Local vs Cloud AI: how to keep your data in-house
Using AI at work today often means pasting a contract, a customer file or a confidential email into an online service… and hoping for the best. It's convenient, but two questions always come back: where does my data go, and how much will this cost at scale? Local AI answers both. Here, in plain language, is when it makes sense — and when the cloud is still the right call.
Local AI vs cloud AI: what are we talking about?
A cloud AI (ChatGPT, Claude, Gemini…) runs on a provider's servers. You send your text, it replies, and you usually pay per use. A local AI is the same kind of language model (an "LLM"), but installed on your machine or server. Nothing leaves your premises. It relies on open-source models (Llama, Mistral, Qwen…) and tools that run them easily, such as Ollama.
The 3 real reasons to go local
1. Privacy (and GDPR)
This is reason number one. Law firms, healthcare, HR, finance: as soon as data is sensitive, sending it to a third party is a problem. With local AI, no data leaves your walls. You keep full control, and compliance becomes far easier to demonstrate.
2. Predictable cost
The cloud bills per token — a few cents here, a few cents there. Across a team using it all day, the bill climbs and becomes hard to forecast. Local AI is a one-off hardware investment: once the machine is in place, use it as much as you want, with no meter running.
3. Availability and sovereignty
No API outage, no overnight price change, no model that vanishes. Your tool is yours and keeps working — even offline.
Let's be honest: what local AI doesn't (yet) do as well
We won't oversell it. For very complex reasoning or top-tier creative tasks, the largest cloud models still lead. Local AI also needs a minimum of hardware and a bit of maintenance. The good news: for the vast majority of business needs — summarising, classifying, extracting, answering from your own documents — recent open-source models are more than up to the job.
"Do I need a NASA-grade server?" — No.
That's the biggest misconception. A lightweight model runs perfectly well on a recent PC with a consumer graphics card, and some even run on a good CPU with no dedicated GPU. We size the machine around real usage, not the other way round. Often, a single well-chosen workstation serves a whole team.
The best answer is often: both
In real life, the smartest solution is hybrid. We keep everything sensitive local, and switch to a cloud model only for the heavy tasks that justify it. You protect what matters without giving up cloud power when it's genuinely useful. That's exactly the kind of architecture we build into our custom AI interfaces.
The right question isn't "local or cloud?" but "what must stay in-house, and what can leave?"
In short
Local AI isn't a purist's gadget: it's a concrete answer to two very real problems — privacy and cost. It doesn't replace the cloud everywhere, but it takes back control where it counts. If you handle sensitive data or your AI bill is spiralling, there's probably a local — or hybrid — version worth building.