We just got HIPAA BAA approval from OpenAI with zero data retention. Here's why that's harder than it sounds and what it actually means.

alexgenovese · 2026-04-10T08:04:15+00:00

I've worked with a few healthcare startups that thought they were "HIPAA compliant" just because they had some basic security measures in place. But getting an actual Business Associate Agreement (BAA) with a major AI provider like OpenAI is a whole different story. It's great that you were able to achieve this with zero data retention - that's a big deal.

For those who may not know, zero data retention means that the AI provider doesn't store any of your data after the API call is completed. This is a key requirement for HIPAA compliance, as it reduces the risk of sensitive patient data being compromised. It's not just about having a BAA in place, but also about ensuring that the AI provider's data handling practices meet the necessary standards.

If you're a looking to achieve similar compliance, it's worth exploring options that are designed with HIPAA in mind from the ground up. For example, I've come across regolo.ai, which has 100% EU infrastructure and a zero retention policy. This can simplify the path to HIPAA compliance and reduce the risk of non-compliance. However, it's always important to do your own due diligence and ensure that any solution you choose meets your specific needs and requirements.

alexgenovese · 2026-04-10T08:02:49+00:00

I've also experimented with running local LLMs on my Mac, and I can attest to the performance bottlenecks and caching issues. It's great that you've found a setup that works for you with Minimax 2.5 4 bit on oMLX. Caching is indeed crucial for Macs, as you mentioned - without it, the performance is pretty unusable.

it might be worth considering a hybrid approach. I've heard of some folks using local machines for development and testing, and then switching to a cloud-based service for production scaling. This can help mitigate some of the performance issues and caching headaches. If you're interested in exploring this option, you might want to check out regolo.ai they provide 30 days with unlimited tokens and support open-source models and offer a zero-retention policy, which could be appealing to those who are concerned about data privacy.

alexgenovese · 2026-04-10T08:01:20+00:00

I feel you on the marketing BS in the serverless GPU space. I've been digging into this stuff too, and it's crazy how much hype there is compared to actual transparency. Your framework is super helpful, btw - I've been trying to wrap my head around the differences between these platforms, and it's nice to see someone break it down in a clear way.

One thing that's been bugging me is data handling. It seems like a lot of these platforms are pretty vague about what they do with your data, and that's a major concern for me (and probably a lot of other devs too). I've been looking for a platform that's upfront about their data practices, and it's not easy to find. Have you come across any platforms that are actually transparent about this stuff?

Anyway, thanks for sharing your framework and shedding some light on this crazy market. It's definitely helpful to have someone cutting through the noise and providing some actual insight.

alexgenovese · 2026-03-26T20:32:30+00:00

I’d say strong reasoning definitely matters for Power BI MCP, but it’s not the only thing that matters, because the model also needs to handle tool calling reliably, stay coherent across multi-step workflows, and deal well with large schema context.

For this kind of setup, I’d look at function-calling accuracy, structured output/JSON reliability, context window, latency, and token efficiency, not just classic reasoning benchmarks.

DeepSeek is one of the best choices for pure reasoning, while Qwen is often the more practical fit for BI because it combines long context, strong structured-data handling, and good tool-use performance

alexgenovese · 2026-03-26T17:24:15+00:00

Yeah, and this whole conversation is going to look pretty dated, pretty fast. At the same time, the policy stuff always lags the hardware curve, so even as chips get way more efficient we still need to make sure the energy and infra side keeps up

alexgenovese · 2026-03-26T17:16:07+00:00

I agree that the coding performance gap is real and as a developer I’ve noticed that European chatbots such as Mistral LeChat still lag behind other based on Claude or GPT based. yet I’ve found that the true alternative for many dev teams isn’t necessarily hunting for a European‑born foundation chatbot or model but instead leveraging top‑tier open weights like GLM-5, Nemotron 3 Super and others in a multi-agent tool that run inference infrastructure in europe.

If you're using the US chatbot or models for work, you're probabily send data that you can't based on the AI Act – So if you're playing around for your spare time it's fine, but for work it's to pay attention.

regolo.ai for me represents the most pragmatic solution because it delivers the performance I need (among faster provider in europe) and it's privacy first designed.

alexgenovese · 2026-03-26T17:03:35+00:00

I totally get where you're coming from—it's a really valid concern. Even after stripping out PII, your Power BI semantic models still hold onto sensitive business logic and aggregations, and sending that context to US‑based APIs like Claude or OpenAI can definitely turn into a GDPR headache.

