Unity AI Gateway

Governing AI Strategy with Unity AI Gateway

Azure Databricks, two-week-old account: partner pay-per-token endpoints (Claude, Grok) have never once succeeded — "Databricks-set rate limit of 0" — and the Assistant has "no daily token allowance". Open models work. What gates this?

Azure Databricks, Premium, one account with three workspaces, Unity Catalog, Azure Marketplace billing (no card, no trial credits left). The account was bootstrapped on a trial workspace about two weeks ago and moved to Premium six days later. I've spent two days on this and want a sanity check from anyone who has seen it. The facts, from system.serving.endpoint_usage: • Every open-weight pay-per-token endpoint has worked since the trial: gpt-oss-120b has ~50 successful calls going back to the trial period, Llama 3.3 70B likewise, zero refusals. • No partner pay-per-token endpoint has ever returned a success on this account. The very first call anyone made to databricks-claude-sonnet-5 was refused, and every call since, on every Claude endpoint and on Grok, in all three workspaces, for users and service principals alike: 403 PERMISSION_DENIED: The endpoint is temporarily disabled due to a Databricks-set rate limit of 0. • It is not our AI Gateway config: I removed every rate limit from one Claude endpoint and invoked it — same 403. The endpoints' config shows nothing else. • The notebook Assistant worked during the trial. On the paid account it refuses everyone, admins included: *"This workspace has no daily token allowance for the assistant."* Underneath, /ajax-api/2.0/conversation/llmproxy/ returns 429 {"type":"daily_token_limit_reached","daily_limit_tokens":0,"message":"…Daily token limit of 0 reached. Resets at midnight UTC. (DTB)"} and it recomputes to 0 every midnight. • Once, the Databricks Apps in the workspace were stopped by the platform with *"App compute was stopped due to workspace or account status"* while every workspace showed RUNNING. apps start brought them back. Red herring, for completeness: we also created a Unity AI Gateway per-user budget whose first version had a $0 threshold with BLOCK_USAGE. That blocked Genie for a day, exactly as documented, and was fixed. It was created a day *after* the first Claude refusal, so it isn't the cause of any of the above. What I've found: two Community threads describe "Databricks-set rate limit of 0" as a workspace trust-tier gate — trial-born accounts sit in TRIAL_VERIFIED, pay-per-token partner models are gated to PAYABLE_VERIFIED, a card alone doesn't flip it, only Databricks Sales/Support moves the tier. Both were AWS/personal accounts, neither resolved on-page. The account console's own API reports our account's feature_tier as STANDARD_W_SEC_TIER although the workspaces are Premium; I can't find what that field means. Questions • Does Azure Databricks with Marketplace billing go through the same PAYABLE_VERIFIED gate? Does it clear on its own after the first settled invoice, or does someone have to move it? • If you were moved: who did you contact (account team, or "Contact us" in the console) and how long did it take? • Is the Assistant's "daily token allowance" the same gate, or a trial allowance that simply goes to 0 when the trial ends on an unverified account? • Does feature_tier: STANDARD_W_SEC_TIER on the account object mean anything to anyone? submitted by /u/sumit671 [link] [comments]

Balancing Model Agility and Centralized Governance with Mosaic AI Gateway

AI is apparently getting a new name: SI (Super Intelligence)

Donald Trump said at the UN that super-intelligence will replace artificial intelligence in US government documents. If you work with Databricks, this probably sounds strangely familiar. So now I’m wondering… was Databricks just preparing for this? Because there are still plenty of names left: AI Search → SI Search? AI Functions → SI Functions? AI Runtime → SI Runtime? AI Gateway → SI Gateway? P.S. I keep track of Databricks renames and product changes on REbricked.org submitted by /u/Significant-Guest-14 [link] [comments]

Databricks 5 Minute Features: Unity AI Gateway

Seems like there is a bit of naming-confusion with Unity AI Gateway - but that should not take away from the feature itself. Have you started using it yet? In this video I try to give you an overview of how and why to use it! submitted by /u/Remarkable_Rock5474 [link] [comments]

What is release status Databricks Micro Apps?

The serverless micro apps were announced at Summit , but can't seem to find the current status. Usually that means gated private preview, but wanted to confirm. This post on Brickster.ai seems to be current and says as of this week still not released, but things change fast around here (releases, names (are we using Unity Gateway, AI Gateway, what?)) so thought I'd check. I'm actually presenting on apps this week at a user group so would appreciate the info. submitted by /u/ecp5 [link] [comments]

Unity AI Gateway is renamed to Unity Gateway and is now GA

Unity Gateway is Generally Available Move from tokenmaxxing to valuemaxxing with unified AI governance and cost control submitted by /u/sai-nageshwaran [link] [comments]

Could a “Data → Agent” composer be useful for Databricks?

