Why choose private AI over public cloud APIs?
Public cloud AI services are convenient for casual experiments, but risky for business and research: confidential files leave your control, queries are metered per token, and operations depend on overseas servers. A privately hosted model ensures your data stays in Wales, protected under your project controls.
Follow the data: public cloud vs. private Wales hosting
Toggle between the two journeys below to see how a query and attached files travel.
Cloud journey: your query and files cross the public internet to overseas servers. They are processed under a third party's retention, training and pricing terms — metered per token, and unavailable offline. (General characteristics of public AI APIs, for illustration.)
Private journey: your request stays inside an isolated, Wales-hosted workspace at Bangor University. Project-specific controls protect confidential data and prevent it training external models — with no per-token charges during the pilot, subject to capacity and governance.
What changes when the AI is on your side of the wire
| Consideration | Public cloud AI APIs | North Wales AI Platform (private, in Wales) |
|---|---|---|
| Where your data goes | Off-site — usually to overseas provider servers | Into an isolated workspace hosted at Bangor University |
| Training on your data | Depends on the provider's plan, settings and changes to them | Project controls aim to prevent use for training external third-party models |
| Cost model | Per-token fees that grow with usage; rate limits can block work | No commercial per-token charges during the pilot, subject to capacity & governance |
| Offline & low-connectivity work | Not possible — dependent on internet and provider uptime | Edge AI units can run suitable models on your site, disconnected |
| Latency | Internet round-trip to distant data centres | Regional network latency; near-zero on deployed edge units |
| Model choice | The provider's models, their updates, their deprecations | Managed open-weight models (Qwen, GLM, DeepSeek or successors) on your terms |
| Adapting models to your domain | Limited fine-tuning options; often extra cost | LLM/VLM fine-tuning and RAG on your own corpora, guided by Bangor researchers |
| Fits existing tools | Proprietary SDKs or API standards | OpenAI-compatible API — change a setting, not your workflow |
| Support & people | Documentation and paid support tiers | Academic leads, student project teams, training and technical support |
What private AI is — and what it isn't
It isn't a cheaper way to copy the biggest consumer chatbots. Frontier closed models remain frontier closed models. What a locally hosted platform does is make very capable open-weight models a practical, private foundation for the boring-but-valuable 90% of business AI work — document drafting and review, classification, extraction, code, RAG over your own records, agents over your own systems, vision and sensor analytics.
It isn't self-service HPC you're left to wrestle alone. Bangor manages the infrastructure, security, access and support; projects are scoped with an academic lead. You bring the problem, the data and the domain knowledge.
Where cloud AI still makes sense
Fully public marketing copy, one-off brainstorming, or tasks where convenience outweighs sensitivity — public services are fine, and the platform's models are complementary, not a ban on anything.
Where private AI wins
Contracts, clinical-adjacent records, designs and IP, client data, internal codebases, sensor and telemetry streams — any workload where the data can't or shouldn't leave your control, or where volumes make token bills run away.
Where edge AI wins
On-site inference with no connectivity, deterministic latency, or pilots that need the hardware in front of customers — the DGX Spark units are built for exactly this.
Curious what it would mean for your data?
Tell us about a sensitive or high-volume task you'd like to hand to AI — we'll tell you honestly whether private AI fits.