Local AI vs Cloud AI: Honest Tradeoffs

The comparison at a glance

When local AI is the right choice

Local AI is a strong fit when privacy is not negotiable. If your workflow involves confidential documents, client data, personal health information, legal work, or anything you would not want stored on a third-party server, local inference removes that risk from the equation entirely. There is no API call to intercept, no data retention policy to audit, and no breach surface beyond your own device.

It is also the right choice if you want to work without an internet connection. Once a model is downloaded, it runs fully offline. Flights, remote locations, restricted networks — none of that matters for local inference.

For everyday tasks — writing, rewriting, summarizing, coding assistance, Q&A — a 7B or 8B parameter model running on Apple Silicon is genuinely capable. Most day-to-day work does not require the frontier reasoning of the largest cloud models.

When cloud AI is the right choice

Cloud AI has real advantages. The largest frontier models are ahead of local 7B models on complex multi-step reasoning, very long documents, and tasks that require broad world knowledge. If your work regularly involves 100,000-token contexts or advanced tasks that push model capability limits, a cloud subscription may still make sense for those specific cases.

Cloud AI is also faster in raw token throughput. Server-side clusters generate at 50–200 tokens per second; a local 7B model on an M2 generates at 15–30. For most reading-speed workflows the difference does not matter, but for high-volume generation tasks it can.

The honest position: for most everyday AI work, local is capable and private. For the edge cases where you need the most capable model available or the longest context windows, keeping a cloud option available is practical.

The hybrid approach

SilicaAI is built around a local-first default with optional cloud integrations. You install local models and use them for everything by default. If you want to connect an external provider for specific tasks, you can — but you never have to.

This matches how most people actually work once they try it: local for sensitive work and everyday tasks, cloud for the occasional task that genuinely needs more capability. You stay in control of which requests go where.

What this comparison does not cover

This page compares local and cloud AI at a structural level. It does not compare specific cloud products or rank providers against each other. Every major cloud AI provider has different privacy terms, data retention policies, and model capabilities — if you are evaluating a specific cloud product, their documentation is the right source.