AI does not have to live in one place. It can run on somebody else's infrastructure, on hardware you control, or across a combination of both.
Cloud AI uses models running on infrastructure operated by another provider.
Usually no specialist hardware is required inside the business.
Cloud providers can offer very capable models without you running the infrastructure.
Information required for the task may leave your environment for processing.
Local AI means the model runs on hardware controlled by you or your organisation.
Information can potentially remain inside the environment you operate.
You need enough computing resources to run the model.
Updates, security, monitoring and infrastructure become your responsibility.
Hybrid architecture uses local and cloud systems together instead of treating the decision as all-or-nothing.
Some information can remain within your environment.
High-capability cloud models can still be used for appropriate tasks.
Only the information required for a particular job needs to travel.
Sometimes the answer is AI. Sometimes it is automation. Sometimes two existing systems simply need to communicate properly. And whenever business information is involved, privacy and data ownership should be part of the decision.
It can provide greater control because processing can remain inside an environment you control, but the complete system still needs to be designed securely.
Sometimes. For occasional use, cloud AI can avoid the cost and maintenance of dedicated hardware.
Yes. Hybrid approaches can be very practical.
Start with the task, sensitivity of the information, volume of use, budget and level of control required.
Tell us what is repetitive, slow or frustrating. The first job is understanding the problem. AI, automation and infrastructure come afterwards.
Tell us what's wasting your time