Anthropic

I am trying to build an interactive dashboard on the underlying Databricks. Which of these are the best?

I’m thinking of 4 options here 1. Build an MCP (a custom MCP) that can access custom tools on Databricks and interface it on Claude.ai or Claude desktop Advantage- Claude is very good at inferencing, multi-turn conversation and multi-step processing Disadvantage - custom MCP and tools needs to be built accurately and validated. It should have full context of schema and unity catalog Build a semi-custom MCP - this will use “askGenie “ as one of its tools with additional custom tools Advantage- complexity decreases as we leverage genie space Disadvantage- double inference by genie and Claude Use custom Databricks connector in Claude. Not sure if this uses genie and therefore double inference but it’s more reliable than custom build because this is a native offering by vendor Use only genie and build custom dashboard without needing Claude interface What’s the thought on this? submitted by /u/Bala_Devaraj [link] [comments]

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]

Let's talk about AI rate limits

Databricks Unity Gateway is great and getting even better very quickly. It now has nearly everything we could hope for. New frontier models are usually available within 24 hours of release. But...the models aren't really available . They all come with extremely low rate limits, way too low to support a team of engineers in any sizable enterprise using the models. If you have a sizable agents.md even 1 person hits the limits. Why is it like this? Sure we can raise a ticket to request higher limits but that takes weeks and the highest we've gotten is 1M tok/min. We can (and do) go to Bedrock and get 5M+ tok/min by default, no special request needed. Is the expectation that we hook up other providers? Are the key frontier labs (OpenAI, Anthropic, Google) limiting Databricks as a whole? For such a great product this seems like a glaring limitation that's holding us back. Make it make sense. submitted by /u/degenbets [link] [comments]

Claude Academy's Using Databricks for Data Analysis - This official documentation from Anthropic on Claude Academy for how to work with Databricks is really underwhelming, so I wanted to share a guide that properly explains everything.

Hey Databricks community! I have been working with Databricks as an admin for over 4 years and I was attempting to follow this official tutorial from Anthropic for how to connect Claude with Databricks https://academy.claude.com/tutorials/using-databricks-for-data-analysis And I encountered multiple significant frustrations with doing it, as the tutorial is very underexplained and outdated for how to get Claude working with Genie One and other functions within Databricks. Therefore, I thought this video on Youtube would be valuable content to share to help solve this problem, for other people that are encountering similar difficulties when getting these two tools connected and building out systems that work for Claude to properly interact with Databricks. Let me know if this video is helpful! submitted by /u/k_kool_ruler [link] [comments]

Databricks Cost Optimisation 3rd Party Tools

Does anyone have experience with third party optimisation tools like espresso.ai or Zipher? Espresso has an enticing pricing model but I wonder if dumping a whole heap of logs and system stats into Claude would get me 80% of the way there. submitted by /u/Agitated-Western1788 [link] [comments]

Genie One MCP is now GA

The Genie One MCP server is now GA. It exposes Genie One over MCP, allowing any agent to communicate with Genie One as a peer agent. The MCP exposes tools for asking questions to Genie One, getting query results, checking on incremental progress, and steering responses. These capabilities allow you to integrate Genie One into whatever agent you want without changing your workflow. And you get all of the semantics built-in instead of having to use raw SQL APIs with fragmented skills/stale markdown repos/additional semantic layers like we used to with ai devtools. Here are the docs: https://docs.databricks.com/aws/en/agents/mcp/genie-mcp I've seen some pretty cool use cases with integration into ChatGPT/Claude, but also headless agent workflows where you want to delegate the data questions to Genie One so it can use agents/ontology. Interested in folks' thoughts on the right/wrong use cases for this feature. submitted by /u/lakehouse_vacation [link] [comments]

Show HN: Like LeetCode, but multi-file and multi-step

On LeetCode you write one function, it passes, and you're done. Most AI labs such as Anthropic and OpenAI, as well as some big tech companies such as Meta, Snowflake and Databricks, don't stop in one function and instead ask multi-step questions that are closer to real-world problems. Say you're building a key-value store. Step 1 is get, set, delete, count. Once your tests pass, Step 2 unlocks: expiring keys. Then nested transactions. Then atomic batches. I built this because I couldn't find a good place to practice for multi-step, multi-file questions. The questions are based on ones people have reported from those loops. Check it out at https://loopprep.dev. There are 24 questions, 3 free, and no signup to try them. Happy to answer questions. --- top comments --- [MiroslavPokorny] Writing code in a browser like this or leet code is wrong on so many levels its not funny.

