Local AI or Cloud AI: an organisation decision table
Short answer: Local AI keeps inference and retrieval inside an agreed environment. Cloud AI trades part of that boundary for managed flexibility. Choose from the data and operating model, not from the word private.
Questions to answer first
Separate payload, prompts, logs, telemetry, updates and support fields. Then record latency, concurrency, retention and the owner for each path. “Private” is not a substitute for a data-flow map.
Comparison table
| Dimension | Local | Cloud | Hybrid |
|---|---|---|---|
| data boundary | stays in the agreed environment | includes an external provider | split by named connector |
| cost | hardware, power, backup and people | usage, storage and contract | carries both cost shapes |
| connectivity | search does not wait for an API | requires connectivity | needs recovery design |
| operations | model, index and identity ownership | uses managed services | documents split ownership |
Use the Local AI decision aid and Trust Center before building a business case.
RAG or fine-tuning
RAG is often the first test for changing documents because the corpus and retrieval can be revised. Fine-tuning changes model behaviour and needs its own data rights, evaluation and rollback plan. See the Private RAG guide.
Limitations
Local is not automatically cheaper or safer. Include GPU, power, patches, identity, backup and team skills in TCO, and require human review for material decisions.
Read next
Private RAG: a document-search architecture that keeps data in the boundaryOpen-source AI is not free: the costs and responsibilities to planWhat to maintain every month after an AI go-liveContinue with the decision context
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