the most appropriate model based on task complexity, quality requirements, and cost.

"On Udemy's data platform, we route all foundation model traffic through Databricks AI Gateway, giving us a single governance layer for the entire lifecycle. From production agents running on Claude to PII detection pipelines that intelligently balance smaller and larger GPT models for cost efficiency, everything is governed consistently with unified access control and clear cost attribution."  — Nathan Sullins, Principal Software Engineer, Udemy Govern AI assets and interactions in one place As organizations scale AI, the number of models, agents, MCP servers, tools, and skills quickly multiplies. Yet most enterprises still govern each of these assets separately, if at all, leading to fragmented systems, inconsistent access policies, and limited visibility into what agents do and how AI systems are being used. Today, we're extending Unity Catalog to govern AI assets beyond models. Databricks-hosted models, external model providers, MCP services, agents, and skills can now be registered, discovered, secured, and audited using the same governance framework organizations already use for data.  Administrators gain centralized visibility through permissions, lineage, observability, and cost management, while Unity AI Gateway enforces runtime controls across model calls, tool invocations, and agent workflows. Together, Unity Catalog and Unity AI Gateway provide a unified governance layer for both AI assets and AI interactions. “AI has the potential to enhance efficiency across the enterprise, but scaling AI responsibly requires strong governance, transparency, and trust. Databricks has helped our team build the unified foundation we need to help govern AI systems, protect sensitive data, and support enterprise-wide AI adoption in a regulated industry.” — George Torres, Senior Director of AI Engineering, First American Govern models across providers Organizations increasingly rely on models from multiple providers.  Admins can now govern foundation model access through Unity Catalog using the same fine-grained access policies they use for data. Policies can be dynamically applied based on attributes such as model provider, country of origin, approval status, or any governed tag, making it easier to consistently enforce controls as model inventories grow. Govern any MCP service As MCP adoption grows, organizations need a consistent way to govern access to enterprise tools. Databricks now provides managed MCP services for applications including Google Drive, Jira, Confluence, Slack, GitHub, and SharePoint , giving teams governed, ready-to-use integrations without managing their own infrastructure. Now you can also register custom MCP services, creating a centralized inventory of approved tools and integrations. Admins can manage access, enable or disable individual tools, and audit usage, while developers...