On-device AI vs cloud AI for business: how to choose

On-device AI or cloud AI for your business? A plain comparison of speed, cost and privacy, the tasks each one suits, and how to decide which work goes where.

By Sourav De · 12 November 2026 · 6 min read

Part of the guide: Private and on-device AI for business: a guide

A closed dark laptop on a desk by a window, its edge lit orange, with monsoon clouds over rooftops in the distance

The short answer: cloud AI gives you the largest models and asks you to send your data out and pay per user or per use. On-device AI runs a smaller model on the computer in front of you: nothing leaves the machine, it works without internet, and there is no bill per question. Use on-device AI for everyday work on sensitive material, and cloud AI for the occasional task that needs the biggest model and involves nothing confidential. Most companies need both, with a clear rule for which work goes where.

What is the difference between on-device AI and cloud AI?

The difference is only where the thinking happens.

With cloud AI, your question and any attached file travel over the internet to a data centre. A very large model, running on specialised hardware, produces the answer and sends it back. You pay a subscription per user, or a fee per unit of use.

With on-device AI, the model is a file on your own computer. Your question is processed by your own processor and memory. Nothing is sent anywhere, so it works on a train, in a factory with poor connectivity, or with the network cable pulled out.

There is a middle option, AI running on a server your company owns, which behaves like cloud AI inside your office walls. We describe all the levels in sovereign AI and private AI.

How do they compare on speed, cost and privacy?

	On-device AI	Cloud AI
Where your data goes	Nowhere. It stays on the machine	To the provider's servers
Works offline	Yes	No
Response time	Starts at once; speed depends on your hardware	Depends on your connection and the provider's load
Size of model	Smaller, limited by the device	The largest available
Best at	Summarising, drafting, extracting, searching your own files	Open-ended research, long reasoning, large creative tasks
Cost model	Included with the software or the device	Per user per month, or per use
Cost as you grow	Flat	Rises with headcount and usage
Who controls changes	You: the model changes when you update it	The provider: models and terms change on their schedule
What you must trust	Your own device security	The provider's contract and controls

Three of those rows deserve a closer look.

Speed

People assume the cloud is faster because the hardware is bigger. In practice it depends on the task. For a short request, such as rewriting a paragraph or pulling the dates from a letter, a local model starts answering immediately because there is no round trip. For a long, difficult request, a large cloud model will usually finish first and do it better. On a slow or patchy connection, local wins every time, because the alternative is waiting or nothing.

Cost

Cloud AI is priced so that the first month is cheap. The real number is the annual one, multiplied by headcount. Ten licences is a line item; a hundred is a budget. Usage-based pricing adds unpredictability, because your bill depends on how enthusiastic your staff are.

On-device AI costs nothing per question. The cost is in the hardware you already own, and the limit is what that hardware can run. If you are adding up your subscriptions, the worked example in what software really costs a 25-person company shows how the AI line compares with the rest.

Privacy

This is the row that decides most cases. A cloud provider's business plan may promise not to train on your data. Even when that promise is kept, the data has still left your company, and you are relying on a contract. With on-device AI there is nothing to rely on, because nothing left.

That changes what staff can safely do. An assistant that never sends data out can be given the salary register, the client contract and the bank statement. A cloud assistant, under any sensible policy, cannot.

Which tasks suit on-device AI?

The everyday ones, which are most of them.

Summarising a long document, email thread or meeting transcript
Drafting and rewriting letters, replies and reports
Fixing tone and grammar, and translating between English and Indian languages
Reading scanned invoices and photographs of documents into text
Finding a file or a figure in your own folders
Answering questions about your own business data
Turning a plain request into a command or a formula

These share two features. The source material is yours and often confidential, and the task is bounded: the answer is in the document or the data, not in the model's general knowledge. A small model that can read the document does well here.

Which tasks still need cloud AI?

Research across public information where the model's breadth of knowledge matters
Long chains of reasoning over very large inputs
Generating images, video or large amounts of original creative material
Specialist work, such as complex code generation across a big codebase

Notice that most of these involve public or non-sensitive inputs. That is the natural division: private material stays local, and public questions can go to the cloud.

Can ordinary office laptops run AI?

Yes, with the right software. This surprises people, because the AI in the news runs in vast data centres. But models built for one job each, and tuned to run on a normal processor, do not need a graphics card or a new machine.

This is how we built DNX OS. Nova, its assistant, runs on the laptop's own processor and works on machines with 4 GB of RAM. It uses small specialist models trained for one job each. It needs no cloud account and no per-seat AI bill, and each AI service is sandboxed with no network access.

DNX Files with a Word document selected and a Summarise button beside Open — A real screenshot of DNX Files. Summarise hands the document to Nova, which reads it on the laptop itself.

We should be straightforward about the limit. A model on a 4 GB laptop will not match the largest cloud models on open-ended tasks. For companies that want more, DNX AI Cloud is planned for 2027, with larger models on GPUs hosted in India.

How should a company decide?

Sort your work, not your vendors.

List the ten things your staff most often ask AI to do, or would if they were allowed.
Mark each one: does it involve confidential or personal data? If yes, it belongs on the device or on your own server.
Mark each one: does it need the largest model? Be honest. Summaries and drafts do not.
Count who needs cloud AI at all. It is usually a handful of people, not the whole company.
Write the rule down in one line: which kind of work goes to which tool. That line is the heart of the AI policy we describe in shadow AI and a policy that works.
Test with the network off. Whatever you were told, an assistant that stops working without internet is cloud AI.

The choice is not on-device or cloud. It is which work you are willing to send outside, and the answer for most confidential business work is none of it.