Every business considering AI eventually hits the same fork: run it in the cloud, or keep it local?
Both options work. Both have real costs and real limits. The right choice depends on your data sensitivity, your budget, and what the AI actually needs to do. Here is a clear breakdown.
The core tradeoff
Cloud AI means sending your data to an API (OpenAI, Anthropic, Google, or similar) and getting a response back. You get access to the most powerful models available. You pay per use, which looks cheap until you scale. And your data leaves your premises.
On-device / self-hosted AI means the model runs on hardware you control - a server you own, a server you rent, or in the case of mobile apps, directly on the user's device. Your data never leaves. The cost is mostly fixed infrastructure. The trade-off is capability: smaller models, more setup work.
Neither is universally better.
On-device: when it makes sense
On-device AI is the right call when:
The data is sensitive. Passports, medical records, HR data, financial documents, legal contracts. If you would not email this data to a third party, you should not send it to an API either. On-device means the data never moves.
You want zero per-call cost. Once the infrastructure is set up, processing more documents does not cost more. For high-volume workflows, this is significant.
Latency matters and connectivity cannot be guaranteed. A mobile app that needs to work offline, or a system in a location with unreliable internet, needs local processing.
We have shipped this in production. Kept - our iOS document vault app - does all AI processing on-device using Apple's Vision framework. Passports, IDs, expiry dates - all extracted on the phone, with no data ever sent to a server. Privacy manifest, strict concurrency, App Store live.
The on-device models available through Apple Vision, and local LLM frameworks that run on modern hardware, have improved to the point where they are genuinely useful for a wide range of tasks - not just a fallback.
Cloud AI: when it makes sense
Cloud AI is the right call when:
The task needs the most capable model available. Complex reasoning, long-form synthesis, code generation, nuanced document analysis - the frontier models (GPT-4o, Claude, Gemini) are still ahead of what you can run cheaply on local hardware.
Volume is low and the data is not sensitive. If you are processing a few hundred documents per month and they contain nothing regulated, the simplicity of an API is worth the per-call cost.
You need fast iteration. Getting started with a cloud API is faster than setting up self-hosted infrastructure. For a proof-of-concept or a sprint, this often makes sense - with a clear plan for where to go at scale.
Self-hosted: the middle path
Between device and cloud sits self-hosted: you rent a server (or use your own), install an open-source model (Llama, Mistral, Qwen, and others), and run inference there.
You keep your data. You pay fixed infrastructure costs, not per-call fees. You get more capability than on-device. The setup and maintenance cost is real but manageable.
For many UAE SMEs, self-hosted on a reasonable cloud instance is the practical answer: data stays in the UAE or in a region you control, costs are predictable, and capability is sufficient for document retrieval, extraction, and Q&A tasks.
A simple decision guide for an SME
Ask these questions in order:
Is the data sensitive (legal, medical, financial, personal IDs)?
- Yes: on-device or self-hosted. Do not send it to a third-party API.
- No: proceed.
How many documents or queries per month?
- High volume: on-device or self-hosted. Per-call costs add up fast.
- Low volume: cloud API may be simpler to start.
What does the AI need to do?
- Simple extraction, classification, Q&A on structured documents: on-device or smaller self-hosted models handle this well.
- Complex reasoning, long-form synthesis: frontier cloud model is likely needed.
Do you need it to work offline?
- Yes: on-device only.
Run-cost reality
Cloud API costs are easy to underestimate. A document-heavy workflow that looks cheap at low volume can cost thousands per month at scale. Token pricing is per-use, and documents are often long.
On-device has no per-call cost after setup. Self-hosted has server costs (typically a few hundred to a few thousand AED per month for capable hardware) regardless of volume.
The sprint maps your expected volume and use case to an architecture and gives you a monthly run-cost estimate. That number often changes the conversation about which approach makes sense.
The right answer for your business depends on your specific data, volume, and use case. The sprint is how we figure that out - two weeks, AED 5,000, and you leave with a clear architecture recommendation and a fixed build quote. Start here.