Foundation Models
Recent items mentioning Foundation Models across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Hosted foundation models integrated with Unity Catalog, Lakeflow Jobs, and Databricks Apps are powering agent-based security review architectures that automate routine evaluations and route high-risk cases to human reviewers 3. Meanwhile, practitioners testing external model integrations should note that Gemini Foundation Model endpoints are unavailable within 14-day commercial trial environments 2.
Generated daily from the 3 most recent items mentioning Foundation Models. Click any [N] to jump to the source.
🚀 Massive updates came to Databricks Unity Gateway this past September!
Enterprise teams gained access to an expanded, multi-provider model hub managed directly through Foundation Model APIs . Whether you needed cutting-edge enterprise reasoning, lightning-fast inference or lightweight task processing, Unity Gateway expanded its lineup with the latest releases from OpenAI, Anthropic, xAI and DeepSeek. Here is a recap of the foundation models that landed on Unity Gateway throughout September 2026: 🤖 OpenAI GPT-6.1 Sol GPT-6 Sol & GPT-6 Luna GPT-6 Astra ⚡ xAI & DeepSeek xAI Grok 4.7 DeepSeek V4.1 Flash 🧠 Anthropic Claude Opus 5.5 Claude Fable 5.1 submitted by /u/Youssef_Mrini [link] [comments]
Gemini Foundation Model endpoint unavailable in 14-day commercial trial
How I built agent-based security reviews on Databricks
An agent-based security review layer on Databricks automates predictable evaluations while routing ambiguous or high-risk cases to human reviewers. Built using Unity Catalog, Lakeflow Jobs, Databricks Apps, and hosted foundation models, this governed architecture improves review cycle times and consistency without removing human authority.
The Web Search Your Agent Inherited Isn't Good Enough
Omnigent lets you define an agent once—model, tools, policies, limits—and deploy it across Claude Code, Codex, or the raw API instead of rebuilding it per harness, with model calls routed through Databricks Foundation Model APIs for unified cost tracking and governance. Swapping in Nimble for web search lifted benchmark accuracy from 46% to 71% while cutting search costs in half.
Feature enablement for Foundation Model Unity Catalog permissions
Community BrickTalk | Real-Time Data & AI: Tripwise Demo
Hey r/Databricks ! Join us for community BrickTalk on Thursday, September 24 , focusing on real-time data streaming, AI agents, and governance using Databricks. BrickTalks is a community event series where Databricks experts share real-world use cases, demos, and practical insights for building with Data and AI, giving customers a direct line to the people behind the products. In this session, we'll walk through a live demonstration of the Tripwise Demo , featuring: Sub-Second Transactions & Streaming: Device registration into Lakebase with sub-second reads/writes, plus telemetry streaming via Zerobus through a governed Medallion architecture. AI-Generated Offers & Pricing: Generating real-time agent offers using Foundation Model APIs and scoring behavioral data for usage-based renewal pricing. Natural Language Analytics: Enabling underwriters, product managers, and marketing teams to query governed insurance data in seconds using AI/BI Dashboards and Genie. Unified Governance: Managing safety, compliance, and control end-to-end with Unity Catalog. This is a great chance to see real-world architecture in action and ask questions directly to Databricks experts. When: Thursday, September 24 9:00 AM PT 12:00 PM ET 5:00 PM BST (London) 9:30 PM IST Register here and save your spot submitted by /u/Subject_Ant1789 [link] [comments]
How to enable guardrails to an existing LangGraph agent deployed on a Databricks Serving Endpoint?
I have a LangGraph agent deployed as a Unity Catalog model on a Databricks Serving Endpoint. User : -> Databricks Serving Endpoint: -> UC Model: ai_workspace.agent.agent: -> LangGraph Agent: -> LLM + Tools The serving endpoint shows AI Gateway enabled , but its configuration currently only shows usage tracking. I would like to apply Unity AI Gateway guardrails to this agent. When I go to AI Gateway → Model Service → Create Model Service , I can configure guardrails, but my existing UC model: ' ai_workspace.agent.agent' does not appear in the Destination list. The available destinations appear to be Databricks-hosted foundation models and other supported model providers. My questions are: Can Unity AI Gateway guardrails be applied directly to an existing LangGraph agent deployed on a Databricks Serving Endpoint? If not, can I configure the LangGraph agent to use a Model Service as its underlying LLM endpoint, so that the LLM requests are governed by AI Gateway guardrails? Would those guardrails apply only to the LLM request/response, or can they also govern the LangGraph agent's tool calls and overall execution? What is the recommended Databricks architecture for applying AI Gateway guardrails to a custom LangGraph agent? Is the AI Gateway enabled option on a custom/agent serving endpoint different from the newer Unity AI Gateway Model Service functionality?
Databricks-hosted Foundation Model APIs blocked by workspace rate limit of 0
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.
Enable Foundation Model APIs for AWS Marketplace trial workspace
Request to Enable Foundation Model Endpoint Rate Limits
Foundation Models API blocked — "Databricks-set rate limit of 0" even after adding payment method
How Ecolab rebuilt retail intelligence on Databricks and Anthropic Claude
Ecolab rebuilt retail intelligence on Databricks and Anthropic Claude, converting 700-page FDA manuals into real-time answers for frontline staff using Foundation Model APIs and cutting compliance report compilation from two weeks to under two minutes. The solution, a native Databricks App with Lakebase Postgres and Unity Catalog, unifies nine siloed data sources and employs a multi-agent orchestration framework with Judge LLMs and MLflow tracing for personalized, continuously refined intelligence.
Beyond parsing X12: Closing the gap for revenue cycle workflows in healthcare
Healthcare billers now have an operational workbench built on Unity Catalog gold views, providing a purpose-built UI with a denials queue, remittance drawer, and timely-filing age alerts directly on their fully parsed 835/834/837 EDI data. This solution integrates GenAI via Databricks Foundation Model APIs to auto-draft appeal letters, moving billers beyond manual spreadsheet and SQL work to review and approve instead of writing from scratch.
PipelineIQ: Forward‑Looking Sales Intelligence That Drives Action
Your CRM data is a mess. Everyone knows it. Most AI tools pretend it isn't. Databricks took a different approach with PipelineIQ - instead of building yet another forecasting model that assumes clean data (spoiler: it never is), they built a prescriptive action engine that works with the chaos. The result? Every deal in the pipeline gets one of three verdicts: 🚶 Walk - disengage, this isn't worth your time 🔄 Pivot - viable deal, wrong approach 🚀 Accelerate - conditions are right, lean in now No vague "insights." No dashboards that require a PhD to interpret. Just: here's what to do today. Built on Databricks' own stack (Foundation Model APIs, Delta Lake, Unity Catalog) and used internally by their own sales org - this is a rare "we built it for ourselves first" story. Read the full blog here: [https://www.databricks.com/blog/pipelineiq-forward-looking-sales-intelligence-drives-action](https://www.databricks.com/blog/pipelineiq-forward-looking-sales-intelligence-drives-action)