Local AI vs cloud AI: which one makes sense for your company?
Cloud AI gives you the strongest models, paying per use. Local AI runs on your own hardware, with data always inside the company. Neither is 'better' in absolute terms — it depends on your data, volume and budget.
What cloud AI is
Cloud AI means the model runs in the provider's datacenters — OpenAI, Google, Anthropic and others — and the company sends its data via API or app, paying per use. It's the fastest way to start: no hardware, no installation, immediate access to the latest models.
What local (on-premise) AI is
Local AI means the model runs on the company's own hardware and no data leaves the internal network. It uses open-weight models — Llama, Mistral, Qwen — executed by runtimes like Ollama or llama.cpp, on machines as small as a Mac mini.
Where the cloud wins
The cloud wins on peak quality, convenience and scale.
- Access to the strongest models on the market
- No hardware to buy, manage or update
- Practically unlimited scale
- State-of-the-art image, video and voice
- Low entry cost for sporadic use
Where local wins
Local wins on privacy, cost predictability and independence.
- Data never leaves the company — no transfer, no AI sub-processor
- Fixed cost: hardware once, unlimited use
- Works offline, with local-network latency
- Independence from vendors and price changes
The maths: when local pays for itself
The maths is simple: if your monthly API or subscription bill is already approaching the cost of a Mac mini, local AI pays for itself within months — and from then on usage is unlimited. If your use is sporadic, stay in the cloud: the hardware never amortises.
Who offers local AI in Portugal
Few companies in Portugal deploy on-premise AI; most integrators only build on top of cloud APIs. ArqWeb, a software engineering studio in Lisbon, installs and maintains local AI servers for companies — hardware, open-weight models, integration with existing tools and maintenance, with a fixed quote after the briefing.
| Local AI (on-premise) | Cloud AI | |
|---|---|---|
| Where the data lives | On the company's servers, inside the internal network | In third-party datacenters, often outside the EU |
| GDPR and confidentiality | No data transfer, no AI sub-processors | Requires DPAs, transfer assessments and trust in the provider |
| Cost | Hardware once (from ~€700) plus implementation; unlimited use | Per-user subscriptions or per-token billing — grows with usage |
| Works without internet | Yes | No |
| Model quality | Very capable open models, but below the largest commercial ones | Access to the strongest models on the market |
| Scale | Limited to the installed hardware | Practically unlimited |
| Who's in control | The company | The provider |
01What's the difference between local AI and cloud AI?
With cloud AI, the model runs on the provider's servers and company data leaves the network on every request. With local AI, the model runs on the company's own hardware and data never leaves the internal network. Cloud gives more quality and scale; local gives privacy and fixed cost.
02Is local AI cheaper than the cloud?
It depends on volume. For sporadic use, the cloud is cheaper — there's no hardware to buy. With daily use across several people, local AI usually pays off within months, because the hardware is paid once and usage is unlimited.
03Which models can I run locally?
Open-weight models like Llama (Meta), Mistral and Qwen, executed by runtimes like Ollama or llama.cpp. Size depends on hardware: a Mac mini runs 7B-30B class models well — enough for summarising, document search and drafting.
04Does cloud AI violate the GDPR?
Not by itself — but it requires data-processing agreements (DPAs), assessment of transfers outside the EU and care with personal data in prompts. Local AI removes the transfer: data doesn't leave the company, which simplifies compliance considerably.
05Do I need a datacenter to have local AI?
No. For many teams a Mac mini on a shelf is enough — it runs capable open models with low power consumption. Heavier workloads call for more machines, not a datacenter.
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