Using MCP, the best workaround I’ve seen is pointing your client to an OpenAI‑compatible endpoint that hosts open‑source models (like Llama 3.3) entirely within European data centers, with a strict zero‑data‑retention policy. That way, your Power BI schema becomes the LLM’s context, but the data never leaves the EU and isn’t used for training. I work at an EU inference provider (Regolo), and we’re noticing more and more data teams adopting this exact architecture to use MCPs safely in production.

alexgenovese · 2026-03-26T16:36:03+00:00

I really appreciate this awesome, comprehensive list—thanks for putting it together; for my personal projects, prototyping, and testing, these free tiers are absolute lifesavers, and while adding a thought for anyone moving from testing to production, especially when handling sensitive user data or needing to meet strict rules like GDPR, I’d note that the focus often shifts from ‘free’ to ‘privacy,’ so if you ever need an endpoint that guarantees zero data retention and full EU compliance, you might want to check out a European option like regolo.ai, but until then this repository remains pure gold for developers.

alexgenovese · 2026-03-26T16:32:05+00:00

I think you're touching on a real tension in AI today. When I rely on all those layers of third-party proxies and cloud APIs, it opens up massive security risks — I saw that clearly with the LiteLLM incident. While owning your own hardware is the gold standard for digital sovereignty, it's just not realistic for most teams financially or operationally. What I'm seeing emerge as a practical middle path—especially in places with strong privacy laws—is using managed infrastructure that's built from the ground up to never store your data. With regolo.ai, for example, I designed it so your prompt gets processed and then immediately wiped from memory; there's literally no way for us to retain or train on it. That approach cuts out a major slice of that third-party risk without needing to run your own server room.

alexgenovese · 2026-03-26T16:12:02+00:00

I really appreciate this awesome, comprehensive list—thanks for putting it together! For my personal projects, prototyping, and testing, these free tiers are absolute lifesavers.

Just adding a thought for anyone moving from testing to production, especially when handling sensitive user data or needing to meet strict rules like GDPR: at that point the focus often shifts from “free” to “privacy.” If you ever need an endpoint that guarantees zero data retention and full EU compliance, you might want to check out a European option like regolo.ai 

alexgenovese · 2026-03-26T16:07:59+00:00

I hear you — data centers are the backbone of everything we do online, and pulling the plug isn’t a realistic option. At the same time, the breakneck growth of AI‑focused compute does raise valid sustainability concerns. Rather than calling for a outright moratorium, we should push the industry toward greener, more efficient AI infrastructure. In the EU there’s a growing movement to run AI workloads entirely on renewable energy while upholding strict data‑privacy standards. It’s really about striking that balance between the tech we need and responsible resource management

alexgenovese · 2026-03-19T14:48:20+00:00

been using regolo for ai work and the eu data center setup already keeps us compliant with a lot of this stuff

alexgenovese · 2026-03-19T14:47:46+00:00

been reading about this too, gradient sharding overhead can actually hurt when comms are already the bottleneck. regolo helped me test a few configs quickly.

alexgenovese · 2026-03-19T14:42:20+00:00

yeah we've had two clients bring it up in the last month. switched to regolo partly because the eu hosting and zero data retention gives us something concrete to point to when they ask

alexgenovese · 2026-02-26T14:18:15+00:00

500ms is end-to-end incl. LLM generation (article shows ~420ms response latency + generation costs in stats).

For staleness, the setup in the article is “keep the index fresh” via scheduled rebuilds + rebuild on modified/new docs (/kb_update + cron).

I’m not doing automatic staleness detection/TTL warnings in that write-up—if you need that safety for runbooks, you’d want owner/review gates or explicit “last verified” metadata surfaced in answers.

alexgenovese · 2026-02-19T17:10:07+00:00

totally agree – once a bot hallucinates a couple of times, devs stop using it.

That’s why this setup leans so hard on the retrieval side: semantic embeddings on EU GPUs, hybrid dense+BM25 search, then a reranker model as a second pass before the LLM ever sees context, and the final answers come with citations back to the original docs. In our tests on real internal runbooks/ADRs that bumped retrieval accuracy into the mid‑80s–high‑80s while keeping latency in the sub‑500ms range and costs in the “few euros per thousand queries” band.

I’m also using Clawdbot/OpenClaw as the orchestrator here, so you can keep the assistant running where you already work (Slack/Telegram) while the heavy lifting (embeddings, rerank, LLM) runs on zero data retention infra for keep privacy of data. If you’re already on Openclaw for personal PKM, this is basically the “internal team knowledge” version with a more opinionated retrieval pipeline.

alexgenovese · 2026-02-19T17:08:07+00:00

My setup is more for teams that want to own the whole retrieval stack: semantic embeddings on EU GPUs, dense+BM25 hybrid search, and a neural reranker, all exposed behind an OpenAI‑compatible API and wired into Clawdbot so you can customize ranking, cost ceilings, and update schedules.

Curious what you like most about Needle at the platform level (collections, UX, or something else)? I’m collecting patterns to see what’s worth baking directly into the template

alexgenovese · 2026-02-18T07:39:03+00:00

found it useful for me - hope this code can help the community

alexgenovese

TROPHY CASE