Local vs Cloud AI: When to Choose Each

The local AI stack has matured. Here's a practical decision framework.

Choose local when

• Privacy – Legal, medical, financial, or proprietary data. Nothing can leave your perimeter.
• Cost – High volume. API spend exceeds the cost of hardware. Break-even is often 1–3 months.
• Latency – You need sub-second response and can't depend on network.
• Offline – Air-gapped or unreliable connectivity. Inference must work without internet.
• Customization – Fine-tuning, custom prompts, or model swapping without vendor approval.

Choose cloud when

• Quality – You need the best model (GPT-4, Claude Opus). Local models lag for complex tasks.
• Multimodal – Vision, voice, or image generation. Local options are limited.
• Zero ops – You don't want to run servers. Cloud is managed.
• Scale spikes – Bursty traffic. Cloud scales; local is fixed capacity.

Hybrid approach

Many teams use both: local for sensitive or high-volume tasks, cloud for quality-critical or multimodal work. Open WebUI and n8n support both in the same workflow.