Self-hosted AI is often presented as the private and inexpensive alternative to cloud services. Cloud AI is presented as effortless. Both descriptions omit the operating conditions that determine whether either choice is suitable.
Cloud services
Cloud models provide strong capability without purchasing hardware or operating inference infrastructure. They are quick to integrate and scale with usage. The business must evaluate supplier terms, data location, retention, availability and dependency on pricing and API changes.
Self-hosted systems
Self-hosting provides more control over deployment and can keep data within infrastructure selected by the business. It may be attractive for stable, high-volume workloads or strict data boundaries. It also transfers responsibility for hardware, updates, model serving, security, monitoring and capacity planning.
Running an open model is not the same as operating it reliably. Performance depends on hardware, quantisation, context size and the task. A smaller private model may be entirely adequate for classification but unsuitable for complex drafting.
Compare the whole system
Evaluate output quality on representative cases. Calculate model usage or infrastructure cost alongside engineering and support. Consider recovery, peak demand and how quickly the organisation can apply security updates.
A hybrid approach
Many businesses can route tasks by sensitivity and complexity. Deterministic processing and selected AI tasks may run locally, while difficult low-volume work uses a cloud model after data minimisation. The workflow should make that routing explicit.
Choose based on evidence from the intended workload. Control is valuable only when the organisation can carry the responsibility that comes with it.