Where Omnigent Fits Alongside ChatGPT Work, Claude Cowork and Cursor Projects

datatf: Automate importing Databricks workspace into Terraform

DataTf , bring an existing Databricks Workspace into Terraform (IaC). generates dynamic terraform.tfvars + import code built on the Databricks Go SDK compatible with Terraform or OpenTofu compatible with Databricks Omnigent, Claude Code, opencode, OpenAI Codex and others native support for Azure, with GCP and AWS support upcoming Disclosure: I am the Author/Founder, 536 Technologies. submitted by /u/536tech [link] [comments]

Omnigent Local Coding Model Rec

After watching Matei's webinar and the post on controlling spend , been trying to use the other harnesses and models folks are suggest and trying out Qwen 2.5 coding and 3.6 with Polly in local Omnigent (not connected to a workspace). I have Codex and Claude but ideally thinking best to use paid higher model to plan and then have the local Ollama based Qwen model on my Mac build but so far I haven't seen Polly use it much. What are folks experience, is there a good local model I should use, should I be giving Polly and the sub-agents more direction? (This is on a MacBook M5 btw) submitted by /u/ecp5 [link] [comments]

Databricks SSH Tunnel for connecting your coding agents and IDEs to your workspace

Just made a video about a feature I'm pretty excited about. tldr: You can use an SSH tunnel to connect your coding agents and IDE (VSCode/Cursor) to your Databricks workspace. See the video for a full walkthrough. Some notes on things I forgot to mention in the video: - claude/codex isn't natively installed when you connect to your workspace, so you'll have to install if you want use them (Ex: curl -fsSL https://claude.ai/install.sh | bash ). We're working on better native support in the future, but I wanted to make sure you know this is an option in the meantime. - For the base environment YAML file. You'll have to set a base environment of '4' for it to work when using the SSH tunnel. Our example yaml ( https://docs.databricks.com/aws/en/admin/workspace-settings/base-environment#example-environment-specification ) shows '5' so don't let this trip you up! As always, please feel free to leave questions and feedback in the comments! Docs: https://docs.databricks.com/aws/en/dev-tools/ssh-tunnel Previous post with more info: https://www.reddit.com/r/databricks/s/kCFBEfPTC6 YT link: https://www.youtube.com/watch?v=rHoGWVpb6kg submitted by /u/tony-dang [link] [comments]

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]

Any chance we get Genie One a python sandbox env?

Trying to get a python enviornment on genie one to run for the end-user so they can use the xlxs claude skill. I have found a way to get the skill added into genie one with the scripts, however, as I suspected it does not have a python sandox yet. submitted by /u/BoiBoii619 [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]

Multiple Databricks Genie MCP connectors in one Claude workspace collapse into a single shared connector, anyone solved this?

We're connecting Claude (Cowork/Claude Desktop) to several separate Databricks workspaces, each with its own Genie space. Running into a platform-level snag and hoping someone's hit this before. Setup: each workspace exposes the standard Genie MCP endpoint ( https:// /api/2.0/mcp/genie ). We add each as its own connector in Claude so we can ask natural-language questions against the right workspace's data. Problem 1, generic connectors are indistinguishable: every Genie MCP server reports the same tool names and the same generic description, regardless of which workspace it's pointed at. Claude has no way to tell them apart from metadata alone, so it either has to probe each one (asking a neutral "what workspace is this?" question and checking the returned deep_link ) before it can route a question correctly, or it guesses wrong. Problem 2, trying to fix it with named connectors backfires: we tried building a Claude plugin that declares each of the seven as a distinct, fixed-name MCP server (e.g. genie-workspace-a , genie-workspace-b , ...), hoping deterministic names would remove the need for probing entirely. Turns out Claude's client recognizes Databricks Genie as a "verified" connector type in its directory, and collapses all seven fixed-name declarations into a single shared authorization instead of keeping them independent. Authorizing one silently becomes "the" Genie connection, and the others just reflect that same state rather than getting their own. Problem 3, working around the collapse trades one problem for another: we found that changing the declared URL slightly (adding a harmless query param) makes the client stop recognizing it as the verified type, which does force it down the plain custom-connector path and keeps it independent. But then it needs an actual registered OAuth client ID against that Databricks workspace, since it no longer gets whatever implicit OAuth handling the verified integration has. That means registering an app per workspace in Databricks/Azure just to get back to where the plain manual setup already was, more infrastructure, not less. So right now we're back to manually-added generic connectors plus a routing skill that probes and caches per conversation, which works, but costs a lookup the first time each workspace is used per session, and never scales cleanly as we add more workspaces. Has anyone gotten multiple Genie MCP connections into the same Claude account/org without them colliding into one shared authorization? Is there a way to deploy or configure the Genie MCP endpoint itself so each workspace reports distinguishable tool names or descriptions, rather than relying on the client to differentiate them? Btw, in Codex/chatgpt this is not an issue, because we can edit the description/name, and it automatically discovers the right plugin to use. It seems this is a very claude thing issue submitted by /u/Akroma188 [link] [comments]

