Local AI or cloud AI: start with the right question

What do you actually want to delegate?

“What is the best AI?” is too broad to guide a purchase. Rewriting an email, finding information in documents and producing code are different jobs. Write down one recurring task, how sensitive its inputs are and how you will assess the result.

Take meeting notes as an example. A useful output is not simply elegant prose: it is a list of actions that distinguishes decisions, owners and deadlines without inventing missing information. That definition gives you something concrete to compare.

Local does not always mean isolated

An application can run a model on your computer while also offering connected features. Ollama’s documentation distinguishes local execution from cloud and web-search features and describes a local-only configuration. Check the functions that are enabled rather than relying on the product name.

For a trial, map the path of your information: source document, application, model, any external service and destination of the result. This small map can be more useful than a long marketing claim.

Four criteria to put on the table

Use this as a discussion framework, not a universal ranking.

Run a small, useful trial before committing

Choose five representative cases and keep a reference answer prepared without the tool. Run the same cases through each option. Record errors, necessary corrections, total time and tasks that cannot be completed. Five cases are an exploratory trial, not a scientific performance benchmark.

Do not use sensitive personal or work information for a casual demonstration. Synthetic examples are enough to assess the output format and obvious limitations. Real use still needs a separate privacy review.

Three situations, three starting points

If you mainly work with non-sensitive text and want to avoid maintaining an installation, our starting point would be a limited trial of a hosted service. If documents must remain on the computer, examine local execution and every connected feature with the person responsible for the data. If you are simply experimenting, start with the hardware you already own rather than buying a graphics card.

These are starting points, not permanent verdicts. Keep an exit route: export results in ordinary formats, retain your instructions and avoid opaque automations. Ask what remains of your work when you stop using the service.

The cost calculation we would make

Compare the same period across four lines: any necessary purchase, subscription or usage, installation, and review. No upfront charge does not mean no setup time. Equally, already owning a suitable computer changes the calculation. Do not charge one scenario for an entirely new machine while assigning zero hardware cost to the other without explaining why.

Add a “usable result” column. An answer produced in ten seconds but corrected for ten minutes has taken ten minutes and ten seconds. That is a calculation example, not a model measurement. Our AI evaluation worksheet explains how to record the trial.

Small, reversible and documented

Our suggestion is to use one tool for one task over a week of ordinary work. Do not spend the week chasing the perfect prompt. Record successes, failures and the moments when you chose not to use it. Keep sensitive information out of the initial trial.

Finish with a decision sentence: “I will keep this solution for this task, under these conditions.” If you cannot complete it, the purchase can wait. The choice does not need to be universally right; it needs to be defensible for your use.