Private local LLMs vs. cloud AI: what BC businesses should weigh

The productivity gains from AI are real — but so is the risk of quietly sending your most confidential files across the border, and the two are easy to confuse.

Every business in Greater Vancouver is being pitched AI right now. Drafting, summarising, answering staff questions, sifting through contracts — the tools genuinely help. The harder question isn't whether to use AI. It's where your data goes when you do. For most office tasks, public cloud AI is perfectly fine. For a law firm, a clinic, an accounting practice or an engineering shop, that answer changes the moment a client file or a set of drawings gets pasted into a chat box.

The two roads: public cloud AI vs. a private local LLM

Public cloud AI means the big hosted services — the ChatGPT, Gemini and Claude apps and their APIs. You send text up, an answer comes back, and the model runs in a data centre you don't control, usually in the United States. It's the fastest way to get the most capable models, and you pay only for what you use.

A private or local LLM means running an open-weight model — Llama, Mistral or Qwen are the common families — on hardware you control. That might be a GPU server in your office, or a virtual machine in a Canadian cloud region. The model never phones home. Your prompts and documents stay inside your own boundary.

The real data risk — and who should care most

The risk isn't that a cloud provider is reading your email. It's more mundane and more important than that:

• Data leaves Canada. Most public AI processing happens on US infrastructure. For a Canadian firm with client-confidentiality obligations, that crossing alone can be a problem under PIPEDA, professional rules, or a client contract that says data stays in Canada.
• Logging and retention. Even where a vendor promises not to train on your data, prompts may be logged for abuse-monitoring and held for a period. That's fine for a marketing draft and not fine for a privileged file or a patient record.
• Training on your inputs. Free and consumer tiers have historically reserved the right to learn from what you type. Business and enterprise tiers usually don't — but the default many staff reach for is the consumer one.
• Shadow use. The biggest leak is rarely a policy decision. It's an employee pasting a client agreement into a personal account to "just summarise this quickly."

If you're in law, health, finance, or engineering with sensitive IP, those four points are exactly why this matters to you and less so to a retailer or a trades business.

When cloud AI is genuinely fine

Let's be fair to the cloud — it's the right tool more often than not. Reach for it when the data isn't sensitive and you want the strongest possible model with zero infrastructure: drafting website copy, brainstorming, cleaning up internal notes, writing code against non-secret repositories, or summarising public documents. If you sign up for a business or enterprise tier with a no-training agreement and a data-processing addendum, you can responsibly widen that circle further. The mistake is assuming the consumer free tier is the same thing — it isn't.

The question isn't "cloud or local." It's "which data is allowed to leave the building, and which isn't" — and then matching the tool to the answer.

What "local LLM" actually looks like in practice

It's less exotic than it sounds. A working private setup has three parts:

• A GPU server — a machine with a capable graphics card (or a Canadian cloud GPU instance) sized to the models you want to run. A single modern GPU comfortably runs a strong mid-sized open model for a small team.
• An inference engine — software that serves the model. Ollama is the easy on-ramp for a small office; vLLM is the choice when you need to serve many people at once efficiently.
• RAG over your own documents — "retrieval-augmented generation." Instead of hoping the model memorised your handbook, you index your own files and the system pulls the relevant passages in at question time. That's what turns a generic model into one that answers from your policies, contracts and project history — without any of it leaving your network.

The cost model is the part people get wrong

Cloud AI is billed per token — roughly, per word in and out. For light, occasional use that's wonderfully cheap. But as a tool becomes part of daily work for a whole team, a usage meter that never stops can grow into a surprising monthly line item, and it scales with how much you use it.

A local LLM flips that. You buy or rent the hardware once, and the marginal cost of the ten-thousandth query is essentially zero. The trade is that you carry fixed infrastructure and someone has to keep it patched, monitored and updated. For a steady, heavy workload over your own confidential data, owned infrastructure often wins on both cost and control. For spiky, lightweight, non-sensitive use, the cloud meter is hard to beat.

How to decide — a short checklist

1. Is the data sensitive or regulated? If yes, lean local or Canadian-hosted. If no, the cloud is on the table.
2. Do client contracts or your profession require Canadian data residency? If yes, that decides it.
3. Is usage occasional or constant? Occasional favours pay-as-you-go cloud; constant favours fixed-cost local.
4. Do you need the absolute strongest model, or is a very good one enough? Frontier cloud models still lead; open models are more than good enough for most internal tasks.
5. Who keeps it running? Local means real infrastructure to maintain — plan for that before you commit.

FirstLayerIT sets up and manages either road — a hardened business-tier cloud arrangement, a private local LLM with RAG over your own documents, or a blend of both — and keeps it patched, monitored and inside your data-residency rules as part of your flat monthly per-device plan. If AI is starting to creep into your team's day, see AI & local LLMs for how we'd approach it for your firm.