Unity AI Gateway

MLflow 3.16.0

MLflow 3.16.0 makes the redesigned trace explorer the default interface, introducing natural-language custom trace views via the MLflow Assistant, session grouping for multi-turn conversations, and span links. The update also adds Unity Catalog model service support for built-in evaluation judges, per-user AI Gateway budget policies, and fail-closed authorization by default.

Smart Routing in Unity AI Gateway: Match frontier quality with 30%+ lower cost per task

Smart Routing in Databricks Unity AI Gateway automatically directs coding tasks to the optimal model and harness across the price/performance frontier, matching frontier-level quality while cutting cost per task by more than 30%. The feature addresses the growing diversity of models and harnesses practitioners face by handling that selection for them rather than requiring manual tuning.

Kimi K3 from Moonshot AI is now available on Databricks through Unity AI Gateway

Moonshot AI’s most capable open-weight model, Kimi K3, is now available on Databricks through the Foundation Model API with full governance via Unity AI Gateway. Initially hosted in the US with comprehensive Zero Data Retention, the model integrates directly with your existing unified API, cost controls, guardrails, and observability workflows.

Unity AI Gateway is Generally Available

Unity AI Gateway is now Generally Available, giving organizations a unified way to govern spend, security, and access across models, agents, MCPs, skills, and tools. It provides end-to-end observability, smart routing, and runtime guardrails to manage growing token costs and protect sensitive data while preserving developer choice.

The New Monday Morning Report: How Generative AI can deliver the insights your executives need.

Rebuilding retail Monday morning reports with generative AI transforms stagnant weekly rituals by fusing internal and external signals, ranking SKU and store watch outs, and drafting executive recommendations. This workflow operates directly on your governed lakehouse using Genie Ontology for context, Unity AI Gateway for control, and support for any model across any cloud.

v1.124.0 adds Terraform resources and data sources for three AI Gateway components: model service, model provider service, and MCP service. This enables infrastructure-as-code management of AI Gateway deployments through Terraform.

MLflow 3.15.0 introduces an MCP Registry for registering and sharing Model Context Protocol servers, enhances the Assistant with multi-provider LLM support and per-session token usage tracking, and enables proxy-less artifact transfers via presigned URLs to reduce server load and timeouts on large files. Additional improvements include sharable Runs table views, multi-modal image attachments for LLM judges to evaluate vision tasks, and numerous bug fixes across tracing, evaluation, gateway, and UI components.

The SDK introduces a new AI Gateway workspace service and adds new methods and fields across bundle deployments, compute, jobs, serving endpoints, and other APIs. Breaking changes include a modified create_deployment() method signature and removed deployment_id and lifetime fields.

This release adds a new AI Gateway service accessible via workspaceClient.aiGateway(). Bundle deployments now support the updateOperation() method and include additional fields for tracking deployment metadata, version history, and update timing.

The release introduces a new workspace-level AI Gateway service for managing AI catalog operations. Bundle Deployments now support update operations and include additional fields for tracking version history, dashboard metadata, and modification details.

How Databricks manages its own coding agent spend with Unity AI Gateway Budgets

Databricks governs its internal AI spend by routing every coding agent through Unity AI Gateway for centralized policy enforcement, spend controls, and unified observability. By pairing separate daily and monthly budgets with self-service budget increases, this governance model prevents runaway costs without slowing down developer productivity.

Building a soccer coaching app on Databricks

Coach's Corner is a Databricks App that processes 25 fps match tracking data into a sub-second 2D/3D tactical bench with replays, event analytics, a scout chat, and an opponent-dossier agent. The end-to-end solution is powered entirely on the Databricks platform, utilizing Lakeflow pipelines for data refinement, DBSQL and Lakebase for rapid querying, and Unity Catalog-governed AI tools like Genie, Vector Search, and MLflow tracing.

Meta’s Spark Muse 1.1 is now available on Databricks, fully governed by Unity AI Gateway

Meta's new Spark Muse 1.1 model is now available on Databricks through Unity AI Gateway's Model Provider Services, allowing teams to securely access and govern any model from day one. By registering providers once in Unity Catalog, platform teams can eliminate API key sprawl, enforce centralized permissions and guardrails, and automatically track token usage, costs, and audit logs for end-to-end observability.

Inkling model from Thinking Machines Lab now on Databricks

The open-weights Inkling model from Thinking Machines Lab is now available on Databricks through the Unity AI Gateway, allowing practitioners to easily build and deploy AI agents and coding applications on enterprise data. This release enables teams to leverage a model optimized for coding, agentic reasoning, and multimodal inputs while maintaining centralized security, cost controls, and observability.

Data-Native AI Agents: Why Agents Must Move to Your Data

AI agents must run directly within your data stack rather than in a separate, external stack to avoid compounding penalties like fragmented governance, high egress costs, and latency. By deploying data-native agents on the Databricks Data Intelligence Platform, enterprises can leverage an integrated stack of Unity Catalog, AI Search, MLflow, Lakebase, and AI Gateway to ship secure, trusted AI features faster.

Databricks positioned highest in execution and furthest in vision for the second consecutive year in Gartner Magic Quadrant

Databricks is recognized as a Leader in the 2026 Gartner Magic Quadrant for AI Platforms for Data Science and Machine Learning, positioned highest in execution and furthest in vision for the second consecutive year. This reflects the market shift towards deploying agentic applications that reason on governed data, enabled by Databricks' unified data, AI, and governance platform with Unity Catalog and Unity AI Gateway.

Building an open ecosystem for AI governance with Unity AI Gateway

Unity AI Gateway now integrates with 10 security vendors to protect AI interactions and 3 identity providers to govern AI agent access. This enables centralized visibility and control over AI activity, policy enforcement, and risk management across models, agents, and tools.

MLflow 3.14.0 adds one-command agent setup with Databricks support and durable low-latency Claude Code tracing, Review Queues for trace annotation and feedback collection, and @mlflow.test pytest markers for regression testing. Default model serialization formats change for sklearn to skops, PyTorch to pt2, and LightGBM to skops.

What’s new with Unity Catalog at Data + AI Summit 2026

Unity AI Gateway now extends Unity Catalog's runtime governance to AI agents, models, and tools, allowing you to govern agent actions, not just data access. Glossary and Domains provide a shared, governed source of business context for both people and agents, while a single catalog and policy set ensure consistent governance across all clouds and regions.

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

Unity AI Gateway now offers unified cost management, governance for AI assets and interactions, and monitoring for AI activity at scale. These new capabilities, announced at Data + AI Summit 2026, extend Unity Catalog to models, agents, and services, while providing spend visibility, runtime controls, and security features.

Unifying Data and Governance in the Agentic Era: What’s New with Azure Databricks

Azure Databricks now offers new capabilities for unifying data and governance in the agentic era, including the industry's first true LTAP architecture, serverless Postgres database branching, and millisecond-level response times via Lakehouse//RT. These updates also bring Genie for Microsoft Teams and M365 Copilot, the new Azure Databricks Excel Add-in, and Azure Databricks CustomerLake, a lakehouse-embedded Agentic CDP.

How to Manage your LLM Teams using MLflow's Role-Based Access Control

MLflow's new Role-Based Access Control (RBAC) helps LLM teams define reusable roles, isolate workspaces, and enforce fine-grained permissions across prompts, experiments, and AI Gateway resources. Learn how to manage your LLM teams using these new MLflow RBAC capabilities.