"The cloud" is one of the most overused terms in technology. It sounds abstract and vague. It is not. It is a very specific thing, and understanding it helps you make better decisions about how your business uses AI.
What is cloud computing?
Cloud computing means using someone else's computers over the internet.
That is genuinely all it is. Instead of buying a powerful computer and running software on it yourself, you rent computing power from a company that owns thousands of computers in data centres around the world. You access that computing power over the internet and pay for what you use.
The three main cloud providers
The cloud computing market is dominated by three companies:
Together, these three control about two-thirds of the global cloud infrastructure market, which is valued at over $600 billion annually.
What do you actually get?
When a business "uses the cloud," they are typically using one or more of these services:
How cloud computing connects to AI
AI and cloud computing are deeply intertwined. Here is why:
Training AI models requires cloud-scale infrastructure
Training a large AI model requires thousands of GPUs working together for weeks. No individual business can afford this infrastructure (we are talking hundreds of millions of dollars). Cloud providers like AWS, Azure, and Google Cloud make this possible by pooling massive GPU clusters and renting them to AI companies.
OpenAI, for example, trains its GPT models on Microsoft Azure's infrastructure. Anthropic has partnerships with both Amazon and Google for compute capacity.
Running AI models at scale requires cloud distribution
When millions of people use ChatGPT simultaneously, the requests are distributed across thousands of servers in data centres worldwide. This is cloud computing in action — dynamically allocating computing resources to meet real-time demand.
APIs make AI accessible
The reason a small business can use GPT-4o or Claude without owning a single GPU is cloud computing. You send a request to an API (Application Programming Interface), it gets processed on the provider's cloud infrastructure, and the result comes back. You pay per request. The complexity of the underlying infrastructure is entirely hidden from you.
Cloud vs local: the trade-offs
For businesses deploying AI agents, there is a genuine choice between running things in the cloud and running them locally. Both have advantages.
Cloud advantages
Cloud disadvantages
Local advantages
Local disadvantages
The hybrid approach
Most businesses end up with a combination. The AI agent runs on local hardware for tasks that involve sensitive data or need to work without internet, and connects to cloud AI APIs for tasks that benefit from the most powerful models. This gives you the best of both worlds: privacy and control where it matters, power and scale where you need it.
What does this mean for business owners?
Understanding cloud computing demystifies a lot of the AI conversation:
When someone says "AI costs are falling," they mean cloud providers are making their infrastructure more efficient, which reduces the cost per API call. This is driven by better GPUs, better software, and economies of scale.
When someone says "your data goes to the cloud," they mean your business information is being sent to servers owned by Amazon, Microsoft, or Google for processing. Whether that is acceptable depends on your industry, your data, and your regulatory obligations.
When someone says "run AI locally," they mean processing happens on a computer in your office. The trade-off is less power but more privacy and control.
There is no universally right answer. The best setup depends on your specific business, your data sensitivity, and your budget. What matters is that you understand the options well enough to make an informed choice — rather than defaulting to whatever a vendor recommends.
If you want help figuring out the right setup for your business, that is exactly what our consultation covers.