• 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:

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.

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.