Databricks Cost Optimizer: Audit Spend with Codex or Claude Code

Managing AI Coding Costs at Scale

--- top comments --- [extr] I would be really curious to hear from devs at Databricks what the experience of development is like internally. I work at a small startup with essentially unlimited AI spend budget - the entire point is that I should be turning to it at every opportunity since our human labor is so expensive relative to tokens. So generally it's like: - Spend most time prioritizing/discussing what to do. - Once that's agreed, use Fable 5 High + 5.6 Sol XHigh come up with a design + plan. Agree on the high level plan. (Usually this just comes down to choosing where the change belongs on the spectrum between minimal patch <-> full redesign) - Use Opus 5 or Sol Med to execute - Auto-fix bugs and CI until green + thermonuclear review skill x3. - Manual interrogation of change/nits - Come up with QA plan and have Codex Computer Use execute on it - Manually spot check the final result (usually a sizable diff, thousands of lines, complete feature E2E, etc) I probably spend like $80 a day at least but I produce the output of 3 or 4 2022 engineers and probably at better quality. So it's easily worth it. Would I save money by switching to GLM 5.2 and such...perhaps? IDK. At our scale it's not worth the time spent building the eval harness to actually understand the performance tradeoff. [lbriner] There are a surprising number of articles like this along the lines of, "we started using AI tools and ended up spending millions per year". On what planet do people start paying for things without keeping an eye on the costs and no-one notices until you have spent a crazy amount? I don't understand. You are either paying a fixed amount which you are happy about in-advance or you are PAYG in which case you would ballpark how much it costs. Otherwise it reads a bit like a fake problem, because it didn't really happen, you just foresaw it (as you should) and added a few guide rails. [sashank_1509] I suspect that when it comes to hard complex software products, you’re better off ignoring agents and doing “trad coding”. What you lose in short term speed you gain in manageable complex codebases. If you have a 500k line codebase and even > 50% is written by agents, you are in a world of pain that won’t justify the costs longer term. Now of course, there are products that just involve lots of code but are not actually complex. This is generally the project with like hundreds or thousands of features but most of the features are separate and don’t actually interact in complex ways. Think a task management app with hundreds of features like calendar, email integration etc. there I think agents gives you more bang for the buck. Just my thought, using agents at work. [platinumrad] Careful. If you admit to using models that weren't trained by OpenAI or Anthropic then you might hauled in front of Congress: https://www.scmp.com/news/china/diplomacy/article/3362616/us... [dgellow] What I take from this is that models are already commoditized, and it’s pretty clear nobody has a moat: routing for the models, they can be swapped whenever new models are released, AI labs will have to continue to run on the treadmill non stop or be replaced. Long term I cannot imagine that business will be high margin. Routing for the harness, so anything that differentiate a provider vs another isn’t exposed to the user and isn’t too relevant. One more datapoint for the thesis that OpenAI and anthropic aren’t viable, sustainable businesses, and cannot justify their $1T valuation and the level of compute commitment (reminder that OpenAI committed to >$750B in infra spending for 2030)

Pi, Minimal and Performant

--- top comments --- [zhinit] Im looking forward to trying out Pi this week. It seems like anthropic just keeps adding more features that I don't really want and now is ignoring my claude.md and hooks. It feels like i'm adding more and more guardrails just to get to the place i used to be at before all the new features. Hopfully Pi helps