I've been thinking about a gap between Databricks data and agent frameworks. Databricks already has a lot of the building blocks: - Unity Catalog - Genie / Genie Agents - MCP - Vector Search - AI Gateway - Agent skills/tools - MLflow - Omnigent / Kasal And tools like Omnigent and Kasal already solve a lot of the agent orchestration/execution side. But I'm wondering about the step before that: What if a customer already has a large, curated and governed Databricks data estate — how do we turn that data estate into an agent-ready configuration without manually wiring everything together? Something like: Existing Databricks Data Estate ↓ Data-to-Agent Composer ↓ ┌────────┼─────────┐ ↓ ↓ ↓ Domains Semantics Metrics ↓ ↓ ↓ Genie MCP Skills └────────┼─────────┘ ↓ Agent Configuration ↓ Omnigent / Kasal ↓ Agent The idea wouldn't be to build another chatbot or another agent framework. It would be a Databricks-native composition/bootstrapping layer that understands an existing Unity Catalog/data estate and generates the pieces needed for agents to work with that data — domain boundaries, semantic context, approved tools, Genie configuration, MCP exposure, skills, policies, evaluation setup, etc. In other words: Kasal/Omnigent: Agent → Tools/Data Proposed layer: Data Estate → Agent I'm curious if this is already solved somewhere in the Databricks ecosystem, or if people are currently doing this manually when building enterprise data agents. Would love to hear how others are approaching the “existing data estate → production-ready data agent” problem. submitted by /u/imsuryya [link] [comments]

Smart Routing in Unity AI Gateway: 30%+ Cost Savings on Coding Tasks

What’s new in Databricks - August 2026

Databricks shipped many major Generally Available features in August 2026. Here is the breakdown of what just landed: 🚀 Unity AI Gateway Enterprise AI governance layer covering model access, Model Context Protocol (MCP) management, and cost observability. 🔒 Role-Based Access Control (RBAC) Switch to scoped, temporary role assumptions instead of dealing with permission bloat. 🔑 Secrets in Unity Catalog Unified security secrets are now governed, 3-level namespace securable objects. ⚙️ Serverless Compute Access Control Granular admin controls over who can trigger serverless workloads across your organization. ⚡ Lakebase Postgres APIs & LTAP Direct Writes Accelerated synced-table loads and improved transactional data integration. 🤖 Genie Agent Upgrades Official GA releases for both the Agent mode API and Full-page Genie Code view. submitted by /u/Youssef_Mrini [link] [comments]

Evaluating Unity AI Gateway for Enterprise Cost Control

Fixing SSL: CERTIFICATE_VERIFY_FAILED when trying to use ucode

While configuring ucode for UC skills I get the following error when running ucode configure ERROR Databricks Unity AI Gateway is not enabled on this workspace: neither model services (network error: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1082)) nor legacy endpoints (network error: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1082)) are available. I successfully made it past the SSO authentication. I verified this is not an enablement issue. Also, Unity Gateway is enabled by default. When searching, I found 4 suggested solutions. Update Your Root Certificates by running pip install --upgrade certifi . Ran fine, but no change. Bypass SSL Verification. I did not try this as it exposes your connection to man-in-the-middle attacks. Obtain your company’s root/intermediate CA certificate in PEM format and then point to it. Asking IT does not sound like a good solution.

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?

Can Unity AI Gateway Model Service guardrails protect an existing LangGraph agent deployed as a Data

External price control

In the Unity AI gateway, it is also possible to register an external model for which we pay the provider directly (OpenAI, Anthropic, etc.). In that case, Databricks now knows the prices for those models and can calculate, monitor usage, and alert or block based on budgets. more news https://medium.com/databrickscommunity/databricks-news-serverless-genie-code-ltap-lakeflow-61853d8e422a submitted by /u/hubert-dudek [link] [comments]

Skills in Unity Catalog

Skills are available in Unity Catalog. They use a similar concept to volumes and are integrated with the AI gateway. New REST endpoints for skills are coming, and a new tool to manage them, ucode, is already available. more news: https://medium.com/databrickscommunity/databricks-news-serverless-genie-code-ltap-lakeflow-61853d8e422a submitted by /u/hubert-dudek [link] [comments]

Databricks Unity AI Gateway

If I am an AI decision maker for an Enterprise, why should I choose Claude or GPT or any other enterprise subscriptions with seat based pricing where some of my colleagues are power users and some still learning effective AI use? Instead, I could just use Databricks Unity AI Gateway with the options of getting all of these at one place with PayG pricing along with state of the art free Open Source models, budget controls, smart routing, usage dashboard everything at one place. And the cherry on the top is if my enterprise data is also on Databricks!! What are your thoughts? Why would I do that? submitted by /u/myth-buster9999 [link] [comments]

Experience Using Databricks AI Gateway's "Policies" Feature for Adding Safeguards

I am personally happy to see how we are moving away from the AI wild-west and into governed AI with proper safeguards, access, and cost controls. Here I am using Omnigent & Databricks' Unity AI Gateway, which allows you to add several layers of protection against risky/non-compliant AI usage. Note: "Policies" is still in beta, and during my testing, I did hit some cases where the guardrails should have been more rigorous. I passed along the feedback to the Databricks team, and I expect that things will be addressed sooner rather than later. PS: Trying out this different style of animated GIFs to explain different concepts after some feedback from the community here. Love to hear your feedback! submitted by /u/JosueBogran [link] [comments]

Announcement | Unity AI Gateway is Generally Available

🔴 Unity AI Gateway is Generally Available. 🔴

Unity AI Gateway is the Databricks governance solution for AI and is part of Unity Catalog. You can: ⚡ Control which AI services teams can use. ⚡ Route and manage AI traffic across providers. ⚡ Govern MCP servers to control access and costs. ⚡ Monitor usage, cost, access, and lineage from one place. FYI: Some capabilities, including service policies and agent services, remain in Beta. Unity AI Gateway Documentation: https://docs.databricks.com/aws/en/ai-gateway Blog post : https://www.databricks.com/blog/unity-ai-gateway-generally-available submitted by /u/Youssef_Mrini [link] [comments]

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