Where does the model run, and why does it matter?
Businesses considering AI eventually face an architecture decision: should the model run on a provider's servers, or inside infrastructure the organization controls? There is no single correct answer. The place the model runs determines where your data is processed, who keeps the system patched, how cost grows, and what happens when the connection goes down. The table compares the three common set-ups.
| Factor | Cloud AI | Local AI | Hybrid |
|---|---|---|---|
| Where the model runs | On a provider's servers | On hardware or a private cloud tenancy you control | Sensitive steps local, other steps in the cloud |
| Where your data goes | To the provider for processing, under its terms | Stays on systems you control unless you send it elsewhere | Depends on the routing rules you set |
| Who maintains it | The provider | You or your support partner: hardware, model and updates | Both sides |
| Cost shape | Usage or per-seat fees that grow with use | Purchase and running costs that are mostly fixed | A mix of the two |
| Model capability | Access to the largest hosted models | Open-weight models: strong on grounded tasks, may trail the largest hosted models on open-ended work | Chosen task by task |
| Works offline | No | Yes | Local steps only |
| Speed to start | Fastest | Slower: hardware and setup come first | Moderate |
| Often a good fit for | General tasks on non-sensitive data | Confidential documents, predictable workloads, offline use | Mixed workloads with a clear sensitivity line |
What is cloud AI?
Cloud AI uses models hosted by an external AI or cloud provider. It can provide high capability, rapid deployment, scalability and lower infrastructure overhead.
What is local AI?
Local AI runs models within infrastructure controlled by the organization, such as company servers, a private data center, dedicated hardware or a private cloud environment. You will also see it called private AI or self-hosted AI. Our private AI page describes how we set this up.
Why choose local AI?
Organizations may consider local AI for data-location requirements, offline operation, infrastructure control, predictable workloads or specialized internal use cases. Local deployment also creates responsibility for hardware, model management, security, updates and availability. Someone has to own those jobs.
Why choose cloud AI?
Cloud AI may be preferable when the organization prioritizes model capability, speed of implementation, elastic scale and reduced infrastructure management.
How does cost behave in each case?
The shapes differ more than the totals. Cloud AI usually starts cheaply and then grows with the number of people and the volume of work, because most providers bill by use or by seat. Local AI asks for money first, for hardware and setup, and then costs less per question, though power, upkeep and staff time continue. Which is cheaper over a year depends on your volume, the model size you need and who maintains the system. We do not quote a break-even point here, because it would be a guess without your numbers.
What about hybrid AI?
Many businesses do not need to choose only one. Sensitive retrieval may occur inside a private environment while other approved tasks use cloud models. The architecture should reflect actual risk and business requirements, not a preference for one approach.
Is local AI automatically more secure?
No. Greater infrastructure control does not guarantee security. Authentication, network design, permissions, encryption, patching, monitoring and operating procedures remain critical. A poorly maintained local server can be a weaker position than a well-configured managed service, and the reverse can also be true.
What local deployment does give you is the ability to decide where data goes and to inspect what the system does. Whether that becomes better security depends on the work done around it. The NIST AI Risk Management Framework is a voluntary framework from the U.S. National Institute of Standards and Technology for managing AI risk, and it is a useful checklist whichever way you run the model.
How does the choice affect the workflow?
The architecture changes what you have to plan for. With cloud AI, the planning centers on what data you send, what the terms of the provider say about it, and what happens if the service changes. With local AI, it centers on the hardware, the model you run, who keeps it patched and what you do when it fails. Neither list is shorter; they are different.
It is also worth designing the workflow so the model can be replaced later. If the steps around the model, such as reading the email, checking the CRM and queueing a draft for approval, do not depend on one specific model, you can change the model as requirements, costs or capabilities change.
Can you change your mind later?
Yes, if the workflow is built for it. Moving from a hosted model to a local one, or the reverse, is not a copy and paste, because different models respond differently to the same instructions. Plan to re-test your prompts and check the quality of the output on real cases after any switch. Keeping the model behind a clear interface, and keeping a set of test cases, makes that a few days of checking and not a rebuild.
What should you ask before you decide?
- What data does the workflow touch, and who is allowed to see it?
- How much model capability does the task actually need?
- Does the work need to run offline or inside a specific location?
- Who will maintain the hardware, the software and the model over time?
- What happens to the workflow if the system is unavailable?
- What is the budget for infrastructure compared with usage?
Answering these before choosing a model usually narrows the options quickly. The same questions apply when you are weighing a hosted vendor against a self-run model, which we cover in OpenAI API or an open-weight model. They are also where we start on any AI integration project (see our AI integration services).
Whichever way you go, the workflow still needs to be worth automating. What an AI agent is and how it fits a workflow is a good place to ground that. For a worked example of a local design, read how we built a private email assistant that only drafts replies, an internal project.
We can evaluate your workflow, infrastructure and data requirements to recommend cloud AI, private AI, local AI or a hybrid architecture. Start with a short AI workflow automation conversation or request an assessment.