Is the Public Cloud Always the Best Place for AI? Let’s Take a Closer Look
Hey everyone, John here! It feels like you can’t go a day without hearing about a new breakthrough in Artificial Intelligence (AI). Big tech companies like Microsoft, Amazon (with AWS), and Google are pouring unbelievable amounts of money—we’re talking tens, even hundreds of billions of dollars—into building powerful AI tools on their cloud platforms. They promise that using their AI services is the key to the future for any business.
But is it really that simple? I’ve been looking into this, and it seems there’s a growing gap between what these giant cloud companies are selling and what businesses actually need. Let’s break it down together.
The Big Disconnect: Tech Features vs. Business Value
Imagine you walk into a giant hardware store. The salesperson rushes over and starts telling you all about their newest, most powerful drill. They talk about its motor speed, its battery life, and the 50 different drill bits it comes with. It all sounds very impressive, but you’re left standing there thinking, “Okay, but I just need to hang a picture frame. Is this the right tool for me? Is it worth the high price?”
This is a lot like what’s happening with AI in the public cloud. The big cloud providers host huge events and announce exciting new technologies and partnerships. They show off all the amazing things their AI can do, but they often forget to answer the most important question for a business owner: “How will this actually help my company?”
Lila: “John, the article mentions they launch new AI models and APIs. That sounds very technical. What’s an API, in simple terms?”
John: “That’s a fantastic question, Lila! It’s a term you hear all the time. Think of an API (which stands for Application Programming Interface) like a menu at a restaurant. You, the customer, don’t need to know how to cook the food or what’s happening in the kitchen. You just look at the menu, pick what you want (like ‘I want a burger’), and the waiter (the API) takes your order to the kitchen and brings back the burger. In the tech world, an API lets one computer program ask another program for a piece of information or to perform a task, without needing to know all the complex code behind it.”
The problem is, cloud providers are basically handing businesses a huge, complicated menu of AI services and saying, “Figure out what you need.” Companies are left to do the hard work of connecting these technical features to real-world business goals, like improving customer service or cutting costs. The sales pitch is often just “You need AI because it’s the future,” instead of “Here’s a specific way our AI can help you solve your unique problem and make you more successful.”
The Elephant in the Room: The Staggering Cost of AI
Here’s the secret that the mega cloud providers don’t like to talk about: running AI workloads in the public cloud can be incredibly, and sometimes uncontrollably, expensive.
Many businesses start experimenting with AI, and the initial costs seem manageable. But when they try to use it for their everyday operations—what we call moving into “production”—the monthly bills can be a real shock. Training powerful AI models and running them on huge amounts of data requires a ton of computing power, and that power comes with a hefty price tag.
The pricing can also be unpredictable. It’s like having an electricity meter that spins faster and faster, but you’re not quite sure why, and you only find out how much you owe when the eye-popping bill arrives at the end of the month. Many companies are finding that their amazing AI ambitions are quickly hitting their budget limits.
Lila: “That sounds stressful! The article says that because of the cost, some companies are ‘revisiting on-premises deployments.’ What does that mean?”
John: “Great point, Lila! ‘On-premises’ is the opposite of the cloud. Instead of renting computing power from a big company like Amazon or Microsoft, you own and run the computers and servers yourself, right there in your own office or data center. It can be a big upfront investment, but for some AI tasks, it can actually be much cheaper in the long run than paying a monthly cloud bill that keeps going up.”
Because of these high costs, businesses are starting to look at other options. They might move their AI work “on-premises” or turn to smaller, more specialized cloud companies that can offer better prices and more personalized deals. This is a big change from a few years ago when everyone assumed the giant public clouds were always the cheapest and best option.
A Smarter Strategy: Focus on ‘Best Value,’ Not Just ‘Cloud-Only’
So, if relying only on the big public cloud providers for AI isn’t always the answer, what should businesses do? The key is to shift from a “cloud-only” mindset to a “best-value” strategy. This means focusing on what solves the business problem best, not just on where the technology lives. Here’s a simple guide based on the article’s advice:
- Let the Problem Drive the Tech: Don’t start by looking at a catalog of AI tools. Start by identifying a real business problem or goal. Do you want to reduce customer wait times? Or maybe analyze sales data more effectively? Once you know the goal, you can find the right technology to achieve it.
- Look for Real Value, Not Just Cool Features: It’s easy to get wowed by fancy features. But the real question is whether a tool will provide a return on investment (ROI). Will it save you money, make your team more productive, or improve your customers’ experience?
- Be Realistic About Costs: Don’t just trust the online cost calculators. If possible, run small test projects (called pilots) to see what the real-world costs will be. Keep an eye out for hidden fees, like charges for moving your data out of the cloud (known as egress fees).
- Demand a True Partnership: Your tech vendor should be more than just a seller; they should be a partner. If they can’t explain clearly how their product will help you reach your specific goals, they might not be the right fit.
- Stay Flexible: Don’t lock yourself into one single provider. The best solution often involves mixing and matching.
Lila: “John, that last point mentioned being flexible with a hybrid or multicloud approach. What’s the difference?”
John: “That’s a very sharp question, Lila, as people often use them interchangeably! Think of it this way: a hybrid approach is when you use a mix of your own ‘on-premises’ servers and a public cloud service. It’s like having your own car but also using a ride-sharing service when you need it. A multicloud approach is when you use services from several different public cloud providers—maybe you use Google for one task and Amazon for another. In both cases, the goal is to pick the perfect tool for each specific job, giving you the most flexibility and value.”
A Few Final Thoughts
John’s Take: To me, this all comes down to common sense. You wouldn’t use a sledgehammer to hang a small photo. It seems the big cloud companies have built these incredible AI sledgehammers but are trying to sell them for every possible task. It’s a good reminder for businesses to step back from the hype and ask a simple question: “What is the right tool for my job?”
Lila’s Take: As someone still learning about all this, it’s actually a relief to hear this! The world of AI can feel intimidating, like you have to go with the biggest, most famous name. Knowing that the best strategy is actually to ask practical questions about value and cost makes it feel much more approachable.
Ultimately, the era of assuming the public cloud is the one-and-only answer for AI is ending. The companies that succeed will be the ones that cut through the noise and make smart choices based on their own unique business needs.
This article is based on the following original source, summarized from the author’s perspective:
What public cloud gets wrong with AI