Local AI or Cloud AI: Which Fits Your Use Case?

Local AI and cloud AI: the definitions

Local AI (also called on-premise, self-hosted or on-device) means an AI model runs on infrastructure you control: a server in your own data centre, a GPU workstation or an edge device. It typically uses open-weight models such as Llama, Mistral, Qwen, Gemma or the Swiss model Apertus, served through tools like Ollama, LM Studio, llama.cpp or vLLM.

Cloud AI means you use a model through a provider's API or web app – for example ChatGPT from OpenAI, Claude from Anthropic or Gemini from Google. The provider runs the hardware, model and updates; you send requests and usually pay per use. The two approaches are not mutually exclusive: many organisations combine them in a hybrid architecture.

Head-to-head: control, cost, performance, privacy

The four decisive dimensions, side by side:

• Control & sovereignty – Local: data and model stay in-house, versions are stable, no vendor lock-in. Cloud: the provider controls the model, updates and availability; models can change without your input.
• Cost – Local: high upfront investment (CapEx) in hardware, then predictable fixed costs regardless of volume. Cloud: no upfront cost, usage-based (OpEx), scaling directly with request volume.
• Performance – Cloud: access to the strongest frontier models for demanding reasoning and multimodal tasks. Local: open-weight models, strong and good enough for many tasks, with limits at the very top of capability.
• Privacy – Local: no data leaves for third parties, ideal for professional secrecy and highly sensitive data. Cloud: requires a data-processing agreement, verified data residency and clear rules on training use.
• Latency & offline – Local/edge: low latency, works offline and air-gapped. Cloud: network-dependent, but globally available with no maintenance on your side.
• Scaling & maintenance – Local: limited by your own hardware, needs an ops/MLOps team. Cloud: elastically scalable, with operation and security handled by the provider.

Cost, realistically: CapEx, OpEx and the break-even

With cloud AI you pay per use (usually per token), with no upfront cost. Actual prices change often – check the provider's current pricing. The upside: you start immediately and costs follow real demand. The downside: at high, steady volume recurring costs add up quickly.

With local AI the upfront hardware investment dominates. As rough technical guidance: a 7–8 billion parameter model in 4-bit quantisation runs on a consumer GPU with around 16 GB of VRAM; a 70 billion model needs closer to 40 GB and up, often several GPUs. Add electricity, cooling, spare parts and – frequently underestimated – staff for operations, security and model updates.

Rule of thumb: low or highly variable volume favours the cloud; high and steady volume can be cheaper locally over time. Calculate over the full lifetime (total cost of ownership) and include hidden costs such as redundancy and specialist staff.

Privacy and compliance: the Swiss lens

In Switzerland the revised Data Protection Act (nFADP/revDSG) has applied since September 2023, supervised by the FDPIC (EDÖB). For Swiss matters the nFADP governs, not the GDPR; the EU GDPR applies when you process people in the EU. In addition, the EU AI Act has extraterritorial reach and can affect Swiss providers placing AI systems on the EU market.

For particularly sensitive data and professional secrecy (e.g. Art. 321 of the Swiss Criminal Code for doctors, lawyers, fiduciaries), local AI is often the cleanest solution: the data never leaves your own infrastructure. If you still want cloud, insist on: data residency in Switzerland or the EU, a data-processing agreement, a contractual guarantee that your data is not used for training, and a clear deletion concept.

A Swiss angle on sovereignty: Apertus, an openly available language model developed at ETH Zurich, EPFL and the CSCS supercomputing centre, can be run locally. Such open models strengthen digital independence when data sovereignty is the top priority.

Performance and capability: what models really deliver today

For the most demanding tasks – complex reasoning, long context, advanced multimodality – the large frontier models in the cloud still lead. They are continuously improved by the provider without you having to upgrade hardware.

For many real business tasks, however, local open-weight models are long since good enough: summarising, extracting information, classifying, drafting, or knowledge-grounded search (RAG) over internal documents. Quantisation shrinks models for more modest hardware – at the cost of slightly lower accuracy. Local models can also be fine-tuned to your domain, improving consistency and specialist language.

On latency there is no blanket verdict: on-device models often respond very fast without a network hop, while large cloud data centres offer enormous parallelism and throughput. What matters is your load profile.

Decision guide: when to go local, cloud or hybrid

Use these criteria to reach a sound choice quickly:

• Choose local when: strict confidentiality or professional secrecy applies, volume is high and steady, you must work offline or air-gapped, full control and customisation matter, and you have an ops team.
• Choose cloud when: you need top model performance, volume is low or variable, fast time-to-value matters, you have no ops team, and you always want the latest multimodal features.
• Choose hybrid when: different tasks call for different answers. A proven pattern: process sensitive data and standard tasks locally, route the hardest queries to the cloud, and anonymise or mask personal data locally before any cloud call.
• Whatever the model, always check: where the data sits, who is liable, what the exit looks like, and how you avoid lock-in. Standardised interfaces and open-weight options keep the door open.