Challenges of consuming AI from a third party
Why cloud-only AI breaks down at scale, and the four questions that separate a safe buy from a risky one.
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What AI needs to run
AI needs three things: data, power, and compute. When you use cloud AI (ChatGPT, Claude, Gemini, or any API), you send your data to someone else's servers. They run the models. You pay for the privilege.
That's the trade in a nutshell. Convenience and scale on one side; your data, your privacy, and your money on the other. Understanding that is the first step to making informed decisions.
The cost surprise
Cloud AI is pay-per-use. You're charged for every request. The more you use it, the more it costs. There's no ceiling until you see the bill.
We've seen it repeatedly: teams adopt AI, usage grows, costs double in a quarter, nobody predicted it. That's fine, if the ROI was calculated beforehand. Often it wasn't.
The only way to avoid the surprise is to estimate before you commit. Know your volume. Know your use case. Run the numbers. Assign expected ROI.
Where does your data go?
When you send a prompt to a cloud AI, your data leaves your control. Intellectual property. Customer details. Confidential strategy. Where does it go? Who can see it? What happens if you leave?
These are valid concerns. GDPR, sovereignty, cyber security: they all matter. Some businesses can't send certain data off-site. Others are fine with it. There's no one answer.
The point is: ask the question. Know your options. Keep it safe with you, send it to the cloud, or both. We help you decide, and weigh up the risks.
The switching problem
APIs change. Pricing changes. Once your workflows are built around a provider, switching is painful. You're not just buying a tool; you're tying your processes to a vendor.
That's not always bad. But it's something to be aware of. The more you embed AI into your day-to-day, the harder it gets to walk away. Plan for that. Know what you're committing to.
The trade-off
Cloud gives you convenience and scale. Self-hosted gives you control and predictability. Hybrid exists. There's no one-size-fits-all answer.
The right choice depends on your use case, your data, your budget, and your risk tolerance. We don't push one path. We help you choose. And we only say yes if we can prove the numbers work.