Databricks has unveiled Unity AI Gateway, a governance layer aimed at helping companies manage the rising complexity and cost of enterprise AI. The offering is designed to give organizations centralized control over access, spending, observability and policy enforcement across models, agents, tools and related AI systems.
The release reflects a growing concern among enterprises as AI usage expands beyond isolated model calls and into broader agentic workflows. Databricks says the gateway is intended to help teams manage AI estates that include hosted models, external models, coding agents, agent harnesses, MCP servers and other frameworks without forcing them into a single provider stack.
A central theme of the product is spend management. Unity AI Gateway includes budget controls, rate limits and hard caps intended to keep token consumption in check across users, applications, teams and model providers. Databricks also says the system can track AI usage and surface observability data to help companies understand where costs are rising.
The company is positioning the product as a response to what it describes as a need for enterprise AI governance that keeps pace with rapid adoption. According to the product materials, the gateway can apply identity-aware policies, regulate what agents can access and log policy decisions as AI systems operate.
Databricks also says the gateway provides tracing and audit capabilities. It can capture prompts, traces, tool calls, payload logs and audit logs across AI interactions, giving organizations a way to monitor behavior, investigate incidents and support compliance requirements.
Beyond cost control, Unity AI Gateway is intended to serve as a centralized layer for AI operations. The company says it includes a centralized agent catalog for tracking AI services, agents, MCP servers and tools. It also adds traffic management features such as rate limits, load balancing and routing controls.
Databricks says the system can support fallback routing, automatically directing traffic to backup models during outages or quality problems. It also offers model governance capabilities that let teams compare quality, cost and latency using production traffic, then route requests across providers as needed.
Other features listed for the gateway include AI guardrails, audit logging, MLflow integration and a connection to Databricks Lakewatch for security monitoring and threat detection.
Databricks is also highlighting Omnigent on Databricks as part of the broader governance story. The company says Omnigent can run agents above existing harnesses, allowing users to combine Claude Code, Codex and custom agents in one workflow.
In that setup, Unity AI Gateway is used to enforce contextual policies at runtime, including safety and cost controls. Databricks says each session is traced in MLflow and can be shared through a single link.
The company frames this as a way for organizations to expand agent use across teams without losing visibility or control. It says centralized policies, auditability and data-aware controls are meant to help enterprises govern agent behavior as adoption spreads.
Databricks is also targeting coding teams that now rely on multiple AI tools across workflows. The company says Unity AI Gateway can help centralize observability and governance across coding agents, where teams may otherwise struggle to track spend and usage across different providers.
For enterprise tool access, Databricks says the gateway can help secure MCP-based connections by ensuring agents inherit user permissions and only reach approved tools. That is presented as a way to bring more structure to how AI systems interact with business applications and internal data.
The product launch underscores a broader market shift. As companies move from experimentation to production AI, vendors are increasingly competing not just on model access, but on the infrastructure needed to control costs, trace behavior and enforce policy at scale. Databricks is betting that governance will become a core requirement, not a side feature, for enterprise AI deployments.