Running Gemma on Ollama changed how I think about AI tools. Here's the framework I use to decide when to go local and when to stay in the cloud. There's a moment every developer hits: you're mid-project, you've been routing everything through ChatGPT or Claude, and you start wondering - do I actually need to send this to an external API? What if I just ran something locally? I had that moment while working on a security automation pipeline on Parrot OS. Some of the data I was processing wasn't something I wanted to leave my machine. So I spun up Gemma via Ollama, and it handled the task cleanly, no API key, no network latency, no data leaving my environment. That experience pushed me to think more deliberately about when local models make sense and when cloud AI is the right call. This guide is the framework I landed on. First: What We Mean by "Local" and "Cloud" AI Local AI means running a model directly on your machine CPU, GPU, or both. Tools like Ollama make this surprisingly accessible.…