When we started Ollama, we were told how open-source (open-weight wasn't a term back then) will always be inferior to the close-sourced models. This was 2 years ago (Ollama's birthday is July 18th, 2023).

Fast forward to now, open models are quickly catching up, and at a significantly lower price point for most and can be customized for specific tasks instead of being general purpose. For general purpose models, absolutely the closed models are currently dominating.

A person can use Google’s Gemma models on ollama’s cloud and possibly pay less. And have more quality control that way (and other types of control I guess) since there is no don’t need to wonder if a recent model update or load balance throttling impacted results. Your use case doesn’t generalize.

If your workflow is general enough, you can (and should) switch between models. They all have different styles and blind spots.

Like I had Codex + gpt-5-codex (20€ tier) build me a network connectivity monitor for my very specific use case.

It worked, but had some really weird choices. Gave it to Claude Code (20€ tier again) and it immediately found a few issues and simplifications.

> software companies that offer a nerfed "community edition" whose only purpose is to coerce people into buying the commercial version.

This is the play. Its only a matter of time till they do it. Investors will want their returns

Ollama , the local inference platform, stays completely local. Maintained by a non-profit org with dev time contributed to by a for-profit company. That company can be VC backed and can make their cloud inference platform. And can use ollama as its backed, as a platform to market etc. But keep it as a separate product (not named ollama).

This is almost exactly how duckdb/motherduck functions and I think theyre doing an excellent job.

EDIT: grammar and readability

If I were them I'd go whole-hog on local models and:

* Work with somebody like System76 or Framework to create great hardware systems come with their ecosystem preinstalled.

* Build out a PaaS, perhaps in partnership with an existing provider, that makes it easy for anybody to do what Ollama search does. I'm more than half certain I could convince our cash strapped organization to ditch elastic search for that.

* Partner with Home Assistant, get into home automation and wipe the floor with Echo and its ilk (yeah basically resurrect Mycroft but add whole-house automation to it).

Each of those are half-baked, but it also took me 7 minutes to come up with them, and they seem more in line with what Ollama tries to represent than a pure cloud play using low-power models.

Have ollama server support auth / API keys (closed as out of scope) and monetize the way everyone else does around SSO.

Thats what I thought so as well - that it was for people like me who arent professional SWEs and thus im sad to see them go this way. But what ive found is people are using it for "on-prem" style deployment, have no idea if this is common but I wouldnt be surprised given the reality of AI startups + the abundance of ollama in training dataset leading to relatively greater vibe coding success rate