I spent the last thirty days running Ollama locally for automation prototyping. Power Automate flow drafts, Copilot Studio prompt iteration, extraction tests on documents I did not want to push to a hosted endpoint during early experimentation. This is the honest review.
The short version: local AI earned a permanent spot in my prototyping loop. It did not earn a spot in production. The gap between those two things is bigger than most tutorials admit, and the hardware reality check is the part nobody writes about until they have lived through it.
What I used it for and the setup
Workstation is a 32GB RAM machine with a decent GPU. Nothing exotic. I ran Ollama with a rotation of models: Llama 3.1 8B for fast iteration, Qwen 2.5 14B for anything that needed actual reasoning, and a quantized Mistral variant for extraction work. Pulled, swapped, benchmarked over four weeks.
The work itself was three buckets. Drafting Copilot Studio topic prompts and testing how they handled vague phrasing before I touched the real environment. Prototyping extraction logic for unstructured text where I wanted to see the shape of the output before deciding on a hosted model. And quick scratch work: rewriting a Power Fx expression in plain English, sketching a flow outline, asking dumb questions I did not want logged anywhere.
What it does well
Iteration speed. This is the win. When I am tuning a system prompt for a Copilot Studio topic, I want to run it twenty times with small variations and see what breaks. Doing that against a hosted endpoint means watching token costs, hitting rate limits, and waiting on network round trips. Locally, I iterate as fast as I can type. That alone saved me probably six hours over the month.
Working with sensitive drafts. There are documents I would not paste into a cloud chat during a five-minute exploration. Internal text, draft policies, anything that has not been cleared for an external endpoint. Having a local model means I can think out loud against real text instead of synthetic placeholders. The placeholders always lie to you about how the real prompt will behave.
Offline. I traveled twice this month. Trains, hotel wifi that pretends to exist. Ollama did not care. My prototyping loop kept moving.
Learning the failure modes up close. Running models locally forced me to actually see how an 8B model reasons versus a 14B, where quantization hurts, where context windows bite. That intuition transfers directly to how I think about agentic workflows, because the reasoning layer is the reasoning layer regardless of where it runs.
Where it falls short
Quality drop on anything that needed real reasoning. I tried wiring a local model into a Power Automate flow through a custom connector for an extraction task that hosted models handle cleanly. The 8B model produced confidently wrong JSON about thirty percent of the time. The 14B was better but slower than I could tolerate inside a flow that triggered on document upload.
Hardware reality. Most tutorials show a 7B model running snappy and call it a day. Try running a 14B model with a 16K context window while you also have Teams, a browser with forty tabs, and Power Apps Studio open. My machine started swapping. Cooling fans did things I did not know they could do. If you do not have a dedicated GPU with serious VRAM, you are running small models or you are waiting.
Tool calling is rough. Hosted models have spent a year getting better at structured tool use. Local models I tested were inconsistent at best. For an automation developer, this matters. The whole point of an LLM in a flow is reliable structured output. When the local model returns malformed JSON for the fourth time in a row, you go back to the hosted endpoint and you do not feel bad about it. The work being done on the Dataverse plugin for coding agents is a good example of how the hosted side is pulling ahead on exactly this problem.
No place in production. I want to be direct about this. I would not put a locally hosted model behind a production Power Automate flow at my desk. It is not redundant, it is not monitored, it is not anyone’s responsibility but mine. Production is a hosted endpoint with an SLA or it is self-hosted properly in infrastructure that someone else also watches. The OpenAI and Dell Codex on-prem partnership is the more honest version of what on-premise AI actually requires to work at that level.
Where I would (and would not) reach for it next time
I will keep running Ollama locally for prompt iteration, exploratory extraction tests, and any draft work that should not leave my machine. That is the lane. It is a real lane and it saves me real hours.
I will not reach for it when I need consistent tool calling, when the task needs the reasoning quality of a frontier model, or when anyone other than me depends on the output. For those, I go back to hosted. I share my thinking on this kind of tradeoff regularly on LinkedIn, and the pattern is always the same: prototype locally, ship hosted.
Should you start learning local AI right now? Yes, if you prototype a lot or handle drafts you would rather not send to a cloud endpoint. The intuition you build about model size, quantization, and context limits pays off everywhere else. Just do not let anyone sell you that your laptop is going to replace a hosted model in production. It is not. Not yet.
Frequently Asked Questions
How do I get started running Ollama locally for automation prototyping?
You can install Ollama on a machine with sufficient RAM (32GB is a comfortable starting point) and pull models suited to your tasks, such as a smaller 8B model for fast iteration or a larger 14B model for more complex reasoning. From there you can test prompts and extraction logic against local documents without touching any hosted endpoints or incurring API costs.
What are the main benefits of using a local AI model instead of a hosted one?
Local models let you iterate quickly without worrying about rate limits, token costs, or network latency. They also allow you to work with sensitive or uncleared documents that you would not want to send to an external service during early experimentation.
When should I use a local model versus a hosted AI endpoint for automation work?
Local models are well suited to the prototyping and prompt-tuning phase, where speed and privacy matter more than peak output quality. For production automation workflows, hosted models typically offer better reasoning quality and reliability, making them the stronger choice once you move past early experimentation.
Why does model size matter when choosing an Ollama model for automation tasks?
Smaller models respond faster and use less memory, which suits quick iterative work, but they reason less reliably on complex tasks compared to larger models. Understanding this tradeoff locally builds practical intuition that directly applies when selecting and configuring models in production agentic workflows.
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