Benchmarking coding agents on Databricks' multi-million line codebase

--- top comments --- [redmalang] We have an internal proxy (that I've been meaning to open source for ages) that routes all llm usage at our company, which allows us to see data in realtime. Its been fascinating how rapidly Pi has been adopted. Moreover since its pretty hackable, we've been able to automatically aggregate context from pi sessions, which has resulted in Pi efficacy being higher as more people use it, putting in place a interesting virtuous loop. I didn't expect this outcome: for whatever reason I assumed proprietary harnesses fine tuned to work with a companies' models would work better? ps/random aside: there is something slightly off about Pi's edit command, we are planning to investigate this further and patch this as we have quite a few session traces now.. [yodon] I wish they'd do a follow-on post drilling into the impact of the programming language on cost-per-task, specifically looking at cost to complete tasks in mainstream strongly typed languages (eg. C#, TypeScript) vs dynamic languages (eg. Python, JavaScript). Does the additional verbosity of the language help or hurt cost per task? [cpard] This was mostly because Sonnet 5 worked longer and read more to get there, consuming 1.9x more tokens. I have experienced similar behavior between opus and haiku when benchmarking Dara engineering tasks. The “cheaper” model takes many more turns to figure out the task and this is without taking into account other important factors. Another interesting behavior that I observed is that Haiku tended to cheat more maybe because it was having a harder time to find the root cause of the problem. Benchmarking and evaluation of agentic systems is very interesting and if there’s one thing that someone should keep from the Databricks post is how important is for everyone to build and run their own. [anentropic] > the results showed clear clustering of the models and harnesses into 3 capability tiers pretty sure the only thing making that 'clear' is the coloured stripes, if you took that away it'd look like two tiers good result for GLM 5.2 though and Sonnet 5 seems like a waste of time [HarHarVeryFunny] Wow! It's great to see a large-scale real-world benchmark from a user of these tools, as opposed to the the benchmaxxed results from the vendors themselves. Also great to see different harnesses being tested, with considerably different results. Definitely a few surprises here: 1) GLM 5.2 using Pi performs identically in terms of pass rate (~87.5%) to Opus 4.8 high using Claude Code, but significantly cheaper ($1.25 per task vs $2) 2) Absolute best pass rate (90%) was from Opus 4.8 x-high using Pi, beating out Opus 4.8 using Claude Code 3) Pareto frontier performance from any of the models (Opus 4.8, GPT 5.5, GLM 2.5) was using Pi rather than native harnesses Apparently Pi used 3x less context than Claude Code, and one takeaway is to use Pi regardless of what model you are using. The other takeaway is that in real-world performance GLM 5.2 is the equal of Opus 4.8 unless you run Opus 4.8 on x-high in which case you can eke out a 2.5% increase in pass rate at the expense of doubling your cost over GLM 5.2

Show HN: Dex – Cost-aware analytics engineering skills for agents

Hi I’m Marco, co-founder of Exmergo. Me and my team created Dex to help Analytics Engineers do real work with Claude Code (and any other coding agent). We’ve found that Data and Analytics teams are stuck between a rock and a hard place cost-wise: - On one side, they are using some of the most expensive consumption-billed software on the planet (Snowflake, Databricks etc.). - On the other side, Anthropic and Open AI want you to tokenmax (and with data analytics it’s very easy to burn your context window). So we created Dex, our open source skills plugin (Apache-2.0), to solve both of these problems: - Dex forces the agent to use a cost guard when performing exploration queries and transformations. - Dex uses a set of tooling that makes it hard to get burned when transforming data. It achieves these things with tight control scripts that the SKILL.md files are pointed towards when using /dex:explore, /dex:transform and /dex:maintain. Bonus: Dex makes agents really good data exploration, building sql and dbt models and detecting drift. 76% performance on ade-bench with Claude Sonnet 5 (and, per our measures, 2.5x cheaper than Fable 5). Install on any agent with this command in your terminal: npx skills add exmergo/dex Install on Claude Code running these commands (separately): /plugin marketplace add exmergo/exmergo-agent-plugins /plugin install dex@exmergo If you want to see more visual examples you can go through the README or browse here: https://www.exmergo.com/dex Let me know if this helps your analytics workflow and makes your agents more cost-aware.

