I ditched Ollama for Jan after realizing what I actually wanted from local AI

Published Sep 9, 2026, 12:30 PM EDT Gaming has been Samarveer’s greatest passion, and the Literature graduate in him takes immense joy in dissecting games for their themes, messages, and impact. Samarveer holds a deep appreciation of gaming, and considers the platform to be the most immersive and impactful across all media. He can be found engaging with gaming communities online, always ready to debate the finer points of ray tracing or itching to write an 8-page collegiate thesis on any game that impacts him emotionally. I've used enough local LLM tools to know that self-hosting one isn't particularly difficult anymore. Ollama made that pretty obvious to me, and even learning how to use llama.cpp through the command line took only a day to learn properly. It certainly gave me a deeper appreciation for what was happening underneath. Between these two tools, I wasn't exactly searching for another way to run models locally. Then, I came across Jan, installed it mostly out of curiosity, and something unexpected happened — I began rethinking Ollama as my go-to software. Now, before the pitchforks come out, I'm not claiming that Jan wiped the floor with Ollama so hard that I no longer want to use the latter, but it did make me reconsider what I actually want from an LLM self-hosting tool. I never thought I'd have problems with Ollama Running an LLM locally has really become easy for everyone Installing and using Jan hasn't really solved a problem I actually had. During roughly a year as my default, Ollama has been nothing but remarkably impressive from day one. It had already reduced local LLMs to just installing an app and typing a command. All I've ever had to do is pick a model, pull it, and run it, with the occasional command in PowerShell that Claude helps me figure out. Quite frankly, the hardest part about self-hosting an LLM in Ollama is just waiting for a few gigabytes to download. However, that simplicity does come with a particular philosophy: Ollama is primarily concerned with getting the model running. For the most part, it does that very well, without giving me a sprawling interface or a dozen configuration screens every time I want to test a new model. The command line gets out of the way when it needs to, and that's exactly what I want when I'm thinking about inference instead of software. Want to stay in the loop with the latest in AI? The XDA AI Insider newsletter drops weekly with deep dives, tool recommendations, and hands-on coverage you won't find anywhere else on the site. Subscribe by modifying your newsletter preferences! The friction, though, started appearing elsewhere. One fine day, I sat down wanting to write code, and wanted the coding assistant in my editor to use a local model instead of a paid API, which just meant pointing it at Ollama's endpoint on port 11434. It should've been simple enough, except the model it expected wasn't the one Ollama had loaded, so I went back in the terminal to check what was actually running, pulled the correct model, and made sure it stayed loaded long enough to matter. Then, the local model choked on a longer file, so the fix was to fall back to a hosted model for that single task, which meant leaving Ollama entirely and doing it elsewhere. It wasn't hard, but it did make me spend an afternoon thinking about ports, loaded models, and which tool was supposed to be talking to which. My "simple" setup became a project with too many friction points. Jan made me rethink what "self-hosting" means The model is only half the equation Jan feels less like a front end bolted onto local inference and more like a place where the different parts of my local AI setup can coexist. Models are now things I can configure, use, and expose beyond the chat window. Just downloading the model was never the whole job. My setup was completed by tasks like pointing other apps at it, swapping between models, and even reaching for a hosted model when the local one fell short. That's what had turned into a bit of a project requiring elbow grease, and that's exactly what Jan fixed. Ollama runs the models brilliantly, but Jan, as it turns out, runs the setup. Dig a little deeper, and Jan's local API server makes that distinction even more important. It provides an OpenAI-compatible endpoint, which ensures that the model running on my PC can be used by other applications as well. That's a considerably more interesting proposition than simply having another chatbot installed. Ollama runs models brilliantly, but Jan runs the entire setup. Furthermore, Jan ships with Hugging Face model downloads built in, so users can discover, download, and start using compatible models without leaving the app or having to manually hunt down model files. Plus, if you want the convenience of Jan without being limited to the models running locally on your own hardware, it also lets you call Claude or OpenAI through API keys. I could keep the same interface whether the model was sitting on my PC or running somewhere in the cloud. Since I always find myself testing new models twice or thrice a week instead of pledging allegiance to one particular setting, this flexibility from Jan matters more than ever. Now, it's not like Ollama doesn't expose an OpenAI-compatible endpoint too. However, it's a server you configure instead of a place you work. Jan, on the other hand, puts that same endpoint behind a GUI, next to the cloud providers, so the local model and a hosted Claude call sit in one interface. Pretty soon, I uninstalled Ollama Jan became the tool I actually wanted to use Just over two weeks of use, I found myself opening Jan more often than Ollama. I just realized that I had begun preferring the way Jan brought everything together, particularly when I wanted to experiment instead of just launch a model and get on with my day. It's not like Jan runs models dramatically faster than Ollama, but Jan gives me far more control over how I interact with my models. Full disclosure, though: Ollama still makes more sense for plenty of people. If you want nothing more than a lightweight local inference layer, automation, or simply having models available for other applications, there are good reasons to keep it around. I just wasn't using it that way often enough to justify keeping it as my primary tool. Jan The little things really add up I didn't uninstall Ollama because I'd suddenly discovered that everything I'd been doing with it was wrong. Quite the opposite, in fact: using it is what made me appreciate why Jan clicked with me. After spending enough time with local AI, I've stopped being impressed with just a model running on my own hardware. Now, it's more about everything that happens around it. For me, that made Jan the easier choice to keep around. It gives me fewer reasons to think about the machinery underneath them. That's a surprisingly valuable quality in software.