model-sovereignty

Choose fine-tuning over RAG when you need consistent decision logic or behavior alignment, whereas RAG is better for frequently changing documents. A decision hierarchy helps minimize maintenance burden by escalating from prompting to RAG, fine-tuning, distillation, or local hosting.

Decide between self-hosting and cloud private endpoints by applying a sovereignty test and data classification checklist. Calculate cost break-even heuristics and maintenance budgets to determine if sensitive workflows require local processing or if cloud private endpoints suffice.

The best way to host models locally for privacy involves using tools like Ollama, vLLM, or llama.cpp. Evaluate data sensitivity to route regulated workflows on-premises, ensuring data sovereignty while managing inference costs and maintenance budgets.

Yes, you can use LoRA or QLoRA for model customization instead of full fine-tuning to reduce computational overhead. These techniques are recommended within the customization hierarchy when consistent behavior is required but full distillation is unnecessary.

Data classification directly affects model selection and routing by determining whether data must be processed locally or routed differently. A sovereignty checklist guides these decisions, ensuring sensitive workflows remain on-premises while standard queries use cloud endpoints.

Limitations of escalating to local hosting include higher maintenance budgets and infrastructure overhead. While it satisfies privacy and sovereignty requirements, teams must evaluate cost break-even points and baseline token loads to ensure on-premises inference remains sustainable.