Launch HN: Parsewise (YC P25) – Reason Across Documents with an API

Hi all, it’s Greg and Max, founders of Parsewise here (https://www.parsewise.ai/api). Parsewise transforms a bucket of unstructured data into schema compliant data, retaining lineage for values resolved across documents. Imagine giving Claude a bunch of files and asking for a CSV or JSON output. If you have tried this, you know both the system limitations (number of files, type of inputs, cost, latency) but also the human-facing challenge of having no way to validate the results quickly. We solve both. We help tech teams simplify their unstructured data ETL, and loop in business experts for the definitions and for instant validation. Here is a video with a few use cases: https://www.youtube.com/watch?v=dbRllnnh47w Parsewise in the words of someone coming to us: ”I need to extract information from insurance policy PDFs, phone calls that have been transcribed, emails, etc. I am NOT looking for something that would just extract data point by data point, page by page into a structured well-defined schema but more something more agentic that can understand that information might be across documents and that it should reason over what to extract.” We started the company based on a decade of experience (and pain) in complex data transformation and data analysis / synthesis. Greg was building both classical ETL and implemented AI workflows at Palantir. At Bain, Max did highly complex data analysis in the financial sector, similar to many of our customers. Parsewise works by taking in a bucket of data (think hundreds or thousands of pdfs, excels etc.), and outputting schema compliant data where every single value is traceable down to word level citations across multiple documents in the bucket. We provide API customers with ways to show the lineage in their own applications, or they can use our platform for internal operations. At the core of the data processing we have self-improving agent definitions. They define the acceptable sources, the logic for resolving or combining values, and the rule for highlighting uncertainty to the end user. The underlying tech is model and cloud agnostic and can be deployed in private networks. We have seen the best results with Gemini models for visual reasoning, achieving SOTA (beating Claude Fable) on the strongest grounded reasoning benchmark we have found (Databricks OfficeQA). Notably, we focused more on the “human harness” rather than the model harness, leaning into the actual friction we saw in uptake, which is around verifiability. That means optimizing the time and clicks required to trust the outcomes. We use vLLMs for parsing, and then we use small models for efficient large scale exhaustive search. Unlike RAG, we do not sample; instead, we exhaustively find all relevant values for a given query. We use larger models for decision making around resolutions and flagging inconsistencies to users. This exhaustiveness and explicit value sourcing is unique to our platform, and it goes beyond the first step of data parsing that many existing providers cover. We would love to welcome builders and tinkerers to try Parsewise on your complex document challenges. We have a ton of ideas on how we can expand the product and make it better, but would appreciate feedback and ideas from the community! --- top comments --- [whinvik] Document parsing is top of my mind lately because in some of the areas we work on the bottleneck is starting to become being able to query documents the same way one queries an api. I keep thinking the most obvious analogue is we need some way to represent documents the same way we can represent structured data in parquet. Parquet allows easy range bases queries and there is so much tooling built around Arrow. But for documents I keep hitting a wall to figure out what the right abstractions are. Parquet allows filterable metadata. But what such metadata is there for documents. Then there is the arbitrrariness of chunking, vectorization. If we could just do this in a […truncated]

Announcement | Claude Fable 5 on Databricks, Governed through Unity AI Gateway

Omnigent: A Meta-Harness to Combine, Control and Share Your Agents

--- top comments --- [saj1th] Been exploring loop engineering. Addy Osmani has a good post on it: https://addyosmani.com/blog/loop-engineering/ The hard part of loop engineering IMO is the machinery around it, and omnigent sits above pi, claude, codex, etc. and wraps each in a uniform api. The things it adds are exactly that machinery. - Parallel git worktrees so concurrent agents don't step on each other. - Approval and cost policies enforced at the harness layer rather than living in a prompt the agent can talk its way around. - A maker/checker split where the reviewer can run on a different vendor than the writer, which is a more honest check than a second pass from the same model with the same blind spots. [dpbrinkm] I can see this being useful if only for the fact I can search all my conversations with my 50 different agents on different providers easier. I spend so much time looking through my cc/codex session or the desktop app or Hermes agent or antigravity session (back when I used that). Hopefully having a layer above will abstract that away. If I understand it correctly the meta harness will help me scan across all sessions and not have to drop into individual ones. [iamfreee] very similar to https://github.com/kdlbs/kandev/, which also supports multiple different agent harnesses working on the same task

Claude Mythos & Databricks LakeWatch